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PART I: THE DEVELOPMENT OF A PHOTODIODE ARRAY STOPPED-FLOW
SYSTEM AND ITS APPLICATION TO CLINICAL ANALYSIS

PART II: THE DEVELOPMENT OF A NEW CHEBHIIETRIC APPROACH FOR
C(NPUTERIZED COMPOUND IDENTIFICATION USING IR SPECTRAL DATA

presented by

Amidollah Salari

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Ph . D . degree in Chemi Stry

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cWMS-DJ

Part1:

THE DEVELOPMENT OF A PHOTODIODE ARRAY SI‘OPPED-FLOW
SYSTEM AND ITS APPLICATION TO CLINICAL ANALYSIS

Part1]:

THE DEVELOPMENT OF A NEW CHEMONIETRIC APPROACH
FOR CONIPUTERIZED COMPOUND IDENTIFICATION
USING IR SPECTRAL DATA

By

AmidSalari

A DISSERTATION

Submitted to
Michigan State University
In partial fulﬁllment of the requirements
For the degree of

DOCTOR OF PHILOSOPHY

Department of Chemistry

1990

’— 0.9570

_ ’If‘... .-
62 J

Part I

ABSTRACT

THE DEVELOPMENT OF A PHOTODIODE ARRAY STOPPED FLOW
SYSTEM AND ITS APPLICATION TO CLINICAL ANALYSIS

By

Amid Salari

A rapid scanning photodiode array stopped-ﬂow spectrometer has been
developed. The diode array allows for rapid simultaneous detection of all
wavelengths over a given spectral region. A Reticon “S” series type R1-512s
diode array which was speciﬁcally designed for spectroscopic applications
has been used. This device consists of 512 sensor elements in a length of
12.8 mm with a 25 um diode spacing which corresponds to 40 diodes/mm.
Each diode has a height of 2.5 mm providing a slit-like geometry to each
element with an aspect ratio of 100 : 1.

A polychromator equipped with a holographic grating has been employed to
produce a ﬂat field image across the detector elements with minimum
stray light. The speed of data acquisition with the diode array makes it a
very suitable detector for dynamic systems such as stopped-ﬂow kinetic
measurements. The stopped-ﬂow apparatus used in conjunction with the
diode array allows for rapid mixing of reagents in times as short as a few
ms. The stopped-ﬂow mixing time and dead time have been determined to
be on the order of 2.048 and 7.9 ms, respectively.

A control interface has been built to allow for variable integration and delay
time, and to provide proper signals for running the stopped ﬂow system and
synchronization of the data acquisition with. The system developed has

many desirable features for rapid kinetic measurements, particularly
where spectral information is required. The system provides
multiwavelength absorbance information for rapid changes in absorbance.
The type of data generated by the diode array stopped-ﬂow system allows for
a signiﬁcant degree of ﬂexibility and convenience for performing complex
analyses.

The photodiode array stopped-ﬂow instrument has been applied to both
single and multicomponent clinical analyses using a ﬁxed-time, reaction
rate approach. The kinetic analysis of total serum protein has been carried
out as an example of an analysis of a. single component in a sample, while
the simultaneous kinetic determinations of glucose and ethanol in blood
serum has been performed to demonstrate the system capabilities for
multicomponent analysis.

The accuracy and precision obtained for protein determination are in good
agreement with the results reported by other established methods, and
those obtained for the simultaneous analyses of glucose and ethanol in
blood serum are also well within the acceptable range. These results
indicate that the diode array stopped-ﬂow spectrometer in a suitable
instrument for simple as well as complex fast clinical analyses.

Part II

ABSTRACT

THE DEVELOPMENT OF A NEW CHEMONIETRIC APPROACH
FOR CONIPUTERIZED CONIPOUND IDENTIFICATION
USING IR SPECTRAL DATA

By

Amid Salari

A new chemometric procedure has been developed for automated infrared
spectroscopic compound identiﬁcation. Availability of data bases plus more
powerful laboratory computers makes computerized spectroscopic
compound identiﬁcation an attractive possibility. However, direct digital
comparison of reference and sample spectra has some serious
disadvantages. For instance, most conventional library search routines are
designed to operate only with pure samples. The increasing size of spectral
reference libraries, however, necessitates that we adopt new approaches to
accomplish efﬁcient searching.

The proposed method allows for direct spectroscopic analysis of pure
samples as well as mixtures. A multidimensional vector approach is used
which allows the application of Gram-Schmidt’s orthonormalization
procedure to form an orthonormalized vector space from a library of
reference spectra. Computing the projection of an unknown spectrum into
the space spanned by the orthonormal library yields qualitative and
quantitative information.

The use of an orthonormal reference library results in a computational
efﬁciency that could not otherwise be realized. In fact, after completion of
the one time orthonormalization procedure, the qualitative and quantitative
analysis of a mixture can be done in about the same amount of computer
time as is required to identify a pure compound from its stored spectrum.
The required computer time has been evaluated on a 108-member library of
primary mass spectra acquired on a triple quadruple mass spectrometer
using VAX-station 3200 computer system. The amount of time required for
a typical analysis based on this library has been determined to be about 7.7
seconds.

The performance of the new procedure has been compared to that of the two
most popular library search routines, namely, the methods of absolute
differences and dot product matrices. compared to these two matrices,the
orthonormal procedure has shown equal performance for pure samples
and superior performance for mixtures. Also, the method has been found
to be faster than the conventional approaches to mixture analysis as well as
to provide a more efficient screening analysis of a speciﬁc target compound.
Additionally, the orthonormal algorithm can be directly applied to any kind
of spectroscopy; similar performance was obtained for both IR and mass
spectral data.

Finally, the orthonormal procedure has also been applied to the resolution
of fused chromatographic peaks. In this regard, the orthonormal method
has been capable of successful qualitative and quantitative resolution of
severely overlapping peaks.

To:

my dear wife Nayereh
my three lovely children Kataneh, Keyan and Autoosa
and the memory of my father

vi.

ACKNOWLEDGMENTS

I would like to extend my most sincere appreciation to my research advisor
Professor Chris Enke, for his guidance, support and encouragements
throughout the course of my research. I am very grateful to Chris for
leading me into the fascinating ﬁeld of analytical chemistry, showing me
the way to independent thinking and research, and for the patience he
offered me to overcome many lobstacles in my life. His insight, vision and
stimulating discussions were truly invaluable in helping me shape my
scientiﬁc skills and standards. I do take a great deal of pride to have been
associated with a man of such superb scientiﬁc stature and such great level

of human decency.

Many thanks go to Professor Stan Crouch for serving as second reader for
this thesis and for his valuable comments and discussions during many of
our joint spectroscopy meetings. I would like to thank the members of my
guidance committee for their valuable time and comments. I wish to
express gratitude to the faculty and staff of the MSU chemistry department
for the tremendous support they offered. I am very thankful for the
ﬁnancial support received from the MSU chemistry department and for the
scholarship received from the ministry of higher education of Iran. I wish
to thank all the members of Enke’s research group whom I have learned a
lot from and have become like a large family to me. I will always treasure
their friendship. The generous help and support offered by Dr. Tom
Atkinson is greatly appreciated.

I wish to acknowledge the collaborative effort offered by my former
colleague and friend Dr. Marc Nyden at the University of Michigan-Flint

for the work presented in the second part of this thesis.

I am especially grateful to my parents for their unconditional love and
support. In particular my father, a lifetime educator who always placed a
high priority on higher education for his children, and we all answered his
dream.

Most of all, I wish to thank someone very special who is an equal partner in
this journey, my dearest friend and lovely wife Nayereh. Her constant love,
understanding and support made it all possible. I thank her for the
greatest faith she had in me and feel lucky to be with her. Finally I would
like to thank my three lovely children Kataneh, Keyan and Autoosa for their
most honest love and beautiful laughs which gave Daddy 3 lot of strength
and motivation, and made the whole thing a lot easier. Wait a minute! Did

I thank myself at all?

viii

TABLE OF CONTENT

LIST 0 FIGURES ...................................................................... xii
LIST OF TABLES ......................................................................... xiv
PART I
CHAPTER 1 .................................................................................... 1
INTRODUCTION ............................................................................ 1
CHAPTERZ. ................................................................................... 4
HISTORICAL OVERVIEW OF IMAGING DEVICES IN
ANALYTICAL RAPID SCANNING SPECTROSCOPY ....................... 4
Introduction .................................................................... 4
Conventional (Mechanical) RSS ....................................... 10
Dispersion Techniques .................................................... 12
Scanned Spectrum Methods, Mechanical Design ............... 13
Limitation of Conventional (Mechanical) RSS .................... 21
OPTOELECTRONIC IMAGE DEVICES (OIDS) ................................. 23
Introduction .................................................................. 23
Common Characteristics of OIDs ..................................... 25
Classiﬁcation of OIDs ..................................................... 30
TV -Type Camera Tubes, Silicon Vidicon (SV) and Silicon
Intensiﬁed Vidicon (SIT) Tubes ....................................... 32
Image Dissector ............................................................. 42
SOLID STATE IMAGING DEVICES ................................................ 46
Introduction .................................................................. 46
Charge Transfer Devices ................................................. 47
Charge Injection Device .................................................. 51
Charge Coupled Device ................................................... 56
Device Comparison ......................................................... 61
CHAPTER3 .................................................................................. 65
DESCRIPTION OF A RAPID SCANNING LINEAR DIODE ARRAY
SPECTROPHOTOMETER FOR STOPPED-FLOW KINETICS .............. 65
Introduction .................................................................. 65

ix

History of the LDA as an RSS-stopped-ﬂow detector ............ 65

Functional description of the instrument ......................... 66
Linear diode array .......................................................... 69
Stopped-ﬂow mixing system ............................................ 75
Control interface ............................................................ 80
DETERMINATION OF SYSTEM DEAD TIME .................................. 82
DETERMINATION OF MIXING TIME ............................................ 83
CHAPTER4 .................................................................................. 84

APPLICATION OF THE DIODE ARRAY STOPPED-FLOW SYSTEM
TO THE KINETIC ANALYSES OF BOTH SINGLE AND

MULTICOMPONENT SAMPLES ..................................................... 84
Introduction .................................................................. 84
Determination of total serum protein by buiret method ........ 89
Background ................................................................... 89
Kinetic determination of total protein in blood serum
by linear diode array stopped-ﬂow system .......................... 93

SIMULTANEOUS KINETIC DETERMINATION OF GLUCOSE AND

ETHANOL IN BLOOD SERUM ........................................................ 101
Introduction ................................................................. 101
Simultaneous kinetic determination of glucose
and ethanol in blood serum ............................................ 105

REFERENCES .............................................................................. 12)

PART II
INTRODUCTION ............................................................................ 1

AUTOMATED SPECTRAL PROCESSING
A CONCEPTUAL OVERVIEW ......................................................... 5

LIBRARY SEARCH BY INFRARED SPECTROSCOPY
A BRIEF HISTORY ........................................................................ 16

THE DEVELOPMENT OF A CHEMOMETRIC APPROACH
FOR COMPUTERIZED IR ANALYSIS OF PURE AND

MIXTURE SAMPLES ..................................................................... 22
Introduction .................................................................. 22
Mathematical background ............................................. 25
Construction of orthonormal basis,
the Gram-Schmidt process ............................................. 31

APPLICATION TO THE QUALITATIVE AND QUANTITATIVE
ANALYSIS OF PURE AND MIXTURES USING

IR REFERENCE SPECTRA ............................................................. 38
Experimental Results ..................................................... 46
Identiﬁcation of pure compounds ..................................... 47
Analysis of mixtures ...................................................... 53

COMPARISON OF THE ORTHONORMAL METHOD TO OTHER

LIBRARY SEARCH MATRICES ..................................................... 68

Application of the Orthonormal Method to the Resolution of severely

overlapping

CHROMATOGRAPHIC PEAKS ....................................................... 75
Peak purity determination ............................................... 78
Resolution of Overlapping peaks ...................................... 81

CONCLUSION AND FUTURE PROSPECTS ..................................... 87

REFERENCES ............................................................................... 92

xi

Figure

2.1
2.2
2.3
2.4

2.5

2.6
2.7
2.8
2.9

2.10

2.11

2.12
2.13

2.14
3.1

3.2

LIST OF FIGURES

page
Part I
Family tree of analytical rapid scanning spectroscopy .......... 11
Two basic approaches to dispersion scanning spectrometers .14
Expansion of family tree of dispersion techniques ................. 15
Rapid scan methods in dispersion spectrometers,
S is the eidt slit, M1 is the rotating or oscillating mirror,

L is the rotating or oscillating littrow mirror or grating ........ 17

Optical schematic of the corner mirror rapid scanning

spectrometer .................................................................... 2O
Transducing light into electrical charge ............................. 26
Family tree of optoelectronic image devices .......................... 31
Vidicon tube geometry and beam scan pattern ..................... 33

The silicon Vidicon target and signal generating
mechanism ..................................................................... 35

Image dissector photomultiplier. (1) sweep-coil electronics,
(2) photomultiplier power supply. (3) focus-coil electronics,

(4) display, (5) signal ampliﬁcation ..................................... 43
A single CTD element ....................................................... 48
A hypothetical CID device .................................................. 53
The destructive and nondestructive readout schemes

in the CID ........................................................................ 54
Diagram of a hypothetical three-phase CCD ........................ 58
Block diagram of the rapid scanning photodiode array
stopped-ﬂow spectrometer ................................................. 68
Pin conﬁguration of the LDA integrated circuit .................... 70

xii

3.3
3.4
3.5
3.6
3.7
4.1
4.2
4.3
4.4
4.5

5.1

5.2

Sensor geometry and the response function of the aperture ...72

LDA electrical equivalent circuit ........................................ 73
Block diagram of the stopped-ﬂow mixing system ................ 76
Stopped-ﬂow mixer and observation cell .............................. 79
Control interface .............................................................. 81
The biuret reaction ........................................................... 9O
Calibration curve for total serum protein ............................. 96
Enzymatic reactions of glucose and ethanol ......................... 109
Calibration curve. for glucose ............................................ 114
Calibration curve for ethanol ............................................ 11 6
Part II

The geometric representation of the Gram-Schmidt
orthonormalization process ............................................... 36

Representation of Gram-Schmidt transformation for
ten hypothetical compounds ............................................... 44

xiii

Tables

41
.42

43

4A

5.1
5.2
5.3
5.4
5.5
5.6
5.7
5.8
5.9
5.10
5.11
5.12
5.13
5.14

LIST OF TABLES

Page
Part I
Results for standard solutions of serum protein (At = 8 s) ...... 97
Results of determination of total protein in
real human serum ........................................................... 99
Results for standard solutions of glucose (At = 8 s) ............... 115
Results for standard solutions of ethanol (At = 8 s) ............... 117
Part II

General algorithm for spectra interpretation ........................ 9
Analysis of pure samples .................................................. 48
Analysis of pure samples not present in the library .............. 51
List of compounds in the library ........................................ 56
Results of the analysis of MIXTURE 1 ................................. 58
Results of the analysis of MIXTURE 2 ................................. 59
Results of the analysis of MIXTURE 3 ................................. 61
Results of the analysis of MIXTURE 4 ................................. 63
Results of the analysis of MIXTURE 4 ................................. 64
Results of the analysis of MIXTURE 5 ................................. 65
Results of the analysis of MIXTURE 5 ................................. 66
Analysis using orthonormal method .................................. 71
Analysis using the dot product ........................................... 72
Analysis using the absOlute differences ............................... 73

xiv

5.15 Peak purity determination ................................................. 80
5.16 Resolution of overlapping peaks ......................................... 83

5.17 Results of imposing non-negativity constraints; obs. (actual) .85

PART I

THE DEVELOPMENT OF A PHOTODIODE ARRAY
STOPPED-FLOW SYSTEM AND ITS APPLICATION
To CLINICAL ANALYSIS

CHAPTER]

Introduction

Generally in all spectroscopic systems, spectrometric information is
obtained by the measurement of some characteristic of electromagnetic
radiation versus either wavelength or frequency. Devices have been
developed to accomplish wavelength separation for all regions ofwthe

electromagnetic spectrum.

Analytical spectroscopists, however, had a particular interest in the
measurement of light in the UV-VIS region. For a long time, a variety of
combinations of ﬁlters has been used to cut the light down to narrow bands
of wavelength. The very poor resolution obtained by this method has been
an obstacle in the way of development of higher quality systems. The
introduction of dispersive elements such as the prism and grating has been
a great step forward in overcoming such problems. Nowadays almost all
spectrometers in use are equipped with a prism or grating, usually the
latter. The typical dispersive system employed in these instruments, the
monochromator, decodes the incident frequency information into an optical
spatial array on the focal plane of the instrument. A photomultiplier tube
in conjunction with an exit assembly is then used to monitor the light
intensity in a given wavelength resolution element. Inherent to these
conventional kinds of spectrometers, there exists a contradiction. The

contradiction is precisely between the static nature of the measurement

system (one wavelength at a time) and the potentially dynamic nature of the
matter under study. A mixture undergoing a chemical reaction,for
example, could undergo substantial change in composition before or while
it is being monitored. These changes could be manifest as wavelength
shifts in the peak absorbance maxima, abrupt changes in the absorbances
of other wavelengths, transient peaks, many of which, when a narrow

wavelength range is monitored, are never detected.

It is to remove exactly these limitations that the ﬁeld of analytical rapid
scanning spectroscopy was developed. In this approach the spectrometric
information can be obtained by rapidly scanning across the spectral region
of interest. therefore, those phenomena occurring within the scan time
frame can be studied. however, rapid scanning should not be percieved as
simultaneous multiwavelength detection because different wavelengths are
monitored at different times. Traditionally, the detection system for the
simultaneous monitoring of the entire spectrum has been the photographic
emulsion plate. The photographic plates, however, have poor sensitivity,
marginal accuracy and precision, tedious and time consuming calibration

and data retrieval procedures.

In recent years optoelectronic image devices have become the detector of
choice for accomplishing such tasks. These devices actually combine the
multiwavelength capability of photographic plate with a real time electronic
readout and the wide dynamic range and sensitivity of photomultiplier
tubes. Substantial research has been devoted to exploring the capabilities of
such devices as parallel detectors in analytical rapid scanning

spectroscopy. Part I of this dissertation describes the development of a

rapid scan linear diode array spectrometer in conjunction with stopped-
flow mixing system and its application to clinical analysis.

Chapter 2 offers a historical review of the variety of approaches utilized in
analytical rapid scan spectroscopy. The most popular and widely used
techniques, from conventional scanning systems up to the recent
optoelectronic image devices are explained in fairly detailed fashion. the
advantages and disadvantages associated with each system are described.
Chapter 3 presents a thorough discussion of the linear diode array as well
as the stopped-ﬂow mixing system. A description of the electronic interface

which controls the overall operation of the system is also illustrated here.

Chapter 4 describes the application of the linear diode array stopped-ﬂow
spectrometer to the area of clinical analysis. Speciﬁcally, the kinetic
determination of the serum total protein by Biuret method is studied as an
example of a single component system. The multicomponent capability of
the system is demonstrated by the simultaneous kinetic determination of
glucose and alcohol in blood serum using the alcohol dehydrogenase and

glucose oxidase enzymes.

CHAPTER2

HISTORICAL OVERVIEW OF IMAGING DEVICES
IN ANALYTICAL RAPID SCANNING SPECTROSCOPY

Introduction

Rapid scanning spectroscopy (RSS) is a technique in which a selected
region of the electromagnetic spectrum is scanned on a time scale ranging
from several seconds down to a few microseconds. Scanning is
accomplished by mechanical or electronic means. The technique has been
developed primarily for the study of chemical reactions or processes which
involve short-lived intermediates with spectral properties which differ
signiﬁcantly from the reactants or products. Repetitive scans of the
appropriate spectral region can provide information on the number and
types of species present as well as the kinetics of the formation and decay of
intermediates. From these data, one can drive explicit mechanistic
information which could only be inferred from measurements made at a

single wavelength.

The application of rapid-scan spectrometry includes both absorption and
emission studies. In most cases the time resolution aspect is the important
one, but the simple convenience of being able to see a large spectral region,
essentially continuously, has some advantages even in systems that do not
evolve in time or which change only slowly. The RSS techniques have a S/N
ratio disadvantage relative to a single wavelength normal spectrometer.
This arises from the fact that in a normal spectrometer the wavelength of

interest is continuously monitored, whereas in RSS techniques each

wavelength increment rapidly passes by an entrance slit. Thus the detector
spends only a small fraction of the time monitoring the photons of any given
wavelength increment which reduces the S/N ratio by JE- where n in the
number of wavelength increment monitored. The scamiing speed and S/N
ratio thus actually work against each other. It should be cleariﬁed at this
point that the term RSS is used in a general sense here to also include
optoelectronic image devices. The integrating optoelectronic image devices,
however, do not suffer from the S/N ratio disadvantage mentioned above

and the discussion only applies to mechanically scanned spectrometers.

A most interesting application of RSS is in molecular luminescence (1).
When molecules are excited with light, they may relax to the ground state
by emitting radiation in the form of fluorescence (short-lived),
phosphorescence (long-lived) or both. The study of molecular luminescence
has value in investigating molecular structure and chemical properties of
molecules as well as in quantitative analysis. The RSS methods offer

advantages to both of these ﬁelds.

The molecular electronic spectroscopist considers the the ﬂuorescence and
phosphorescence of molecules and chemical systems as probes into the
structure of the molecules and the chemical interaction between molecules.
The photochemist uses molecular luminescence to follow the paths for
relaxation of electronically excited molecules in order to gain knowledge
about photochemical reactions. The properties of molecular luminescence
of greatest interest to the spectroscopist and photochemist are: emission
and spectral location, shape, vibrational structure, intensity, polarization,

and life time of decay. Rapid scanning spectroscopy can give all of this

information very quickly, and in the case of life times has an advantage
over other methods. Some RSS techniques have calibrated scan times as
fast as a few msec, and therefore, can be used to follow phosphorescent
decays for many wavelength elements simultaneously. If one is doing a
phosphorescence experiment with a mixture of phosphorescencing
molecules, RSS can time-resolve spectra of individual molecules by taking
advantage of their differing lifetimes. A molecule with a long
phosphorescence decay lifetime (exponential decay) usually has a
comparably long rise time during excitation. Thus, by adjusting the
duration of excitation and the time interval between the end of excitation
and observation, one can enhance the observation of either the long- or the
short-lived molecular phosphorescence. These types of studies require
rapid data acquisition speed which has only become possible after the

introduction of RSS techniques.

The RSS methods can also be used to monitor molecular luminescence
while changing temperature or excitation wavelength, or during a
chemical perturbation. For example, one can obtain total luminescence
information by simultaneously scanning both excitation and emission
monochomators or, with a ﬁxed wavelength interval (Al) between them
(sychronous excitation ﬂourometry). Also, time dependent changes as a
result of any perturbation can be monitored by means of rapid and repetitive

spectral acquisition.

The RSS techniques are most useful when observing spectra that evolve in
time. An excellent application is stopped-flow kinetics which will be

discussed in greater detail in the next chapter. The information gained

about a reaction while observing the entire spectrum (or a substantial
portion of it) is powerful indeed. Chemical kinetics, when followed by
spectrometry, is subject to several pitfalls. The usual approach is to
monitor the radiant power transmitted by reaction mixture at a ﬁxed
wavelength as a function of time. Such a measurement, while quite
precise, often will give erroneous information if spectral peaks shift, or if
there are transient absorptions at the monitoring wavelength during the
reaction. If one could observe the individual variations in each element of a
rather large spectral region during the reaction, all of the kinetic data
could be extracted while observing any spectral shifts or intermediate

absorptions that could interfere with the measurement.

The RSS techniques allow one to observe a large spectral range in one scan
(of a few msec duration). This makes them ideally suited for the stopped-
ﬂow kinetics scheme. Another application of RSS is in combination with
liquid chromatography where the ability to identify the individual
components in overlapping peaks is most helpful (2-3). Other typical
examples which lend themselves to this type of study include enzyme-
substrate complexes (3), mixed complexes in ligand exchange reactions (4),

and products of electrochemical (5) and ﬂash photolysis experiments.

As one can see the activity in the ﬁeld of RSS is extensive and has already
been fruitful. The spectral regions under consideration extend from the
UV to the far IR, and the scan time sought causes some workers to begin
speaking in units of nanoseconds. A variety of interesting problems are

now under study that were quite out of reach only a few years ago.

The preceding examples give some indication of the kinds of interesting
spectroscopy possible with RSS. There are, of course, many more
specialized uses, such as: ﬂame emission, are emission, plasma emission,
atomic absorption and ﬂuorescence and others. Actually one of the most
striking features of the literature on this topic is that it involves a series of
specialized instrument systems applied to equally specialized problems, but
they all share one important disadvantage "being mechanically scanned"

and therefore, limited to its associated problems and pitfalls.

The evolution of "photoelectronic imaging devices", however, has opened a
whole new era to the ﬁeld of rapid scanning spectroscopy. Since the
introduction of imaging devices, there has been a slowly growing attraction
toward array detection. Within the past decade there has been an almost
complete shift toward the application of imaging devices rather than rapid
mechanical scanners for solving complex analytical problems. With the
advancement of television technology and the development of commercially
available image devices, the practical optical multichannel detector has
become a reality. These devices, often referred to as optoelectronic image
devices (OID), are typically constructed on a single monolithic silicon
crystal wafer, using well established LSI manufacturing techniques.
Linear detectors (25 nm long) with up to 2000 discrete light sensors and two
dimensional image devices with a few hundred thousands sensors are now

commercially available.

State-of-the-art optical parallel detectors conﬁrm very well to mdst criteria
of "ideal" detectors. They are reliably manufactured at a reasonable cost.

They can be made immune to sudden "light-shocks". They are

mechanically rugged, compact and easily cooled, and consume little
electrical power. They can be constructed with exceptionally high
geometric accuracy (wavelength-to-sensor position registration across the
focal plane of the spectrometer). Some of these detectors provide high
ﬂexibility in addressing the sensor elements, including individual and
random access reading. The silicon type detectors, particularly self-
scanned photodiode arrays, have a remarkably high sensitivity with
relatively uniform characteristics across a very wide spectral range (190-
1100 nm). Most signiﬁcantly, their dynamic range (real-time) can be as
high as 105:1 with highly linear transfer characteristic (gamma of 1).

The present technology of optoelectronic imaging devices favored by the
advancement of minicomputers and microprocessors allows that, for
almost all problems, the manual and/or conventional mechanical designs
can be replaced by versatile electronically controlled systems. The time and
effort required to obtain one or several scans of a spectrum covering several
hundred nanometers should be little more than that now required to obtain

measurements at one wavelength by use of conventional instrumentation.

The purpose of this chapter is to review the course of development of RSS

according to the following two rather wide categories:

(1) Conventional (mechanical) RSS; and

(2) Optoelectronic image devices RSS.

This chapter will not cover the detailed design of the several RSS's designed
in the ﬁrst category, but rather will develop a perspective of the different

general approaches which have been developed for RSS. More emphasis is
placed on the second category. Of course, those particular image devices
that have found considerable application in analytical chemistry will be

discussed in detail and the discussion about the rest will be brief.

Conventional (Mechanical) RSS

In terms of mechanism of scanning, rapid scanning spectroscopic
techniques can be classiﬁed into two rather large groups: Those which
employ mechanical means of scanning, also referred to as conventional
RSS, and those which utilize electronically scanned array detectors, also
called optoelectronic image devices (011)). This section will only deal with
mechanical systems; optoelectronic image devices will be the subject of the

next section.

Regardless of the scanning mechanism, most scanning spectrometers can
be grouped under one or two major classiﬁcations which may be labeled as
dispersion and multiplex spectrometers. Most common instruments
‘ employ the dispersion mode in which a prism or grating separates the
spectrum into narrow bands of energy (resolution elements) which can be
monitored independently. Spectrometers operating in the multiplex mode
utilize a single detector to monitor the total beam which is modulated in a
manner so that the intensity for each resolution element can be extracted

from the composite signal by use of mathematical methods.

10

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Each of these major categories can be divided into at least two signiﬁcantly
different subgroups. In the dispersion method the two subgroups are:
scanned spectrum methods in which a single detector is used and array

detector methods which uses a multichannel detector.

The two subgroups of the multiplex methods are Fourier Transform
Spectroscopy (FTS) and Hadamard Transform Spectroscopy (HTS), each of
which operates on a different principle. Figure 2.1 represents the family

tree of rapid scan spectrometers.

Dispersion Techniques

Dispersion can be deﬁned as breaking apart or separating a mixture of
wavelengths into its component wavelength. This can be accomplished
with a Prism using the phenomenon of refraction or with a Grating using
the phenomenon of diffraction. Dispersion is the mechanism on which the

most common spectrometers are based.

A spectrometer must eventually produce as the end result a spectrum,
which is a plot of intensity versus frequency. Therefore, in conventional
spectrometers, the polychromatic radiation must be fanned out or
separated into bundles of (almost) monochromatic radiation. Then by some
suitable mechanical arrangement the fanned out radiation is slowly swept
past a suitable detector. Each independently identiﬁable set of frequencies
can be termed a resolution element. The detector of the dispersion

instrument then produces a simple electrical signal which is

12

proportional to the intensity of the radiation in the resolution element
striking it. The spectrum produced in this fashion is thus the result of a

series of radiometric measurements.

As previously mentioned the dispersion method is subdivided into scanned
spectrum and array detector methods. Figure 2.2 represents the two
subgroups. In one approach (2a), the dispersed spectrum is scanned
across an exit slit and onto a single detector, the instantaneous response of
which is dependent upon the wavelength and the intensity of the band of
energy being passed by the slit. In another approach (2b), the dispersed
spectrum falls on an array detector, and the array is interrogated to

generate spectral information.

In order to discuss each of the above basic approaches of dispersion
scanning spectrometers it will be helpful to take the dispersion portion of
the RSS family tree and expand it further to the variety of RSS techniques
developed and then brieﬂy explain each of them. Figure 2.3 shows just that.

Scanned Spectrum Methods, Mechanical Design

Figure 2.4 shows two basic ways of using any conventional two-slit optical
system in rapid scan mode. In method 1, the image of the entrance slit is
swept, after dispersion, across the exit slit with mirror M1, or an optical
equivalent. This takes advantage of the fact that a range of frequencies
strike M1, and the corresponding portion of the spectrum is displayed on

the exit slit jaws. Oscillation or rotation of M1 sweeps this spectral region

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15

across the exit slit and thus sweeps the frequency that reaches the detector.
An advantage to this scanning technique is that M1 is generally a rather
small mirror, which permits high rotation rates. An alternate mechanical
option is to move the exit slit laterally across the spectrum, as is
conveniently done by placing the slit near the edge of the rapidly rotating
disk. The latter technique was used by Bethke (7) in the construction of a
pioneering instrument that achieved 50 microsecond scan times in the near
IR with low resolution (100 cm'l). Both methods, Mo rotation and lateral
slit movement, suffer from the fact that M is perfectly focussed and
illumination is centered on M only at one position (usually when the beam

falls on the slit jaws at normal incident).

The second method shown in Figure 2.4 is more conventional, since
rotation of a littrow mirror or grating that reﬂects a collimated beam is the
usual scanning technique in most ordinary spectrometers. The littrow
mirror can be either oscillated or rotated. The disadvantage of this optical
technique is that a relatively large mirror (or grating) must be moved at
high speeds. Pimental et a1. (8) used a polished aluminum mirror up to
speeds of 200 rps. Then to improve resolution, they have substituted a
conventional replica grating on glass, and routine operation at 60 rps has

given no observable deformation to the grating.

Nevertheless, rotation of this large mirror is a limiting factor, and two
clever methods have been devised to relieve this problem. One, developed by
Terenin and co-worker (9), uses a sequence of rotating mirrors at the
Littrow position so that their rotation rate are additive. More elegant is the

method used by Hexter and Hand (10), in which the Littrow mirror deﬂects

16

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17

the collimated beam into a gallilean telescope to direct the light onto a
smaller mirror which is usually driven at rates as high as 2000 rps. Not
only this instrument permit 10 microsecond scan times, also a rapid
repetition rate that is quite desirable for kinetic studies. There are also
other spectrometer based upon one or another variant of the methods

described(1 0-1 3).

The most widely used design of an RSS based on mechanically scanned
monochromator incorporates a series of 24 corner mirrbrs mounted on a
rotating drum to scan the spectrum across the exit slit (14). The
wavelength scanning principle of this instrument allow the dispersing
optical train to be held ﬁxed. The spectrum is scanned by rotating the scan
wheel; this sweeps the corner mirrors through the intermediate focal plane
formed after the ﬁrst pass through the monochromator. Each time a
corner mirror sweeps through the focal surface, the spectrum is scanned
once. The scan repetition rate equals the number of corner mirrors times
the rotation speed of the scan wheel. Figure 2.5 shows the optical

schematic of the instrument.

This method of scanning, which is applicable to both prism and grating
monochromators, has some important advantages. Because the moving
element is outside the dispersing train and is dynamically balanced, the
optical quality of the spectrometer is not affected by the rapid scanning, and
performance is limited by the signal-to-noise ratio of the detector system, as
in a well designed, slow scan spectrometers. Also, the output is linear in
time and wavelength, the scan time is variable up to 80% of the repetition

time, the entire useful ﬁrst order of a grating spectrum can be covered in

18

one scan, and the design inherently provides for simultaneously scanning
two contiguous wavelength intervals. The device achieves high scan rates
with low drum velocity, but requires exact matching of the corner mirrors

for high photometric accuracy.

Another device employs a vibrating mirror to scan the spectrum (5,15). The
angular velocity of the vibrating mirror is controlled by the frequency of a
triangular wave which provides drive current. A beam splitter divides the
energy from the exit slit into a sample and reference beam, and a
logarithmic ampliﬁer generates an output linear in absorbance. This
instrument scans the region from 370 to 700 nm (or any portion thereof)
with scan rate between 0.01-4000 scan/sec. The zero absorbance line is
constant to within 5% T between 380 and 650 nm, and the absorbance range
is 0.02-2.0 with spectral resolution of 2 nm at 1000 scan/sec. The
instrument was used to study the electrochemical reduction of methyl

violigen at an optically transparent electrode (16).

Electronic designed scanned spectrum methods (Electro-optic materials,
Fabry-perot, etc.) have not been able to gain popularity among analytical
spectroscopists and therefore they will not be discussed here. Multiplex
method (FTS, HTS), even though quite popular, but it is too far aﬁeld from
the main topic of this research and will not be presented here either. A
brief discussion of the photographic emulsion plate as an example of an
array (parallel) detector will be made in comparison to optoelectronic image

devices in the next section.

19

 

 

 

 

 

 

 

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Figure 2.5 Optical schematic of the corner mirror rapid scan
spectrometer.

Limitation of Conventional (Mechanical) RSS

Both of the multiplex methods and the more common spectrum scanning
methods involve mechanical devices. This factor limits the fastest speed
which can be obtained by use of these approaches. All spectroscopic
measurements involve some trade-offs among spectral resolution,
photometric accuracy, and measurement time. An ideal spectrometer
would include the capability of varying each of these measurement
parameters over a relatively wide range. Conventional spectrometers
which feature mechanical scan mechanism have tended to emphasize
spectral resolution and/or photometric accuracy at the expense of scan

Speed.

There are problems of inertia associated with moving parts. A common
problem of conventional (mechanical) RSS techniques, specially in scanned
spectrum methods, is irreproducible baselines, brought about by multiple

mirror reﬂectance, mismatch, and differential aging.

Another problem associated with scanned spectrum RSS is that
information is obtained at only one wavelength at a given time. This gives
rise to longer analysis time and poorer signal-to-noise ratio. The technique
also offers limitations in situations where there are shifts of wavelength or

fast transient processes are studied.

It appears that the alternative to the ﬁeld of rapid scanning analytical

spectroscopy is high speed "parallel detection" with wide spectral coverage.

21

That is how the optoelectronic image devices operate and their advent has
attracted the attention of many rapid scan spectroscopists. This will be

discussed in the next section.

22

OPTOELECTRONIC IMAGE DEVICES (OIDS)

Introduction

Considering all the limitations that various conventional (mechanical)
rapid scanning spectrometers have, it was concluded in the previous
section that an attractive alternative approach is the simultaneous
detection of multiple wavelength segments. The idea of parallel detection
was realized long before the age of optoelectronic image devices was put into
practice in the form of spectrographs (photographic plate detector).
Actually, for a long time, the most widely used and commonly available
parallel detector has been the photographic emulsion. It has a ﬁxed and
accurate geometric registration that allows for reliable wavelength
calibration. Its operation is simple, its cost is affordable and its physical
dimension, and therefore spectral resolution practically unlimited,
depending only on the design of the spectrograph. Unfortunately, the
sensitivity (quantum efﬁciency) of the photographic plate is poor, its
transfer characteristic is non-linear, its accuracy and precision are
marginal and it requires tedious and time consuming calibration
procedures. Worse yet, the validity and usefulness of the spectral data
gathered can be veriﬁed only after the "plate-development" process. This
can result in a loss of invaluable, and at times, irreproducible experimental

data.

23

A more recent multichannel (parallel-detection) approach is based on the
use of PMTs, because of their wide acceptance as a VUV-near IR
spectrometric detector. An array of PMTs is optically arranged across the
focal plan of a spectrograph. Although excellent sensitivity, wide dynamic
range, and rapid response are achieved, in practice this detector provides a
limited number of channels aligned for a predetermined speciﬁc
application (17,18). It does not provide the optimal ﬂexibility in the
interaction between the spectroscopist and his/her experiment that would
be desired of a multichannel detector for general purpose and research

applications.

The newest approach involves optoelectronic image devices which can
provide several hundred independent optical channels in the linear mode
and many thousands in a two dimensional mode, e.g., echelle spectroscopy.
These detectors, when interfaced to a microcomputer (19) or a
microprocessor, provide unprecedented ﬂexibility in spectroscopic data
handling, including signal processing, scan rate selection, various modes
of integration, ultra-rapid data accumulation and real-time display, and
elimination of undesired spectral information through software

manipulation or even through "on-target" random access capability.

In the last decade, as the state-of-the-art optoelectronic image devices have
adequately matured, their use as spectrometric parallel detectors has been
demonstrated, and they have gradually gained acceptance among

spectroscopists.

24

Common Characteristics of Ole

Generally, all optoelectronic image devices (OID), including TV-type or
solid state imagers, are, by their very nature, multichannel parallel photon
detectors which can accurately transform optical images into their
corresponding electronic images. Nevertheless, all these devices
implement the three basic functions of conversion, integration and readout

as elaborated in the following.

Charge Conversion

The role of a charge conversion component is to absorb photons and cause
the removal of electrons from the charge storage area in proportion to the
number of photons absorbed and in positions where the photons were
incident (20). In other words, it converts the photon images into their
electrical analogs, i.e., a pattern in electrical current or charge. The
mechanism of charge conversion varies from one type of OID to another.
Some operate by photoconductivity, some by photoemission, and some like
silicon Vidicon by discharging the stored charge on the depletion region of a
reverse biased photodiode and then reading the remaining charge and so
on. In a photoconductor, the electrical conductivity is approximately
proportional to the irradiance to which it is subjected (21). An electric ﬁeld
is applied to the photoconductor for example, by applying a ﬁxed positive
potential at the transparent conducting signal plate as shown in Figure 2.6,
and driving the exposed surface to near gun-cathode potential through the

action of the periodically scanning electron beam. The ﬁeld decays across

25

Photoconductor Transparent conducting
J ﬁlm (signal plate)

charge

carriers

Faceplate
\ %/
--------- \rg .\ / Irradiance

PHOTOCONDUCTIVE (initial electrical potential is applied to the exposed
PHOTOCATHODE surface of the photoconductor through the action of
the electron beam)

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Photoemitter

  
  

Faceplate

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26

the elemental capacitance according to the resistance-capacitance time
constant of that element. The resistance is controlled by the irradiance, so
that during the exposure interval the electrical potential at the scanned
surface of an element will be more or less positive, depending on the
irradiance. In this way, a pattern in positive electrical potential is formed

at the free surface that is reproduction of the optical image.

Photoemissive photocathodes are used in the image orthicon, the image
isocon, the SEC CAMER, and SIT. The photoemitter is a very thin
semiconductor ﬁlm formed on the inside of the faceplate of the camera tube.
Light incident on the photoemitter excites into the vacuum an emission of
photoelectrons which is proportional to the irradiance. In more modern
tubes, the photocathode is a continuous electrode which does not hold a
charge image. Instead, the photoelectrons are imaged either
electrostatically or electromagnetically to produce a pattern of charge on a

separate storage medium.

In other kinds of OIDs such as the silicon vidicon, the charge conversion
component are the depletion regions of a planar array of reversed biased
silicon photodiodes which are charged up to a known voltage. When
irradiated, they become discharged leaving a charge pattern corresponding
to the light intensity pattern. This remaining charge pattern is eventually

read out as will be discussed in this chapter.

There is apparently a fourth option for the transducer, the photovoltaic
effect, the same as is used to generate power in solar cells. The photovoltaic

effect is not used in camera tubes because the voltage modulation produced

27

across a single junction by a convenient irradiance is not sufﬁcient for a
usefully sensitive device. It is conceptually possible, however, to devise a
photovoltaic pick-up tube that depends on the series of voltages developed by

multiple junctions.

Integration and Storage

Again, different mechanisms for storage and integration of charge are
implemented in different OIDs. Aside from different mechanisms of
storage they are either integrating or non-integrating detectors. In an
integrating detector, such as a vidicon, the electrical charge image
developed by the charge conversion component is in general accumulated
on a storage medium throughout the entire exposure time. The charge
pattern is stored for subsequent read-out by the scanning electron beam.
The storage medium may be the photosurface itself, for example, the
photoconductor ﬁlm in the vidicon, or the photoemissive mosaic in the
orthicon. In a non-integrating detector such as plumbicon the signal is not
integrated for the entire exposure time but rather the charge pattern
present at the storage medium on the arrival time of the electron scanning

beam becomes read out.

Read-out

The ﬁnal product of a read-out system is a video signal. This might be done
by an electron beam scanning like in TV-type OIDs, or by some electronic
circuitry as in solid state OIDs. In TV-type camera tubes the stored

electrical charge pattern is read out by the scanning electron beam to form

28

the video signal. An electron gun in conjunction with electron optics,
provide a means of uniformly depositing electrons on the photoconductor or
target surfaces. Electrons emerging from the gun are made to form a beam
and are focused at the surface noted above. The focused electron beam
follows a predetermined pattern of movement tracing out scanning lines
designed to provide uniform coverage of the surface. The process is called a
raster scan and might simply consist of the focused beam moving across
the surface in parallel horizontal lines, beginning at the upper left corner
and ending at the lower right corner as in'Figure 2.8. The process is
repetitive and the time it takes for the electron beam to cover the surface is

called the raster period.

In normal operation, the substrate of the detector is biased positively with
respect to the cathode of the electron gun. The substrate potential relative to
cathode potential is called the target voltage and is typically 5-10 V. The
impinging electron beam thus strikes the substrate with a maximum
energy of 10 eV and removes the charge pattern from small sections of the
storage area so as to be able to reconstitute the intensity and position

relations of the original signal.

As mentioned already, all OIDs have the above basic three components in
common, but the methods of implementation vary from one type of device to
another. Historically the trend in the OID industry has been toward
simpliﬁcation via consolidation of these components. In some OIDs, all
three components including the video ampliﬁer itself are combined on a

single monolethic silicon crystal wafer (22).

29

Classiﬁcation of Ole

Optoelectronic image devices can be classiﬁed into at least three rather

broad categories:

I. TV-type image devices also referred to as electron beam
imagers.

II. Intensiﬁed imaging devices.

III. Solid state imaging devices (SSID).

Figure 2.7 outlines the family tree of ODS. The variety demonstrated in
this outlines implies that there are important and careful decisions that
have to be made by the chemist who is interested in using one of these
detectors. The decisions are ﬁrst of all whether the general idea of the OID
is compatible to his/her problem. Secondly, which of the detectors will best
ﬁt his/her need and will be his/her optimum detector of choice. As will be
seen later there is no simple answer to these kinds of questions, since the
compromise and trade-offs involved in going from one device to another are

quite important in some cases.

All of the varieties of image devices listed have been applied as
spectroscopic detectors. Not all of them, of course, have found widespread
applications. Only a few of them have gained particular attention in the
ﬁeld of rapid scan spectroscopy and their attractiveness and promising
features are continuously growing among analytical chemists. Therefore,

only those devices that have successfully been used as an RSS detector

30

 

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(designated with an asterisk(*) in the family tree) will be discussed in a
fairly detailed manner here. The reader can refer elsewhere (23-25) for

brief discussions of the rest.

- TV -Type Camera Rhee, Silicon Vidicon (SV) and
Silicon Intensified Target Vidicon (SIT) Tubes

The term vidicon is frequently used as a generic term for photoconductive
camera tubes. There are many types of vidicons in use today such as
silicon vidicon, plumbicon (PbO) and Sb203 (often referred to as Vidicon),
which differ in the material used to form the photosensor and target,
mechanism of read-out and whether they are integrating or non-
integrating type. Among all the types of vidicon, the silicon Vidicon has
found the widest application in analytical rapid scan spectroscopy for a

number of reasons which will be explored in this section.

The heart of the silicon vidicon (SV) is a single monolithic silicon crystal
wafer with a microscopic array of a few million diode junctions grown on it.
The p-regions of the diodes acts as charge-storage regions and the p-n
junction depletion region is the light transducer. All diodes have a
common cathode and their isolated anodes are selectively addressed by a
scanning (readout) electron beam. Figure 2.8 and 2.9 show the Vidicon tube

geometry, beam scan pattern, and mechanism of signal production.

In normal operation, the diode array substrate is positively biased with

respect to the electron gun cathode. The substrate potential relative to the

32

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33

cathode potential is referred to as the target voltage and is typically 10 V.
The impinging electron beam thus strikes the mosaic and deposits
electrons on both the p-type islands and the silicon dioxide ﬁlm
surrounding the diodes, which isolates the substrate from the beam. Since
the resistivity of the silicon diordde ﬁlm is very high, the electronic charge
accumulates on this surface and charges it to some voltage very close to the

cathode potential, where it remains until the next readout.

The beam diameter as indicated in Figure 2.9, is generally larger than the
diode spacing to eliminate any need for registration between the beam and
the mosaic. The electronic charge deposited by a sufﬁciently intense beam
will place a reverse bias of 10 V on the diode as it scans over the array. This
bias will create a depletion width of approximately 5 M with a 10 ohm-cm
substrate, giving a junction capacitance that result in an effective charge-
storage capacitance for the target of approximately 2000 PF/cmz. Notice
that, at this bias, the silicon surface under the oxide will normally be
depleted, as indicated in Figure 2.9 with very low values of diode leakage
currents (less than 10‘13A/diode). The diodes remain in the full reverse
biased condition throughout the entire frame period, if they are not

illuminated.

Almost all of the incident light associated with the image is absorbed in the
n-type region, each absorbed photon giving rise to one hole-electron pair.
Since the absorption coefﬁcient for WW IS light in silicon is very large, the
majority of the photo-generated carriers will be created near the .

illuminated surface. This will increase the minority carrier (i.e., hole)

34

Si 02 Insulation

 

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Figure 2.9 The Vidicon target and signal generating mechanism

35

above its thermal equilibrium value and cause a net diffusion of holes
towards the reverse-biased diodes. If the lifetime of the holes is sufficiently
long and the illuminated surface has been treated properly to reduce
recombination effects, a large fraction of the photon-generated holes will
diffuse to the electric ﬁelds associated with the depletion regionsof the
diodes and will contribute to the junction current. - The light induced
junction current will continue to flow and discharge the junction
capacitance throughout the frame period as long as the diodes remain in
the reverse-biased condition. Thus, high light levels require high values of
reverse-bias voltage or high values of junction capacitance to avoid
saturation. The video output signal from each diode is created when the
electron beam returns to a diode and restores the original charge by

reestablishing the full value of reverse bias.

Because of the insulating properties of the depletion region, one can think of
each photodiode acting as an elemental capacitor which is charged to an
initial potential by the electron beam (26). In this manner each diode
storage capacitor has a portion of its original charge neutralized, creating a
charge pattern which is stored on the beam side of the target between
successive passage of the readout beam. This charge pattern is an
electronic image corresponding to the optical image focused on the
transducer side of the target. The electron beam recovers this spectral
information and returns the diode array to its initial condition by depositing
an amount of charge equal to that discharged by the. light (over the interval
of time since the last scan of the beam). The elemental capacitor
recharging current is capacitively coupled in the external circuit and

ampliﬁed to produce a position encoded photocurrent (27). The relationship

36

between the observed photocurrent from any area (A) of the target and the
photon ﬂux (N) on that area over the exposure time (Te) is given by:

TO
I = QeGAT;1 I(N)'ydt amps
0

Where
Q = quantum efﬁciency of the photosurface;
e = electron charge, coulombs;
G = target gain (G = 1 for SV);
A = area of target scanned as unit of information, cm2;
N = incident photon flux, cm'zsec'l;
7 = slope of current vs. luminous ﬂux curve (7 = 1 for SV);
Tr = readout time to scan area A, sec; and

Te = detector exposure time between readout, sec.

With a constant photon flux over the exposure time the above equation
reduces to I = KAN (Te/Pr) where K = QeG. Typical signals are on the order
of a few nanoampers. As can be seen from the equation there is a linear
relationship between the intensity of the incident light and the magnitude of
the resulting photocurrent.

Vidicon detectors and particularly the silicon Vidicon with its various
image-intensiﬁed derivatives offer a number of unique advantages and, of
course, some limitations as detectors for rapid scan spectroscopy. The most
useful imager for UV to near-IR spectrometric application is the silicon

vidicon. Their behavior and performance are better controlled (even if not

37

always fully understood). The ability to control the time and sequence of the
beam readout (not yet taken advantage of) can prOvide the user with a great

deal of ﬂexibility in the operation of the device.

Silicon vidicon tubes use ﬂat, thin-layered sensors, placed at the focal plane
of a spectrograph, with an excellent geometrical registration between
wavelength and position on the detector surface (spectral-to-spatial
conversion). Once the detectors are calibrated, there is no need to verify this
registration periodically, unless their position in relation to that of the light
dispersive element has been altered. The monitored spectra can be
observed in real time and any gross deviation from the expected result can
be detected immediately and corrected. In this way a great deal of time can

be saved and the loss of highly valuable data can often be prevented.

A large portion of the sensitivity of the device is due to combination of the
advantage of a multichannel detector and integrating detector. The
"integration" mode of detection is derived from their storage
characteristics. Since the vidicon responds to energy (rather than power) it
can be used as a very efﬁcient storage device. The procedure by which SVs
integrate is called "charge integration". This integration capability can be
used to enhance weak signals by increasing the exposure time by means of
blanking the electron beam an inhibiting the deﬂection circuit for an

integer number of frames (time required to record one spectrum).

Charge integration can be utilized only until the most intense feature
saturates. If integration continues after an intense region has saturated,

the charge from the saturated channels will spread into adjacent channels,

38

in contrast to linear diode array (28). The dark current charge also
increases linearly with exposure time, so that it,too places an upper limit
on the integration time that can be used for signal enhancement. Cooling
of the SV signiﬁcantly lowers the dark current (23) and permit longer

integration time.

Another attractive feature of SV is that it is a two dimensional 01]) with an
area-array target comprising a few hundred thousands discrete
photodiodes. Since these diodes can be randomly read out by the scanning

beams, the detector is capable of performing some very useful spectrometric

tasks:

1. 2D spectral contours, such as obtained in "total luminescence
spectroscopy" (Ex. vs. Em.) (29) can be simultaneously
acquired, and actually monitored in real time.

2. The electronic division of the target into a large number of

individual tracks, each of which has ~5OO channels, provides
the means for a rather unique dual—beam and more generally

poly-beam spectroscopy.

3. The adverse effect of blooming (explained later) and spectral
interference, whereby spectral features masked by dominant

neighboring features, can be largely reduced.

For detection of low light levels (i.e., single photoelectron), an image

intensiﬁcation section is added to the silicon vidicon. The resultant imager,

39

 

the silicon intensiﬁed target (SIT) vidicon has a photocathode (transducer)
that converts the photon image into a corresponding photoelectron image.
The generated electron image is accelerated (typically 7-9 KV) and focused
onto the silicon target. Since the number of electron-hole charge pairs
produced on the target is proportional to the potential of the incident
electrons (approximately one charge pair per 3-6 eV) an internal gain of
approximately 1500 is typically achieved, i.e., this is the number of electrons
produced from each photoelectron emitted form the photocathode. With
this gain, the signal is sufficiently enhanced (compared to readout noise) to
allow the detection of very weak signals.

Even though SV tubes are preferred over SIT tubes for detection below 360
nm and above 850 nm and whenever cooling capability (T<40°C) and
experimental conditions allow for long observation periods, in the case of
low light level spectroscopy (such as luminescence) the SIT tube with its

high in-target gain makes a superior detector.

Finally, the SV tube is a better detector than the other two: vidicon (Sb203)
and plumbicon. It responds over a wide spectra range (generally 350-1000
nm, but tubes with limited response down to 200 nm are available) with
high sensitivity, whereas the other two are limited to use between 400 and -
600 nm. SIT has lower lag (will be explained later), lower dark current,
linear response (g = 1), high resolution, freedom from image persistence or

damage from intense sources, and linear dynamic range of at least 104 (30).

Two of the most serious technical problems with the vidicon are the lag and

blooming characteristics. Lag is the incomplete erasure of images on the

40

ﬁrst few readout cycles aﬁer storage. This phenomenon is caused by target
capacitance and reduced beam acceptance at low discharge levels. This
characteristic is more of a problem for low level signals. Blooming, or
signal spreading, is caused by the lateral diffusion for charge from a region
of high spectral intensity to a region of low spectral intensity. The
migration of charge is caused by the potential gradient existing between the
two regions and is a result of ﬁnite target conductivity. Blooming increases
with increasing signal levels and causes a loss of resolution. Blooming
may also prohibit the acquisition of information from a low intensity line in

close proximity to a strong line.

Another undesirable feature of SV that limits its multichannel detection
performance is a substantial coherent pattern noise caused mainly by

diode-to-diode variations in the response and in the dark current.

The choice between operation in the single-frame and cumulative
integration modes presents a dilemma. With successive readout
(cumulative) at a standard TV-rate, the target reaches steady state and the
output response is linear, but the SNR is substantially reduced by the
introduction of ampliﬁer noise with each readout. Alternatively, a long
exposure terminated by a single readout (single frame) eliminates this
effect but produces a non-linear response as a result of the incomplete
readout (lag) of the only slightly discharged pixels (at region of low light

exposure (31). However, ways have been proposed to remove this effect (32).

The list of photometric applications to date includes: astronomical

observation (33), scanner and imaging system for earth observation (34),

41

ﬂame multielemental atomic emission (35-41), and absorption (42-45),
molecular absorption (46-48), molecular ﬂuorescence (49,51), picosecond
(laser) ﬂash photolysis and molecular absorption spectroscopy (52),
picosecond (laser) molecular ﬂuorescence (53), including a single living cell
ﬂuorescence (54-55), time resolved resonance raman spectroscopy (52),
HPLC detection (56-57), stopped-ﬂow kinetic detector (58-59), electro-optical

ion detectors in mass spectroscopy (60), and others.

IMAGE DISSECTOR

The image dissector is one of the oldest and simplest non-storage camera
tubes. It has been used in earlier studies (61-62), and recently received
some attention (63) but not comparable with either vidicon or solid state

devices.

An image dissector, in reality, is nothing more than a photomultiplier with
a small, electronically movable photocathode area which can therefore be
operated as an all electronic low inertia microphotometer. In other words,
it exactly functions as an electronically movable exit slit of the
spectrometer. As Figure 2.10 shows the image dissector acts like a PMT in
which a ﬁeld of view is electronically scanned across a small aperture. It
consists of a photocathode separated from a conventional dynode chain

electron multiplier by a plate corresponding to the light image

42

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43

incident on the photocathode. Electronic scanning is accomplished by
electromagnetic sweep coils surrounding the tube, which sweep the
electron image across the face of the plate. Only the portion of the electron
image which pass through the hole in the aperture plate can reach the ﬁrst
dynode and be ampliﬁed by the dynode chain. As with other scanning
systems, signal averaging techniques can proﬁtably be employed with
image dissector systems. These tubes have been employed with both one

and two dimensional dispersive systems.

Danielsson and Lindblom (64-65) and Danielsson, et a1. (66) have recently
designed an image dissector/echelle system and have described its
application to multielement analysis. This system provides wide
wavelength coverage at high resolution. For example, the system covers
the whole spectral range from 200-800 nm with resolution on the order of
0.001 nm.

Some of the major advantages of an image dissector are the excellent
resolution, extremely high scan rates (up to 1 scan/nsec), easy random
access, relatively simpliﬁed computer interfacing, large dynamic range
(typically 105-106), and the virtual absence of lag and blooming effects.
Unlike vidicon, the image dissector can be operated at stationary deﬂection,
which greatly simpliﬁes software control and permits more rapid
acquisition of spectral information. Although blooming does not occur with
the image dissector, an analogous phenomenon may reduce the available
linear dynamic range. If bright images are incident on the photocathode

when a measurement of illumination at a low level is being made, the back

44

scattered ﬂux from internal tube parts may limit the available dynamic
range to only 2 to 3 order of magnitude, depending on the area, brightness,
and location of the disturbing ﬂux. Another limitation of the device is that
it is not an integrating sensor, such as the vidicon. This means the device
can not accumulate and store information from one part of an image while
another region is being integrated. Thus, if one is scanning N resolution
elements in a given time, then the random component of the signal to noise
ratio for each element would be degraded by a factor of N as compared to the
continuous monitoring of a single resolution element for the same time

period, assuming that noise is limiting.

45

SOLID STATE IMAGING DEVICES

Introduction

Solid state imaging devices consist of arrays of discrete photosensitive
elements which are scanned by means of electronic circuits that are
integrated on the same semiconductor wafer. The various solid state
devices also referred to as silicon self-scanned array devices are similar in
the mechanism, limitations, and capabilities of the sensing elements, but
differ most signiﬁcantly in the arrangement for signal ampliﬁcation and

scanning.

In the last decade, there has been an intense development effort in the
design, manufacture, and application of solid state (SSIDs) devices. Even
though the development of such devices was primarily aimed at capturing
high commercial markets, spectroscopic detection technology is becoming a
major beneﬁciary. Among various solid state devices, the photodiode array
has been the most popular detector for analytical spectroscopy. The
photodiode array (PDA), also referred to as linear diode array (LDA), will be
discussed in detail in the next chapter. Two other solid state devices,
charge coupled devices (CCD) and charge injection devices (CID) have
recently received attention and seem promising for the future. They are

discussed here.

46

CHARGE TRANSFER DEVICES (CTDS)

Charge transfer devices are solid-state integrating optoelectronic image
devices constructed by employing conventional VLSI photofabrication
technology. In contrast a photomultiplier tube (PMT) which produces a
current proportional to the instantaneous photon ﬂux, CTDs similar to
PDAs integrate the photogenerated charge as light strikes them. The
mechanism of charge collection, storage, and read out in CTDs is different
from PDAs. In PDAs, the photon generated charge is collected and stored
in a reverse-biased photodiode whereas in CTDs the charge collection and
storage occurs in a metal-oxide-semiconductor (MOS) capacitors. The
amount of photogenerated charge stored in each detector element is

proportional to the number of photons that strikes that detector element.

There are two mechanism by which the accumulated signal information in
CTDs can be measured (read out). These two charge sensing mechanisms
are known as inter-cell and intera-cell charge transfer modes. Based on
these two different charge sensing methods, two types of CTD detectors
have been designed. The charge coupled device (CCD) utilizing inter-cell
charge transfer mode and the charge injection device (CID) using the
intera-cell charge transfer mode. An individual detector in a CTD array
consists of 3 doped silicon region used for generation and storage of
photogenerated charge. This doped silicon region is separated by an silicon
oxide layer from a series of conducting electrodes used for signal
information read out. A basic operation of a single CTD detector in shown
in ﬁgure 2.11 .

47

.1

The negative voltage on the conductive electrodes with respect to the silicon
creates a charge inversion region which is energetically favorable location
for the mobile minority carriers (holes) to reside. An electron-hole pair is
generated by the absorption of a photon of appropriate energy. The proton is
promoted into the semiconductor conduction band and the hole moves
toward and is stored in the inversion region. When one electrode is held at
a more negative potential than the other, the positive charges (holes) will be

more favorably stored under this more negative electrode.

photon

 

 

 

.. . ++++
SIIICOI‘I oxnde (

 

n-doped silicon region »

 

 

Figure 2.11 A single CTD detector element.

One can measure the amount of accumulated charge using either of the
above mentioned inter-cell or intera-cell charge transfer modes. The
charge measurement process consists of either shifting the charge from

the detector element where it is' stored to a charge sensing ampliﬁer as in

48

CCDs (inter-cell charge transfer), or by the measurement of the induced
voltage change which results from the movement of the charge within the

detector element as in CIDs (intera-cell charge transfer).

Since their introduction in the early 19703 , there have been a great deal of
research efforts aimed at exploring the capabilities of CTDs as
multichannel detectors for analytical spectroscopy (70-73). These research
activities have been mainly motivated by the historical desire to replace the
single channel PMTs with an ideal multichannel detector (74-76). Earlier
attempts at accomplishing this task were the use of television-type camera
tubes such as silicon vidicons, SIT tubes, and image dissectors which were
previously described. These devices possess a large number of pixels for
rapid coverage of a wide wavelength range, while being compact in size.
However, as previously explained, these detectors all employ an electron
beam to carry the video signal information. It is absolutely necessary for
this electron beam to be precisely controlled. Any uncertainties in the
control of this electron beam can hamper the precise pixel selection in these
devices. Moreover, silicon vidicons and SIT tubes suffer from blooming and

lag problems under high light level conditions.

In an attempt to avoid these problems, solid-state multichannel image
detectors were developed. The PDAs were the ﬁrst solid-state multichannel
detectors to be successfully used in scientiﬁc spectroscopic measurements.
Throughout the late 1970’s and early 1980’s, PDAs have found widespread
application and acceptance in the ﬁeld of analytical rapid scan spectroscopy
and several coMercial instruments based on its technology are now

available from a number of manufacturers. In many situations, however,

49

neither the televesion-type camera tubes nor the PDAs are capable of
competitive performance with PMT in terms of sensitivity, dynamic range,
and noise. This has limited the successful applications of these devices to
those circumstances where the multichannel advantage outweighs the
sensitivity, dynamic range, noise, and bloomig (less in PDAs)

disadvantages.

Recently, the extensive research by Denton and coworkers in evaluating the
spectrometric performance of CTDs has demonstrated that when compared
on detector-element by detector-element basis, these devices are capable of
superior sensitivity and dynamic range to that of the PMT and some of
these devices can even exceed the sensitivity and dynamic range of all
available detectors (77-80). A number of manufacturers are currently
producing a wide range CTD devices with different electro-optical
characteristics, formats, and operating requirements. Current CTD
detectors can have peak quantum efﬁciencies over 80%, a high spectral
responsivity ﬁ-om the soft X-Ray to the near IR regions, very low dark count
rate that allow very long integration times, and read noises several orders
of magnitude lower than those of commercially available photodiode arrays
(81).

The detailed operation of the CCDs and CIDs along with their performance
characteristics inﬂuencing analytical spectroscopy have been described by
Denton and coworkers (77,79). Here, a brief description of the CCD and CID
devices as well as their major advantages for and their applications in

analytical spectroscopy is presented next.

50

Charge Injection Device (CID)

CID imaging device utilizes an x-y addressed array of charge storage
capacitors which store photon generated charge in metaloxide
semiconductor (MOS) inversion region. Each single detector element
consists of a pair of charge storage MOS capacitors (ﬁgure 2.11) of which
one is controlled by an x-row drive line, and the other by a y-column drive.
The two storage MOS capacitors are formed by two conductive electrodes
and an n-type silicon region separated by a silicon oxide insulating layer. A

schematic representation of a typical CID detector is shown in ﬁgure 2.12.

A unique feature to note about the CID device is its random access readout
capability. . High speed shift registers (addressing registers) are used to
randomly address any single detector element. As shown in ﬁgure 2.12,
these addressing registers sequentially connect rows and columns to the
output ampliﬁer and drive signal, respectively. The photon generated
charge during the integration is stored in the potential wells formed by by
applying negative voltages (with respect to substrate) to both conductive
electrode in each detector element (ﬁgure 2.11). One of the electrode
(storage) is held at a more negative potential than the other, making the
storage of photogenerated charge more favorable under this electrode. This
energetically more favorable charge storage location is indicated by the
depth of a potential well under the more negative electrode.

As was pointed out earlier, the accumulated charge in the CID device is

measured using the intera-cell charge transfer mode. This involves

51

shifting the charge from under the more negative storage electrode to the
other less negative (sensing) electrode and measuring the potential change
induced on the sensing electrode. The induced potential change is dV =
dQ/C, where C is the capacitance of the MOS sensing capacitor and dQ is
the quantity of charge transferred. A differential charge sensing scheme is
employed where two charge sampling, one before and one after the charge
transfer, is performed. The difference in charge (dQ) between the two
sampling is proportional to the amount of photogenerated charge. In
practice, one can accomplish this process by ﬁrst driving the voltage of
storage electrode from its integrating negative potential to positive causing
the collapse of the inversion region under this electrode and; thus,
transfering the charge to the only remaining potential well under the
sensing electrode. Next, the voltage of the sensing electrode is driven
positive which results in the collapse of inversion region under both
electrodes and the injection of charge from the entire detector element into
the substrate.

The charge readout sequence in the CID device can be completed in one of
two ways (79). In one way known as the nondestructive readout (NDRO);
after the second voltage measurement on the sensing electrode is made, the
charge is shifted back under the storage electrode (original position) by re-
establishing the potential well under this electrode. This returns the
detector element to its condition prior to charge measurement
nondestructively, i.e., without any change in neither the amount nor the
location of the photogenerated charge information. The second option is
that upon completion of the diﬂ'erential voltage measurement, the potential

well under the sensing electrode is made to collapse. This is known as

52

 

Drive

 

 

 

 

 

 

 

  
  

Inject

Inject
control

 

Figure 2.12 A hypothetical CID device

53

Integrate First sample

25¢

    
    

 

 

 

 

 

 

 

’
NDRO
- +
0
<5 6 ('3 '
DRO
+++
I ++ + l
Second sample
Figure 2.13 The destructive and nondestructive readout scheme in

the CID

54

 

destructive readout (DRO), since it causes electron-hole recombination and
destructive elimination of charge from the detector element. These two

readout methods are shown schematically in ﬁgure 2.13.

The CID device has several unique features which makes it very useful as a
spectroscopic detector. First, it can be operated with random access
readout instead of sequentially scanning through the whole array as is the
case in other solid-state devices. This allows for faster readout speed for
rapid analysis. Second, it is very resistant to cross talk (blooming) between
detector elements except under extremely intense illumination conditions
where the charge migration across the substrate junction may not occur at
sufﬁcient rate. Third, the read noise in the CID device can be signiﬁcantly
reduced through the use of NDRO readout. Besides photon counting PMT
detectors which essentially have no noise associated with their readout
system, almost all other spectroscopic detectors including solid-state
devices have some read noise introduced by the detector and its associated
readout electronics. The read noise in solid-state imaging devices varies
from more than 1200 electrons for PDAs to less than 5 electrons for some
CCDs (77). The S/N ratio of a photon flux measurement can be increased by
averaging a number of NDROs with the CID. Contrary to typical signal
averaging which is subject to various photon noises, NDRO averaging in
the CID is free from photon noise and only subject to the read noise.
Therefore, if the read noise is random, the NDRO averaging can result in a

factor of 10 reduction in the effective read noise (81 ).

Now, that many of the earlier problems with the CID devices such as low
response in the UV region and the absorption of light below 350 nm by their

55

 

glass windows have been removed (72), and in light of some important new
developments discussed above, the CID technology appear to be ready and
well suited to many interesting spectroscopic applications. A recent
application of a two dimensional CID to multielement ICP emission
spectroscopy (82) is a very good example. In this regard, the CID device
offers several important advantages. These include full utilization of the
enormous information available in the emission signal, random access
readout and random access integration through NDRO which leads to a
very wide dynamic range as well as clean background correction and

dynamic optimization of the experiment (78).

Charge Coupled Device (CCD)

Like most imaging devices, charged coupled devices (CCD) were not ﬁrst
intended for use in spectroscopy but rather for a wide variety of military,
industrial and consumer imaging applications. Presently, however, a
variety of CCD detectors optimized for scientiﬁc imaging applications are

available from many manufacturers.

Similar to the CID, the structure of CCD also consists of a series of MOS
capacitors for storage of photogenerated charge packets (ﬁgure 2.11). The
two detectors do, however, differ in a number of ways. First, CCDs are
usually manufactured with p-type materials (as opposed to n-type in 01133),
so that electrons are stored in the potential wells. Second, the structure of
CCDs does not allow for random access readout and the entire array must

be read before the next exposure. Third and the most important difference

56

between the two detector is in the way which the charge packets are read
out. As mentioned before, CCDs employ inter-cell charge transfer mode for
charge information readout. Analogous to the CIDs, a number of
independent conductive electrodes are used in each detector element to
effect the charge transfer. The number of electrodes used may range from
one to four giving rise to uniphase to four phase CCD device (83),
respectively. By changing the voltages of these conductive electrodes in a
controlled way, the charge from one detector element is sequentially shifted
to the next (analogous to an analog shift register) and on to a single on chip
charge detector diode and voltage sensing ampliﬁer located at the edge of

one .or two dimensional arrays of detector elements.

A schematic diagram of a typical two dimensional CCD is shown in ﬁgure
2.14. Columns are clocked in parallel causing the charge in an entire
array to be shifted, one row at a time, toward a serial shift register where it
is serially moved to a single low capacitance output node for readout. The
migration of charge from one column to another is prevented by means of
ﬁxed potential barriers between them. The sequential shifting of charge
from each detector element to a single low capacitance output node allows
for higher S/N ratio to be achieved in the CCD output compared to the CID
and the PDA which employ mutiplexed architecture. The capacitance at
the input of an off-chip ampliﬁer in the CID, for example, is relatively
higher than that of the CCD and; thus, the voltage change induced on this
capacitance (dV = dQ/C) for a given quantity of charge is lower in the CID
device. In fact, the dominant noise source in the CID is directly

proportional to the capacitance at the input node of this off-chip ampliﬁer.

57

CCD array —\

  
   
  
 
 
 

‘/Serial register

Potential barrier

Parallel
clock

Detector
element

Output

ampliﬁer register

Serial
clock

 

Figure 2.14 Diagram of a hypothetical three-phase CCD

58

The elimination of the multiplexed circuitry and its replacement with a
single low capacitance readout node provides for an ultra low read noise
observed in the CCD device. However, before the charge from a given
detector element reaches the output node, it undergoes numerous transfers
from detector element to detector element, as well as from the parallel
register into the serial register, and ﬁnally from the serial register into the
output node. This demands for an extremely efﬁcient charge transfer
process where after several thousand transfers, the amount of charge in

each packet stays substantially the same with minimal signal degradation.
Charge transfer efficiencies on the order of 0.99999 is required for this
purpose (79) and fortunately CCD devices charge transfer efﬁciencies on the

order of 0.999995 are available.

A very important characteristic of the CCD which makes it unique among
all other imaging devices is its ability to utilize a special charge readout
mechanism called binning (84). Binning is a process where the
photogenerated charge packets from more than one detector elements is
physically combined into one single charge packet (bin) and is subsequently
transferred to the output node for readout. With the CCD device, it is
possible to perform serial, parallel, as well as two dimensional binning of

any number of consecutively arranged detector elements.

The summing of the photogenerated charge packets from multiple detector
elements has a direct effect on the SIN ratio and the dynamic range of
spectral intensity measurements. The on-chip analog summation
(binning) is deﬁnitely advantageous over digital summation in a computer

memory. This is because the binned charge packet (charge from a group of

59

detector elements) is subject to only one read noise, whereas digital
summation adds the read noises from every detector elements in the group.
Under detector read noise limited situations (very low-light-level), the noise
in a digitally summed signal is greater by a factor equal to the square root of
the number of summed detector elements. Moreover, the number of analog
to digital conversion required to process a group of charge packets is
reduced by binning which, in turn, reduces the total detector noise and
increases the readout speed. It is also important to point out that the
sacriﬁce in resolution accompanied with the gain in sensitivity brought
about by binning may not be that signiﬁcant due to the original large

number of resolution elements in the CCD arrays.

As evident from the above discussion, there are several unique
characteristics of the CCD device that encourages its application in
analytical spectroscopy. First, it is the most sensitive multichannel
detector for low-light-level scientiﬁc applications. The high sensitivity of
the CCD device is due to its high quantum efﬁciency, ability to integrate
charge for a long time, negligible dark current, and most importantly, its
ultra-low detector read noise. Second, all elements integrate over precisely
the same time frame before the signals of all elements are simultaneously
moved into the analog shift register and serially clocked into the output
node. One disadvantage with regard to spectroscopic applications may be
the spatial format of two dimensional CCDs where the height to width
aspect ratio of a single detector element does not match that of a typical slit
image found in spectroscopy. However, the added ﬂexibility provided by
binning in the CCD can easily overcome this spacial format disadvantage.

Appropriate number of detector elements may be binned to exactly match

60

any desired slit image. In fact, if so desired the entire two dimensional
CCD can be binned into a single channel detector with a large active area,
or into a linear array detector with a high aspect ratio or, with appropriate
optics, into two stacked linear array for truly multichannel dual-beam

operation(84).

The high sensitivity obtainable with the CCD device, makes it a particularly
suitable detector for low-light-level spectroscopic applications such as
luminescence and raman spectroscopy. Recently, very promising results
have been obtained with the application of the CCD device to
chemiluminescence (85) and molecular ﬂuorescence (86). In both cases,
signiﬁcant improvements in sensitivity and dynamic range have been
realized through the use of CCD array detector. The case of molecular
ﬂuorescence is particularly interesting in that despite the use of a mercury
pen lamp as excitation source that is modest in both cost and photon ﬂux, a
wide dynamic range (over 6 orders of magnitude) as well as very low
detection limit of 1 fmol has been obtained (86). Based on this kind of
performance, the authors conclude that these type of CCD based
luminescence detection systems can be well suited for use in conjunction

with FIA and HPLC analysis;

Device Comparison

Comparison among various $81le and their advantages and limitations as
rapid scan multichannel spectroscopic detectors appear in previous

sections of this work. By now the reader must be very well aware of the fact

61

that it a difﬁcult , if not impossible, task to single out one particular detector
as the best detector for all RSS applications. All of them involve some trade-
offs among resolution, photometric accuracy, and measurement time.
Other areas which also merit signiﬁcant attention in selecting an
appropriate multichannel detector for a given situation are sensitivity,
dynamic range, spectral response, and addressing schemes. In fact, the
desire for improvement in these areas has been the motivation behind
analytical spectroscopists to keep on searching for new devices and
technological advancements in other areas and exploit them for
spectroscopic application. Actually, that is how the imaging devices came

to be used in analytical spectroscopy.

However, it is obvious at this point that the optoelectronic image devices are
superior detector over conventional mechanical systems at least for
analytical rapid scan spectroscopy in the WW IS regions. Their
applications have already broadened the ﬁeld and will continue to do so.

Within the OIDs, solid state devices have recieved greater attention in
recent years and have become well accepted among analytical
spectroscopists. This is due to the fact that these devices offer long life,
small size, low power consumption, ease of cooling, simpliﬁed circuitry

and thereby ease of interfacing to the computer.

Among solid state devices, the photodiode array (FDA) has been the most
popular multichannel detector for many years. This has been in part due to
the develpment of PDAs speciﬁcally for spectroscopic application.
Additionally, the spectroscopic performance of the PDAs have been well

62

studied and the device has been successfully applied to many areas such as
atomic and molecular spectroscopy, stopped-ﬂow kinetics, and HPLC
detection. These factors as well as commercial availibility of PDA-based
spectrometers in recent years have contributed ti its popularity. In fact,
many spectroscopists had predicted that PDAs would eventually become the

detector of choice.

Recent developments in charge transfer devices (CTDs), however, have
begun to change the equation. Notice, that PDAs do still suffer ﬁ'om several
drawbacks including ﬁxed sequence of scanning, one dimensional format,
higher read noise (due to multiplexed readout scheme), and ﬁnally, lag and
blooming. Moreover, the PDA’s sensitivity and dynamic range does not still
quite match those offered by the PMT, particularly in low-light-level
situations. Even the use of intensiﬁed PDAs does not totally eliminate the
problem, since the dark current noise associated with the photocathode
materials used in the intensiﬁcation stage will still place a limit on the S/N

ratio that can be achieved.

CTDs, on the other hand, provide a number of unique advantages compared
to the PDAs. These include higher density of detector elements, ultra-low
read noise (particularly in CCDs), and random access capibility. Their
sensitivity and dynamic range have been shown to even exceed those of the
PMT when compared on detector element- by-detector element basis. The
imaginative readout schemes offered by the CTDs along with their very low
read noise allows these devices to acheive high SIN ratio measurements

specially at low-light-level.

63

The author believes that once a few remaining obstacles for the CTDs are
resolved in the future, they will most likely dominate the ﬁeld of rapid scan
spectroscopy. One problem is their high cost which is not still affordable by
many potential users. Secondly, the small size of the CTD detector
elements does not match those of the slit-like images encountered in most
spectrometers. Therefore, speciﬁc spectrometers need to be designed to
match the CTD detector elements physical geometry. Third, and most
importantly, intellegent software needs to be developed for control of the
device and efficient processing of the enormous amount of information that
is generated. When these issues are properly addressed and the demand
for their applications increases, the presently high cost of the CTDs will
perhaps become more affordable.

64

CHAPTER3

DESCRIPTION OF A RAPID SCANNING LINEAR DIODE ARRAY
SPECTROPHOTOMETER FOR STOPPED-FLOW KINETICS

Introduction

The purpose of this chapter is to provide a complete presentation of the
instrument developed throughout this research. This is done by ﬁrst giving
a brief historical review of linear diode array applications in rapid scan
spectroscopy in conjunction with stopped ﬂow and then presenting an
overview of the basic parts of the instrument; the function of each part of the
instrument is described. Advantages and disadvantages of the LDA versus
other optoelectronic image devices described in the previous chapter are
discussed wherever necessary so that, this chapter is complementary to the

previous one.

HistoryoftheIDAasanRSSStopped-ﬂowDetector

Only a few of the great many techniques that have been developed for rapid
scanning spectroscopy have been applied to stopped-ﬂow kinetics. Both of
the two general techniques of RSS, i.e., conventional mechanical and
optoelectronic image devices (OID) have been used in conjunction with

stopped-ﬂow and they have been reviewed by E. M. Carlson (87).

65

Until recently the application of the linear diode array to rapid scanning
spectroscopy, especially stopped-ﬂow kinetics, has been somewhat limited.
This has been due to the low manufacture yield which limited the
commercial availability and also the number of sensor element per array.
Now that they have become commercially available with a large number of
elements at a relative low price, these compact integrated circuits are
beginning a new trend. Most experts predict that the self scanned solid
state imagers, especially the LDA will eventually become the OH) of choice.

However, the only report of a rapid scanning stopped-flow
spectrophotometer which utilizes a linear diode array as a detector is that of
Dessey (88). No data have appeared on the characterization or application
of this system.

Functional Description of the Instrument

In traditional stopped-ﬂow spectrophotometers, the reaction under study is
monitored by recording the absorbance change with time of either a
product, a reactant, or in some cases, an intermediate in the reaction.
Because these measurements are done at a single wavelength, they require
a repetitive process of performing the experiment for each wavelength at

which the reaction is to be studied.

The rapid scan diode array stopped-ﬂow spectrophotometer discussed here
with its multiwavelength capability enables one to monitor the light
intensities over a large spectral region simultaneously in a time

comparable to that which a traditional spectrophotometer takes for a single

66

wavelength experiment. This multiwavelength capability provides the user
with the choice of observing any abrupt change of formation of intermediate
species at any wavelength during the course of the reactions. This kind of
information is very helpful for a variety of theoretical studies such as
determining the mechanism of a chemical reaction, rate constant, or rate
expression. The system very quickly provides the analytical chemist with
information over a large spectral region, which allows him/her to look at
all spectral features in a given region and immediately locate the optimum

wavelength desired for the analysis.

Figure 3.1 shows the block diagram of the instrument. The upper half of
the ﬁgure shows the stopped-ﬂow mixing system which is used as a
solution handling system. The sample and reagents are rapidly mixed and
brought into the observation cell. To monitor the subsequent reactions that
take place in the observation cell, a beam of light focused by a double convex
lens is passed through the cell and then into a polychromator module.
Since it is very important for the linear diode array to have a focused
spectrum across its ﬂat surface, the polychromator module employs a
holographic grating. The holographic grating design technology has made
possible the production of ﬂat ﬁeld spectrographic, concave, abbertion-
corrected gratings which by themselves produce a ﬂat ﬁeld image across
the photosensitive elements of the detector with a minimum of stray light.
The polychromator module which contains the holographic grating, linear
diode array and other required optics all in one package has been designed
by P. J. Aiello and is explained in detail elsewhere (89); so, it will not be
further discussed here.

67

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The light dispersed by the polychromator eventually hits the rapid scan
linear diode array, the output of which is recorded by a Tektronix storage
scope. Other important parts of the instrument include: the linear diode
array, the stopped-ﬂow mixing systems and the control interface. The

discussion of each follows.

LinearDiode Array

The linear diode array (LDA) used in this research was purchased from
Reticon Corp. (90). This is a Reticon "S" series Type Rl-512S which has 512
sensor elements and is the ﬁrst solid state image sensor designed

speciﬁcally for spectroscopy applications.

The Reticon "S" series is a family of monolithic self scanning linear
photodiode arrays. The devices in this series consist of a row of silicon
photodiodes, each with an associated storage capacitor on which to
integrate photocurrent and a multiplex switch for periodic readout via an
integrated shift register scanning circuit. The device is packaged in a 22
lead dual-line integrated circuit package with choice of a ground and
polished quartz window or a ﬁber optic faceplate. The pin conﬁguration of
the 512 element array used here is shown in Figure 3.2.

The Reticon S-series self scanning photodiode arrays here consists of 512
sensor elements (silicon diode). The overall length of the array chip is 12.8
mm, and the sensors are on 25 micrometer centers which corresponds to a

density of 40 diodes/mm. Each sensor has a height of 2.5 mm providing a

69

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70

slit like geometry to each element with an aspect ratio of 100:1. This feature
makes the array suitable for use in conjunction with monochromators or
spectrographs. Figure 3.3 shows the sensor geometry and aperture
response function. Each diode consists of an island of p-type bars diffused
in an n-type substrate. Upon irradiating the sensing area, a charge
proportional to the amount of light is generated. The generated charge is
collected and stored on the p-type islands during the integration time. The
stored charges are then transferred to the video line readout. Both the p-
type and n-type silicon substrates are photosensitive. The charge generated
on the p-type region is stored on the corresponding diode, and the charge
created on the n-type substrate between two p-type regions will divide
between the neighboring diodes to yield the response function shown in

Figure 3.3.

Figure 3.4 shows the simpliﬁed equivalent circuit of an RL512S linear diode
array. As can be seen each element contains one photodiode and one
dummy diode, with each having its corresponding storage capacitance.
The diodes are connected to dummy and video recharge lines through
multiplex switches. All the odd sensor elements are connected to one pair
of recharge lines and all the even sensor elements are connected to another
pair. The shift register scanning circuit sequentially turns the multiplex
switches on and off and thereby periodically recharges each element to 5
volts. As a result, a charge of anm is deposited on its capacitance. A two-
phase clock drives the shift registers. The clock also produces a periodic
start pulse for initiation of each scan. The clock frequency determines the
element-to-element sampling rate. The interval between start pulses is the

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integration time. During the integration time the reverse current ﬂowing
in each photodiode gradually discharges the charge on its associated
capacitor. This reverse current consists of two components: the

photocurrent ip and the dark current id which is often negligible.

The photocurrent is the product of the responsivity of diode and the intensity
of light. In a given line scan time the amount of charge which is
discharged from each element is the product of the photocurrent and the
line time. The video line must replace this charge when it samples each
element once each scan. Therefore, each scan from a given N-element
array produces an output signal which is a train of N charge pulses each
being proportional to the light intensity on the corresponding photodiode.
All of the diodes can be sampled in proper sequence by correctly phasing the
two shift registers to the clock drives. Then by simple connection of the two
Video lines together a continuous train of output charge pulses will be
produced. Switching transients are also capacitively coupled into the video
lines by the multiplex switches. The transient are fed into the dummy
lines, so that, they can be eliminated and a clean signal extracted by
reading out the dummy and Video lines differentially. Some of the key

features of the LDA employed in this research are in summary:

1) Simultaneous integration of 512 photodiode sensor elements

with 25 micrometer center-to-center spacing.

2) Integration time as short as 0.5 ms (according to the Reticon

data sheet) or as long as 0.3 s are possible at room

74

temperature. however, in our experience the detector did not

work properly with integration times of less than 4 ms.

3) Clock controlled sequential readout at arbitrary rates up to 2
MHz and simple square wave clock.

4) Differential output to cancel clock switching transients and
ﬁxed patterns.

5) Low output capacitance (2 pF) for low noise.

6) High saturation signal charge (14 C) for wide dynamic range.

7) Wide spectral response (200-1000 nm).

8) Quartz window for UV application.

Stopped-Flow NIixing System

The stopped-ﬂow mixing system described here is a solution handling
system whose purpose is to mix two solution rapidly together and deliver
them to an observation cell where the reaction can be monitored by the diode
array spectrophotometer. A complete schematic diagram of the stopped-
ﬂow mixing system is shown in Figure 3.5. As can be seen in this ﬁgure, it
consists of two delivery syringes, a pneumatic drive piston, four
pneumatically driven check valves, mixing chamber, observation cell, 3-

way pneumatically driven slider valve and a stopping syringe assembly.
Two pairs of check valves works in opposition to one another so that the

syringes are either connected to the reagent or to the observation cell

depending on the direction of pressure. In a normal stopped-ﬂow run (or

75

   
 

  

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76

push), the ﬁrst operation is the ﬁlling of the drive syringes with the
solutions. The pneumatically driven piston shown at the top performs this
operation. The drive syringe piston moves upward, drawing reagent and
sample solutions into the syringes. During this time the check valves
insure that the syringes are ﬁlled with the sample solutions only. Then,
the direction of air pressure is switched and with the 3-way slider valve in
closed position, no solution ﬂows at this time. The drive syringes become
energized, and the check valves prevent the solution from returning to the
sample reservoirs. The solution starts to ﬂow when the position of the 3-
way slider valve is switched to connect the ﬂow system to the stopping
syringe, and the syringe drive piston rapidly strokes forward, forcing the
solution through the mixing chamber. The ﬂow is automatically stopped
when the springloaded stopping syringe comes to rest against a
mechanical stopping block. The reaction occurring in the observation cell
is then monitored. Upon completion of the reaction the 3-way slider valve is
switched, connecting the spring loaded stopping syringe to the waste and
forcing old solution in the stop syringe out the waste port.

The connection of various parts of the system are accomplished by 1/8 inch
Teﬂon tubing (1 .5 mm I.D.) with Altex chemically inert tubing end ﬁttings
(Altex Scientiﬁc Inc., part No. 200-15). The exact dimension and part
numbers of both drive syringes and the stopping syringe (constructed by the
Michigan State University glass shop) or the drive, air valves and check
valves (purchased from outside) are given in the Ph.D. thesis of R. M.
Carlson (87). The same author also explains the stopping syringe
velocity/position transducer (originally designed by F. J. Holler (91)

77

mounted on the stop syringe; this device enables the user to determine the
position of the stopping syringe plunger at any time during the stopped-ﬂow
push.

Figure 3.6 shows a cross-cut view of the stopped-ﬂow mixer and observation
cell. The whole cell assembly, except for the Teﬂon adaptor, is made of a
block of stainless steel and was made by the chemistry department machine
shop of Michigan State University. Same Altex end ﬁtting is used here for
connection. Quartz rods (2 mm) with ground and polished ends (Wilmad
Glass co., Inc.) are sealed to the ends of the observation cell and are kept
tight by the compression of Teﬂon washers, rubber o-ring and aluminum
compression ﬁtting. These quartz rods also play a role as light pipe and
have a tremendous effect on light throughput. The mixer used here is a
Notz-type Kel F mixer (92), which is sealed to the observation cell by

compression from Teﬂon mixer adaptor.

78

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Control Interface

In order to control the overall operation of the rapid scanning stopped-ﬂow
diode array spectrometer a digital interface has been built. The circuit
block diagram of the interface is shown in Figure 3.7. The digital interface
has been designed to provide the following functions:

1. provide a variable integration time for the LDA.

2. provide a variable delay time to allow the user to obtain a
spectrum at different times during the reaction.

3. provide proper signals to run the stopped-ﬂow.

4. provide a triggering signal for the storage scope.

The integration part of the interface consists of four presettable 4-bit
synchronous binary counters which are cascaded to one another to form a
16-bit integration counter; this can be expanded to any number of bits if
desired. The integration time is set by a 16-bit dip switch; once an optimum
integration time is set for a speciﬁc experiment it need not be changed. The
input to the integration counters is a 16 micro second clock pulse from the
Reticon board, and the output from integration counters is the START
pulse. The start pulse is an input to the Reticon and continuously scan the
diode array with a preset integration time. The range of integration times

available is from 4 ms up to about 1 min.

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Another set of three presettable 4-bit down counters are cascaded together to
form a 12-bit presettable delay counter. The delay time is actually a
multiple of the integration time or, in other words, it is the integration time
multiplied by the preset number on the delay counter. Therefore, the delay
counter simply counts the preset number of start pulses and then triggers
the storage scope to record a spectrum. The operation of the delay counter
is controlled by two manual switches. Switch 1 loads the preset delay time
from the dip switches into the delay counters, and clears the stopped-ﬂow
push ﬂip ﬂop. Now as soon as the push button switch 2 is pushed, it
triggers the monostable (MS) which produces a low signal of a few
microsecond duration to the preset input of the stopped-ﬂow push F/F.
Once the push F/F is set, it enables the delay counters, which after counting
the preset number of start pulses, trigger the scope and a spectrum is
acquired. In this way one spectrum per push can be obtained and the
process must be repeated for multiple spectra. In order to synchronize the
time that the scope is triggered and the time that the ﬂow stops for a precise
determination of the time, a counter-time-frequency (CTF) card is used as a
period meter to measure the time between the push and stop

Determination of the System Dead Time

One of the single most important characteristics of a stopped-ﬂow mixing
system is its dead time. The stopped-ﬂow dead time can be defined as the
time between the theoretical start of the reaction and the time at which the
reaction can be monitored, or it could practically be deﬁned as the time for
the solution to ﬂow from the mixer to a point halfway through the

82

observation cell. The procedure employed here to determine the dead time
is known as the extrapolation technique (93). The technique is based on a
determination of the amount of the reaction occurring before the ﬁrst
observation is made. To carry out the experiment a 5 x 10'4 M solution of
Fe(III) in 0.05 M H2804 and a 0.02 M solution of KSCN in 0.05 M H2804
were mixed by the stopped-ﬂow. The spectrum at different times during the
reaction was obtained by the diode array spectrophotometer at 8 ms
integration time and the course of the reaction was followed at 450 nm.
Then the dead time was determined by extrapolating the slope of the
absorbance vs time plot back to the initial absorbance of the reaction
mixture. The dead time is the time between the intersection of the
extrapolated curve with the initial absorbance line and the time of the ﬁrst
observation of the reaction curve. The dead time of the system was

determined to be 7.9 + 1 ms.

Determination of Mixing Time

The mixing time of the stopped-ﬂow system was determined by mixing 0.01
M NaOH with 0.01 M p-nitrophenol solution. The spectrum of this very
rapid reaction was monitored at different integration times with the diode
array spectrophotometer. The oscilloscope was triggered as the plunger of
the stopping syringe came to rest against the stopping block. It was
observed that the minimum integration time of 2.048 ms a constant
spectrum was obtained each time, so it can be stated with certainty that

mixing time was complete in less than 2.048 ms.

83

CHAPTER4

APPLICATION OF THE DIODE -ARRAY STOPPED FLOW SYSTEM TO
THE KINETIC ANALYSES OF BOTH SINGLE AND
MULTICOMPONENT SAMPLES

Introduction

There are a number of signiﬁcant advantages associated with the diode-
array spectrometer for qualitative and quantitative analyses of both single
and multicomponent samples. Ordinarily, due to its relatively low
information content, UV-VIS spectra are rarely used for qualitative
analyses. However, the spectral reproducibility advantage of the diode-
array permits the identiﬁcation of for example two compounds with very
similar spectra. This is very difﬁcult to achieve with conventional
scanning spectrometers. With conventional systems, one can never be sure
whether the small differences in the spectra are real differences or only due
to the instrument reproducibility errors. Therefore, the conﬁdence on
spectral reproducibility offered by the diode-array can certainly extend the
application of UV-VIS spectroscopy to qualitative analyses.

The advantages of the diode-array are even more pronounced when applied
to quantitative analyses of either single or multicomponent systems. In
this regard, one of the major drawbacks of conventional scanning
spectrometers is the wavelength resettability and drift problems which

affects the precision of measurements. These problems become

84

continuously more serious as the mechanical linkages wear. This forces
the analyst to perform all of his/her analyses at the wavelength of
maximum absorption where the wavelength resettability error is at its
minimum. This is despite the fact that the wavelength of maximum
absorption may not be the optimum wavelength for reason of for instance
selectivity. Since there are no moving parts involved in scanning a
spectrum with a diode-array, the above problems are not present in such
systems. The availability of the entire spectrum, virtually free of
wavelength resettability errors, allows the analyst to choose the optimum
wavelength for improved sensitivity, selectivity and dynamic range.
Measurements at any wavelength, peak maxima or on the side of the
absorption band, can be made with essentially the same accuracy and

precision.

The combination of simultaneous multiwavelength detection and the
wavelength resettability advantage offers a number of other beneﬁts to the
analyst for the improvement of sensitivity and dynamic range. For
instance, one can compensate for source fluctuations by performing
simultaneous measurements at the analytical wavelength and a second
wavelength in close proximity of the analytical wavelength where there is
no absorption. This is very important for situations where measurements
are made at low absorption. Notice that this is, in essence, a pseudo dual-
beam operation without the complexity associated with such an optical
design. Conventional scanning systems does not allow for such operations,

since different wavelengths are measured at different times.

85

Another important beneﬁt comes from the fact that stray light is less of a
practical problem with diode—array instruments which leads to an
extension of dynamic range. An extension of the dynamic range at the
upper concentration range may be achieved by measuring strongly
absorbing samples at the side of an absorption band, where absorbance is
lower compared to the band maximum. At low absorption end, the
dynamic range can be stretched further with the diode-array due to higher
light throughput (fewer optical surfaces than conventional instruments),

and lower noise level.

Moreover, a diode-array equipped with a computer and appropriate
software is capable of reducing the noise even further through the use of
two noise reduction techniques known as time and wavelength averaging.
For example, if one desires to obtain a full spectrum over the range of 200 to
1000 nm at 2 nm intervals, one has to make 401 measurements. Assuming
each data point takes one second, a conventional scanning system will
require 401 seconds to collect the full spectrum. The diode-array
instrument on the other hand, measures all data points simultaneously
and, therefore, can generate the full spectrum in one second. In fact, the
diode-array can collect and average several spectra in one second (time
averaging). This will reduce the noise level by a factor of the square-root of
the number of spectra averaged together. Consequently, improved signal-

to-noise and hence sensitivity is achieved.

The second noise reduction technique (wavelength averaging) stems from
the multiwavelength measurement capability of the diode-array. Instead of

quantitative measurements at only one wavelength (e.g., wavelength of

86

maximum absorption), one can measure several data points on either side
of the maximum in the same time and average them together. This will
again result in improved signal-to-noise and in turn sensitivity. However,
for a given absorption bandwidth, there will be a limit as to how many data
points can be averaged to give an optimum signal-to-noise. As more data
points are included, the average absorbance is reduced which has a

negative effect on the signal.

It should be pointed out that it is possible to do both time and wavelength
averaging with a conventional scanning spectrophotometer. However, the
required time is considerably longer which makes it more susceptible to
drift errors. One may also perform wavelength averaging by widening the

slit-width in a conventional system, but then the linearity is seriously

affected.

It can be concluded from the above discussion that the diode-array
spectrophotometer is clearly superior to conventional systems in all
applications where speed and full spectrum acquisition is of major
concern. Undoubtedly, the speed of diode-array data acquisition makes it a
clear choice both for kinetic analyses and for fundamental kinetic studies.
The diode-array spectrometer in combination with the stopped-ﬂow, as a
rapid mixing technique, can further expand the horizon of the instrument

to the study of those reactions which occurs in a very short time scale.

This chapter describes the application of the developed system to the kinetic
analyses of some clinically signiﬁcant species. In fact, many clinically

important processes, in addition to being in a complex media, also occur at

87

a moderately fast rate. Therefore, the diode-array stopped-flow
spectrometer appears to be well suited for the analysis of such samples.
The kinetic determination of total serum protein is studied as an example of
single component sample followed by the simultaneous kinetic analysis of
glucose and alcohol in blood serum as a representative case of

multicomponent system.

88

DETERMINATION OF TOTAL SERUM PROTEIN
BY BIURET NIETHOD

Background

One of the most signiﬁcant clinical test often performed in any clinical
laboratory is the determination of total blood serum protein. The evaluation
of the result of the test is normally used to decide whether further analysis
of subfractions of protein is necessary. Among many methods available for
determination of blood serum protein, the biuret method is one of the most
widely used procedure. This is because the method has been shown (94) to
be least affected by variation in protein composition. The sensitivity of the
method , although not as good as some other ones, but has been evaluated

(94) as sensitive enough.

The principle of the biuret method is based on the fact that all protein
contain a large number of peptide bonds. When a solution of protein is
treated with cupric (Cu+2) ions in a moderately alkaline medium, a colored
chelate complex of unknown composition is formed. The reaction takes
place between the carbonyl ( - C = O ) and imine ( = N - H ) groups of the
peptide bonds of the protein and cupric ions. It turns out, that the organic
compound biuret ( H2N - CO - NH - CO - NH2 ) also undergoes similar type
of process with cupric ions and, therefore, the reaction is referred to as

biuret reaction (Figure 4.1).

89

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90

The reaction appears to take place between the cupric (Cu+2) ions and any
compound containing at least two H2N - CO - , H2N - NH2 - , H2N - CS - ,
and similar groups directly joined together or through a carbon or nitrogen
atom. However, aminoacids and dipeptides do not undergo this reaction,
but tri- and polypeptides and proteins react to produce a pink to reddish

violet color product.

The biuret method was ﬁrst introduced by Kingsley (95) in 1939. Since then,
there have been many modiﬁcations to the method (94,96). In a typical
procedure the blood serum or solution of standard protein are mixed with
biuret reagent and the absorbance is measured after 30 minutes waiting
period for the equilibrium to be established. Certainly, this rather long
waiting period (30 min.) is the main disadvantage of the equilibrium
methods. In an effort to shorten the analysis time , many investigators
have been working toward the applicability of various reaction-rate methods

to the determination of total serum protein by biuret method (94,96).

Hatcher and Anderson (97) were the ﬁrst to describe the determination of
serum total protein with the fast analyzer. However, according to Koch and
coworkers (94) , “ they did not study many samples from patients nor did
they discuss the characteristics and requirements of the kinetic approach”.
Koch, Johnson, and Chilcote (94) employed a fixed time kinetic approach
using the “Centriﬁchem” centrifugal analyzer. Although ﬁxed time kinetic
analysis is limited to those substances undergoing a ﬁrst order (or pseudo-
ﬁrst order) reactions (98-99), they did not ﬁnd biuret reaction to follow a
simple ﬁrst order kinetics. Nevertheless, they found it suitable for ﬁxed

91

time kinetic analysis and described their method as being rapid, accurate,
and reasonably precise. But, Savory and coworkers (96) suggested that the
method was found to be relatively imprecise in their hand and subject to

errors in analyses of sera from patients with dysproteinemias.

In an attempt to develop a more precise and rapid rate method, Wai-Tak-
Law and Crouch (100) examined the biuret method using stopped-ﬂow
mixing system. In agreement with previous investigators, they also found
that the biuret reaction did not follow a ﬁrst order or pseudo-ﬁrst order
kinetics. They, nevertheless, pointed out that the reaction is still suitable
for the two point ﬁxed time reaction rate approach. They, in fact, indicated
that one is able to obtain an analytical curve long before the reaction is over.
They performed experiments both with 0.1 second and 10 seconds time
interval and because of the rapidity of biuret reaction, found that the 10
seconds time interval gave more precise results with standard deviation of

1.4 %.

As a test of our system’s performance, we decided to carry out the analysis
of serum total protein by biuret method using the linear diode-array

stopped-ﬂow spectrometer.

92

Kinetic Determination of Total Protein in Blood Serum
by linear Diode-Array Stopped-Flow System

As pointed out in the previous section, this experiment was chosen to
demonstrate the ease and ﬂexibility with which single component analysis
can be accomplished with the diode-array stopped-ﬂow instrument. There
are several major advantages which becomes immediately apparent to the
analyst as the system is applied to situations such as the one studied here.
The stopped-ﬂow mixing system facilitates the achievements of an
extremely short analysis time. It also demands a very small sample
volume. The rapid acquisition of the entire spectrum with the diode-array,
not only satisﬁes the requirement of the stopped-ﬂow, but also displays the
spectrum instantly for the analyst ‘3 visual inspection. An appropriate
optimum wavelength for the analysis may be selected at a glance. This
relieves the analyst from having to have preknowledge of the analysis
wavelength. Multiple spectra can be acquired by multiple push of the
stopped ﬂow system.

Based on Wai-Tak-Law and Crouch observations, it was decided that two
point ﬁxed time reaction rate method be employed and the At of 8 seconds
was chosen as a good compromise between precision and analysis time. It
should be pointed out, that since the system can acquire data almost
instantaneously after mixing, it could provide for more exact deﬁnition of
blank and, therefore, improvement in precision over other rate methods

using two point ﬁxed time approach.

93

The reagents and solutions used for the experiment were as follows:

Biuret reagent: This reagent contains cupric sulfate (2.4 g L‘1 ) , sodium
potassium tartarate (8 g L‘1 ) , sodium hydroxide (48 g L'1 ) , and potassium
iodide (1.0 g L-1 ).

Standards: The human serum protein standards (Dade Division,
American Hospital Supply Corp., Miami, FL 33152 ; Cat. No. B5158) were
diluted to proper concentrations in saline solution (NaCl, 9 g L‘1 ).

Quality Control Sera: Lyophilized control serum samples ( Moni-trol I, Cat.
No. B5103 ; Moni-trol 11, Cat. No. B5113 ; Patho-trol, Cat. No. B5110 ; Dade
Division, American Hospital Supply Corp., Miami, FL 33152 ) were
reconstituted according to manufacturer’s instructions and diluted to

proper concentrations.

All protein solutions were prepared fresh from vacuum sealed prepackaged
bottles which were stored at 2 - 8 ° C. The biuret reagent was kept in dark
place in plastic bottles.

Figure 4.2 shows the calibration curve for serum protein standards using
the two point ﬁxed time ( At = 8 s ) reaction rate analysis. The 0% T was
recorded with the light source blocked. A spectrum was then recorded
(ﬁrst push) with the diode array integration time set at 8 ms and was
treated as a blank, avoiding the need for running a separate blank. The
integration time was then changed to 8 seconds and a second spectrum was

recorded (second push) . The difference in absorbance (AA) was used in

94

the standard curve. The standards concentration range along with the

statistics of the standard curve is shown in table 4.1.

The statistical calculations were based on the assumption that
measurement precision is determined by the standard deviation(°T) in the
measurement of Transmittance (T) which is constant and independent of
the magnitude of Transmittance. This is due to the fact that a ﬁxed stopped
flow cell was used for all measurements which leads to a negligible
contribution of random error due to cell positioning. Also, the photon
detector shot noise is negligible compared to the somewhat limited
resolution of the readout system (oscilloscope). Under these conditions (%T

noise-limited), the following equation was used (101) for error calculations.

 

Three replicate measurements of each standard solution were overlaid and

the thickness of the scope trace were used as a measure of precision. A

95

Total Protein Calibration Curve

0.8 1

0.7 -

0.6 - ’

0.5 1

Absorbance

0.4 -‘

0.3 '

 

 

0.2 ' I ' I f I 1 I
0 10 20 30 40

Standard protein concentration

 

Figure 4.2 Calibration curve for total serum protein

96

Table 4.1 Results for standard solutions of serum protein (At = 8 8)

 

 

Protein concentration Absorbance RSD (%)
(g/L) ( A average)
10 0.26 1.5
15 0.39 1.4
2) 0.51 1.4
25 0.61 1 5
3) 0.75 1.6

Slope = 2.40 x 10‘2 A g'1 L
Intercept = 2.40 x 10'2 A
Correlation coefﬁcient =0.998

 

97

typical standard deviation of i 0.005 T was found in the measurement of

%T which turns the above equation into:

93 = 0.434 x 0.005
C logT T

which was used for the calculation of relative standard deviations.

The standard curve is linear over the range of standards concentration
with the correlation coefficient of 0.998. The maximum relative standard
deviation obtained was 1.6 %, which is slightly greater than that obtained by
Wai-Tak-Law and Crouch (100).

To experiment with real serum sample, three lyophilized sera ( Moni-trol I,
Moni-trol II, and Patho-trol) were analyzed as controls. Assigned values of
total protein for these sera based on the analyses with well-established

methods are given by the manufacturer.

The results obtained for these sera with diode array stopped-ﬂow system
utilizing ﬁxed-time rate method is presented in table 4.2. The results
reported by three other methods namely improved biuret (equilibrium
method), centriﬁchem (rate method), and the PMT-based stopped-ﬂow
(ﬁxed-time rate method) of Crouch et.al. are also listed for comparison.

98

Table 4.2 Results of determination of Total protein in real human serum

 

Moni-trol I

method 1

method 2

method 3

Moni-trol II

method 1 .

method 2

method 3

Patbo-trol

method 1

method 3

 

 

 

Reported values Measured values

Mean (g/dL) Range (At = 3 8)
7.0 3; 0.06 6.7 - 7.2

6.5 :l: 0.35 5.8 - 7.2 7.0 d: 0.09
6.79 1 0.01

5.3 :t 0.07 5.0 - 5.6

4.5 :t 0.05 4.2-4.8 4.5 i 0.07
5.14 :t 0.04

5.3 3; 0.12 5.0-5.6

5.85 1 0.08 5.31 :l: 0.08

 

Method 1 : Improved biuret (equilibrium method)
Method 2 : Centriﬁchem (rate method)
Method 3 : PMT-based stopped flow (ﬁxed-time rate method)

99

The data in table 4.2 shows that the results obtained for the analysis of
Moni-trol I and Patho-trol are in good agreement with those of both the
improved biuret and the PMT-based stopped-ﬂow methods, while the
results for Moni-trol II is more closer to that reported by centriﬁchem
method. Nevertheless, the results for all three samples are well within the

acceptable range given by the manufacturer.

Based on the overall results of the experiment, it can be concluded that not
only it is possible to carry out single component reaction rate analysis with
the LDA-stopped ﬂow system, but one can perform such analyses with far
greater speed, ﬂexibility, and convenience compared to instruments with
single channel detection. However, the real power of the LDA-stopped ﬂow
spectrometer can be realized when applied to multicomponent systems.

This is demonstrated in the next section.

100

SllVIULTANEOUS KINETIC DETERMINATION OF
GLUCOSE AND ETHANOL IN BLOOD SERUM

Introduction

Optical spectroscopy in the UV-VIS region accounts for a large portion of
all analyses performed in a modern clinical laboratory. However, most
spectrophotometers used in clinical laboratories are based on a design
concept in which a single resolution element is isolated by a ﬁlter or some
type of a dispersion optics and slit combination, and its intensity is
subsequently monitored by a photoelectric detector. With such
instruments, time consuming separation is- usually required before the
measurement step to separate the analyte from other species that absorb in
the analytical bandpass. Because most biological samples are complex
matrices, it would be advantageous in many situations to determine
multiple components without a complete separation step. This, in
principle, requires the ability . to monitor multiple wavelengths
simultaneously. The use of conventional rapid scanning instruments that
require several minutes for the acquisition of each complete spectrum, can

only offer a slight advantage over todays fast separation techniques.

Optoelectronic image devices such as diode array, on the other hand, are
capable of collecting the entire spectrum in millisecond time scale and with
repetition rates approaching 1000 spectra per second. This kind of speed is,
by no means, matched by any of the existing separation technologies. The

101

extremely short time required for a complete scan with the diode array, is
well below the time scale for most clinically important reactions to reach
equilibrium. This makes the diode array spectrometer well suited for
multicomponent kinetic analyses in a clinical laboratories. A brief history
of the application of kinetic methods in clinical chemistry may be

appropriate at this point.

Clinical chemists have long been interested in kinetic methods of analysis.
The main reason for the popularity of kinetic methods among clinical
chemists is the extensive application of enzymes in the diagnosis of
diseases. In fact, enzyme activity determinations constitutes a major
workload of a typical clinical laboratory. The main advantage of enzymes
as analytical reagents is their speciﬁcity in catalyzing many complex
chemical reactions ocurring in biological media. Since enzymes affects the
kinetics of reactions, reaction rate methods have been used for a long time
for the determination of substrates, activators, inhibitors, and also of

enzyme themselves.

However, prior to 1960’s the use of enzymes for analytical purposes has
been rather limited. This has been mainly due to some disadvantages
attributed to the use of enzymes such as unavailability, instability, poor
precision, considerable time and labor required for the analyses. While
these objections were quite valid earlier, numerous enzymes are now
available in puriﬁed form, with high speciﬁc activity, at reasonable prices.
With recognition of the enzyme instability and adoption of appropriate
precautions, this potential problem can be minimized. The burden of using

large amounts of expensive enzymes for routine analytical purposes has

102

also been somewhat alleviated by the development of immobolized enzyme
techniques which allows continuous use of the enzyme for a relatively long

period of time.

The question of poor precision, time and labor demand that have in the past
made enzymatic kinetic methods of analysis undesirable, have been more a
consequence of the experimental techniques and instrumentation than the
inherent fault of enzymes or kinetic approach. Techniques and
instrumentation originally developed for equilibrium methods were being
used for kinetic analysis. Kinetic methods are inherently time dependent
and highly sensitive to experimental variables. Equilibrium based
instruments are not essentially equipped with means of accurate time
measurements, temperature control, or adequate detector sensitivity or

stability for monitoring small signal changes in kinetic analysis.

During the 1960’s, several investigators (102-104) began to address the issue
of designing appropriate instrumentation explicitly for kinetic methods. As
pointed out by H. L. Pardue (105) : “the turning point for kinetic approach
as a viable tool for high quality, fast, routine analyses occurred during the
1960’s when a few workers focused a concentrated effort on the development
of instrumentation and techniques designed explicitly for kinetic
applications”. During this same period and throughout 1970’s, many other
developments such .as rapid mixing techniques, temperature control
reaction cells, various analytical kinetic methodologies, and ﬁnally
automation of most stages of measurement process such as sample
introduction, data acquisition and processing has contributed

tremendously to the maturity and acceptance of kinetic methods of

103

analysis. It was clearly demonstrated during this period that rate methods
of analysis could be performed with speed, reliability and simplicity
compared to that of most common equilibrium methods. These
developments have been the subject of several texts (106-107) and reviews
(108112).

Until recently, however, the most frequent application of rate methods of
analysis in clinical laboratories has been through the utilization of
catalyzed reactions for the determination of single species using
photometers or spectrOphotometers with single channel detection. The
introduction of multichannel spectrometers and their applications to a few
clinical problems (113-115) in the mid to late 1970’s, provided a whole new
opportunities to the modern clinical chemist. When applied to clinical
chemistry an array detector capable of monitoring hundreds of resolution
elements simultaneously can offer several signiﬁcant advantages. In
addition to handling single channel equilibrium and kinetic measurements
often performed with conventional systems, these instruments can also
provide fast repetitive scans of optical spectra, with little or no extra effort
on the part of the operator. these instruments allows for rapid and simple
means of equilibrium and kinetic multicomponent analysis of complex
biological samples as well as fundamental kinetic studies of clinically
signiﬁcant reactions. A properly designed multichannel spectrometer for
clinical applications can, in effect, replace an otherwise several other
instruments and, therefore, bring about a tremendous saving in the cost of

equipment and personnel training.

104

Despite the obvious beneﬁts of these devices, however, clinical chemists
have been very slow in taking full advantage of these new developments and
the application of array detectors for multicomponent clinical analysis have
been quite limited. In the next section, the application of the diode array
stopped-ﬂow system to simultaneous analysis of glucose and ethanol in

blood serum using enzyme catalyzed reactions is described.

Simultaneous Kinetic determinations of
Glucose and Ethanol in Blood Serum

The primary objective in this work was to demonstrate the capabilities of
the diode array stopped-ﬂow system for multicomponent analysis using
enzyme-catalyzed reaction rate method. Therefore, the determination of
glucose and ethyl alcohol in blood serum was undertaken because
established reaction rate procedures are available for each species. Some
modiﬁcations to these procedures were necessary to allow for simultaneous

determination of both glucose and alcohol in a blood serum sample.

The determination of both glucose and ethanol in blood serum are
procedures most frequently performed in hospital chemistry laboratories.
The glucose determination is often done as an aid in the diagnosis and
treatment of diabetes. Early detection of diabetes allows for delay or
minimization of the complications of the disease. The blood glucose level
can fall below normal as a result of either overproduction or excess
administration of insulin. In sever cases, the resulting extreme

hypoglycemia causes muscular spasm and loss of consciousness known as

105

insulin shock. Therefore, it is important for a clinical laboratory to use
methods which can furnish the results of glucose analysis rapidly and with
high degree of reliability.

Enzymatic methOds have been used very frequently for the speciﬁc
determination of glucose in biological samples. One of the common
procedure involves the oxidation of glucose with glucose oxidase, forming
hydrogen peroxide, which in turn oxidizes a chromogenic oxygen acceptor
such as o-dianisidine in presence of horse raddish peroxidase to form a
colored product proportional to the glucose concentration (116-118). The
measurement of absorbance is made after a long time waiting period

required for the reaction to reach equilibrium.

Based upon the above enzymatic reaction, Malmstadt and Hicks (119)
described a rate method which gives quantitative data within the ﬁrst few
minutes of the reaction by an automated spectrophotometeric system. This
method avoids the waiting period for the establishment of equilibrium and
greatly reduces the total analysis time. A potentiometric kinetic method for
the analysis of aqueous glucose samples in which the hydrogen peroxide
formed in the oxidation of glucose reacts with excess iodide, in the presence
of molybdate catalyst, to form an equivalent amount of iodine has also been
described by Malmstadt and Pardue (120). This method is rapid, precise,
sensitive, and has the advantage of substituting molybdate catalyst in place
of the expensive peroxidase enzyme. The above same chemical system has
been employed by Malmstadt and Hadjiioannou (1 21) except that the rate of
iodine formation been monitored by an automatic spectrophotometric

system measuring the time required for a ﬁxed absorbance change. They

106

obtained within 2 % precision for measurements on samples containing

0.08 mL of serum, plasma, or blood.

Most of the interest in the determination of ethanol in blood arises from the
fact that it is the most frequent toxic substance encountered in medico-legal
cases. Not only it is lethal by itself, but it is most often a signiﬁcant
contributor to accidents of all types. Every time that a patient is brought to a
hospital in coma, a differential diagnosis of the cause of coma is performed
in which the effect of alcohol must be ﬁrst ruled out. This, of course,
demands for a rapid and precise method of alcohol determination in
biological ﬂuids. As a result, more analytical methods have been developed

for the determination of alcohol than for any other toxic substances.

Many of the chemical methods are based on the measurement of the
oxidant required for the stoichiometric oxidation of ethanol. The most
commonly used oxidant is dichromate in sufuric acid (122), but
permanganate, vanadium pentoxide, and osmic acid have also been used
(123). The major problem of the oxidation methods is their lack of speciﬁcity
and, therefore, the necessity to eliminate materials such as acids, bases,
aldehydes, ketones and alcohols prior to oxidation using various time

consuming separation techniques (124).

Enzymatic methods (125-126), on the other hand, allows for high speciﬁcity
without separation. The usual enzymatic methods are based on the use of
the enzyme alcohol dehydrogenase (ADH) to catalyze the reaction between
ethanol and diphosphopyridine nucleotide (DPN). A typical 60 - 90 minutes
waiting period is required before the absorbance of DPNH (reduced form of

107

DPN) in measured at the wavelength of 340 nm. In an attempt to develop a
faster method, Malmstadt and Hadjiioannou (127) applied the rapid
spectrophotometric reaction rate method previously developed for the
determination of glucose (128) to the determination of ethanol in blood.
They measured the time required for the ﬁxed absorbance change and were
able to determine blood alcohol concentrations in the range of 0.015 to 0.3

with relative errors of about 2 to 3 %.

To demonstrate the capability of the diode array- stopped ﬂow system for
multicomponent kinetic analyses, the simultaneous determination of
glucose and ethanol in blood serum using enzyme catalyzed reactions was
carried out. The reaction chosen for glucose involves the oxidation of
glucose by glucose oxidase to form hydrogen peroxide. The hydrogen
peroxide then reacts with o-dianisidine in presence of peroxidase to produce
the oxidized form of o-dianisidine which absorbs at 540 nm. The procedure
for ethanol was essentially based on the method used by Malmstadt and
Hadjiioannou (127) which involves the use 'of' enzyme alcohol
dehydrogenase. As part of this reaction, DPN+ is converted to DPNH which
absorbs light at 350 nm. These two reactions are shown in ﬁgure 4.2.

108

GLUCOSE

Glucose oxidase

 

 

 

Glucose + 02 + H20 4- Gluconic acid + H202
peroxidase
o-dianisidine + H202 4 Oxidized + H20
o-dianisidine
(color)
A m s 540 nm
ETHANOL
ADH
CH3CH2OH + DPN+ 4' CHgCHO + DPNH + H+
(color)

it =350nm

 

Figure 4.3 Enzymatic reactions of glucose and ethanol

109

The reagents used are as follows:

1. Deproteinization reagents
a) Barium hydroxide (Ba(OH)2 . 8 H2O), 1.8 % solution in water.
This solution was let stand for two days, then decanted and stored
in a bottle.

b) Zinc sulfate (ZnSO4 . 7H2O), 2 % solution in water. This solution

was balanced against barium hydroxide so that 5.00 mL of barium
hydroxide is equivalent to 5.00 mL of zinc sulfate solution.

2. Buﬂ’er solution, pH 6.8
The KH2PO4/K2HPO4 conjugate pair was used to which 4.0 g of
semicarbazide hydrochloride was added and the PH was adjusted
to 6.8 using a pH-meter. This solution is stable indeﬁnitely.

3. Glucose oxidase
The enzyme packaged mixture (Worthington Biochemical Corp.,
Freehold, NJ. 07728) was used. A small package contains 10 mg of
o-dianisidine and a larger package contains about 125 mg of glucose
oxidase and 5 mg of peroxidase. The soluble o-dianisidine was
completely dissolved in 1.0 mL of distilled water and drained into an
amber bottle. The glucose oxidase and peroxidase mixture was
dissolved in, and diluted to 100 mL with the buffer solution. This
solution was added to the amber bottle and stored in the refrigerator.

The solution is stable for about a month.

110

4. Alcohol dehydrogenase (ADH
Yeast ADH (from Worthington Biochemical Corp) was dissolved in
the buffer solution, just before use and kept in ice bath during use.

5. Coenzyme DPN (diphosphopyridine nucleotide)
This solution was prepared just before use by dissolving 0.33 mg of
DPN from Sigma Chemical 00. per one mL of the buffer solution and

kept refrigerated when not in use.

6. Glucose standards
Stock standard glucose solution of 1 .0 g/100 mL was made by
dissolving 1.000 of dry reagent grade glucose in benzoic acid (2 g/L)
and diluting to 100 mL with benzoic acid. This solution is stable
indeﬁnitely if kept refrigerated when not in use. Working standards

were made from this solution with proper dilutions.

7. Ethanol standards

A 300 ppm stock ethanol solution was made from anhydrous ethanol.
Working standards were prepared from this solution with proper

dilutions.

Using the photodiode array stopped-ﬂow system, two separate calibration
curves were initially established for glucose and ethanol employing the
ﬁxed-time (At = 8 8) reaction rate approach similar to that explained in the
analysis of total protein. This was done by preparing a composite solution
composed of both enzymes (glucose oxidase and ADH), coenzyme DPN in

111

the buﬂ‘er solution. Prior to the analysis, all solutions were kept separate
because the composite solution of ADH and DPN is unstable at room
temperature. The ADH was kept in an ice bath during use. The buffer
solution was placed in a 37 00 water bath and the other solutions were
separately added and allowed to warm up for a few minutes just before the
analysis. The composite solution was adjusted to contain 1.25 mg/mL
glucose oxidase and 1.5 mg/mL ADH. These values are reported as
appropriate values (127-128) to provide satisfactory enzyme activity for the

range of concentrations used for the standard solutions in this experiment.

The second solution consisted of either glucose or ethanol standards in
buffer solution for preparation of their respective calibration curve. This
solution was also allowed to warm up in the 37 00 water bath prior to the
analysis. Under the above selected conditions (substrate and enzyme
concentrations), both reactions follow ﬁrst order kinetics and the
absorbance change (AA) measured near the start of the reactions is
proportional to initial concentration of the substrates. Also, the materials
present in glucose oxidase, ADH and DPN which may absorb light at
analyses wavelengths do not pose a serious problem since absolute
absorbances are not measured. The possibility of side reactions is also very
remote as the measurements are completed during the early part of the

reactions.

However, in addition to enzymes and substrates concentrations the
measured absorbance change is also affected by experimental variables
such as pH and temperature. As was described earlier, every efforts were

made to minimize the effect of temperature variations, but some degree of

112

temperature ﬂuctuation will still be present since the system was not
thermostated. The question of pH is a more difficult one since the
procedure used for glucose is normally carried out at neutral to slightly
acidic pH (~6.8 - 7.0), while that of ethanol is usually done at moderately
alkaline pH (~8.7). This is because the second step in the glucose procedure
(involving peroxidaes) is less specific and at pH values above neutral,
various substances such as uric acid, ascorbic acid, and bilirubin compete
with o-dianisidine for hydrogen peroxide (129). In the case of ethanol, a
very small fraction of alcohol (1-10%) is consumed (127) during the
measurement period and buffering at high pH values can drive the reaction

more to the right.

The buffer solution used in this experiment (pH = 6.8) was in fact optimized
for glucose, while the addition of semicarbazide hydrochloride to the buffer
solution was intended to drive the ethanol reaction to the right by reacting
with aldehyde as it is formed. Figure 4.2 and 4.3 show the calibration
curves for glucose and ethanol respectively, and table 4.2 and 4.3 list their

corresponding standards concentration range and statistics.

As can be seen, both calibration curves which are obtained by plotting AA
against the standards concentrations are quite linear. Neither of which,
however pass through zero because of the certain amount of glucose or

alcohol which is consumed before the measurements are completed.

1.0-

 

 

 

 

0.8 -
8
5 .6.

0.4 -

0-2 I I I ' I I I I i

o 20 4o 60 so 100
Glucose concentration (mg/IOOmL)
Figure 4.4 Calibration curve for glucose

114

Table 4.3 Results for standard solutions of glucose (At = 8 s)

 

 

Glucose concentration Absorbance RSD (%)
(mg/100 mL) (AA, average)

10 0.24 1.5
2) 0.30 1.5
3) 0.48 1 4
'40 0.63 1.5
50 0.69 1.6
60 0.82 1.6
8) 0.94 1.7

Slope = 1.06 x 10-2 A g'1 L

Intercept = 0.145 A

Correlation Coeff. = 0.971

 

115

IUhsmdmmnce

0.6 -

0.5 -'

0.4 '-

0.3 '1

0.2 -

 

 

0.1

o I 13 20 so 40
IMqumdlcommmmﬂratknaCnquOOrnIJ

 

Figure 4.5 Calibration curve for ethanol

116

Table 4.4 Results for standard solutions of ethanol (At = 8 s)

 

 

Ethanol concentration Absorbance RSD (%)
(mg/100 mL) (AA, average)

5 0.16 1.6

10 0.22 1.6

15 0.29 1.5

2) 0.36 1.4

25 0.42 1.4

3) 0.50 1.4
Slope = 1.35 x 10'2 A g’1 L
Intercept = 8.80 X 10'2 A

Correlation coeff. = 0.999

 

117

 

To carry out the simultaneous determination of both glucose and ethanol in
real blood serum, a sample of human blood serum was acquired from
Sparrow Hospital in Lansing, Michigan. The sample was a pooled blood
serum over a large human population. .Therefore, its glucose content was
expected to be in the average of the normal range. As for ethanol, normal
blood serums are free from alcohol and a known amount of ethanol had to
be added to the serum. Thus, after deproteinization of the blood serum
sample with barium hydroxide and zinc sulfate solutions, it was diluted

with ethanol solution to contain 60 mg of ethanol per 100 mL of blood serum.

Three replicate measurements (At = 8 s) of the blood serum sample were
made (by a procedure similar to those of the standards) and the
concentrations of glucose and ethanol were determined from the calibration
curves. The average values determined for the concentrations of glucose
and ethanol were 91 mg/100 mL and 51 mg/100 mL of blood serum,
respectively. The relative standard deviations of the measurements were
on the order of 6.8%. The normal value for glucose concentration in blood
serum is in the range of 70 - 105 mg/100 mL (129) and, as expected, the value

determined for glucose is well within this range.

However, the results of ethanol analysis is more than 15% at error. This
may be explained by considering several experimental factors which were
not favorable for ethanol determination. One factor which was previously
mentioned is that of low pH (6.8) which can further reduce an already small
fraction of ethanol which reacts during the measurement time. Secondly,
the light source which was used (quartz halogen) has a very low photon
ﬂux at wavelengths below 400 nm. Both of these factors can lead to low SIN

118

 

ratio for ethanol. Small amount of ethanol could have also been lost by
evaporation during the measurement period, particularly during the warm
up period at 37 ° C. All of these factors could have contributed to the low
value obtained for ethanol.

Also note that the measurement precision (RSD of 6.8%) for simultaneous
determination of glucose and ethanol is only fair. Obviously, it is possible to
improve the measurement precision by conducting a more vigorous and
careful studies for optimization of the experimental variables such as pH ,
temperature, enzymes and coenzyme concentrations. However, since the
primary objective of this work was to demonstrate the potential utility of the
proposed system to multicomponent kinetic analyses, such extensive

optimization studies were not carried out.

The overall results in this chapter indicate that the photodiode array
stopped-ﬂow spectrometer is certainly a powerful technique for clinical
applications. The system allows for both single and multicomponent
analyses to be performed with great ease, ﬂexibility, and convenience using
either equilibrium or kinetic methods. In most applications, the system
provides for an essentially overdetermined analyses and, therefore, offers a
greater degree of ﬂexibility and freedom to the clinical chemist for utilizing
the information of his/her interest. Undoubtedly, for modern clinical
laboratories involved in many complex chemical analyses, a photodiode

array stopped-ﬂow spectrometer is clearly a viable choice.

119

 

10.

11.

12.

REFERENCES

 

Mars, J .M., paper NO. 48, Pittsburgh Conference on Analytical
Chemistry and Applied Spectroscopy, Cleveland, Ohio, 1972.

Nunn, W.G.; Dessey, R.E. Multichannel Imaging Detectors, ACS
Symposium Series.

Massey, V.; and Gibson, G.H., Fed. proc., 1964, 23, 18.

Carr, J .D.; Libby, RA; and Margeram, D.W. Inorg. Chem., 1967, 6,
1083.

Strojek, J .W.; Gruver, G.A.; and Kuwana, T. Anal. Chem., 1969, 41,
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129

PART II

THE DEVELOPMENT OF A NEW CHEMOMETRIC
APPROACH FOR CONIPUTERIZED COMPOUND
IDENTIFICATION USING IR SPECTRAL DATA

INTRODUCTION

Computer compound identiﬁcation was one of the ﬁrst data handling
applications to appear after the computers were interfaced to spectroscopic
instruments. Ever since, the bond established between analytical
chemistry and computers has become continuously stronger. In recent
years, the integration of computers with just about all modern
spectroscopic instruments, has created a new enthusiasm for research
activities in the ﬁeld of automated compound identiﬁcation. Beyond the
signiﬁcant effect of these efforts on the work of experts, it has made an
impact of far greater dimension on the way that chemical analysis is

performed by users who are not specialists.

Clearly, chemistry continues to play an increasingly important role in
today's modern life. For members of every society , almost all aspects of life
such as health, food, environment, consumer products, etc. are more and
more inﬂuenced by chemistry or, as perceived by the general public by
"chemicals". This inﬂuence is not limited to the material aspects of life,
but also effects the formulation of much legislation and many public
policies which govern the society. In many situations, accurate
identiﬁcation and thereby undrestanding the nature and properties of these
“chemicals” is of key importance. This is a complicated problem when
considering the fact that the vast majority of users of such “chemicals” at
best possess minimal knowledge about their characteristics and behavior.

This, of course, create a large demand for qualiﬁed personnel which is

near impossible to achieve considering the extent and the magnitude of the

issue.

A better understanding of the technical problem involved is achieved by
drawing an analogy with that of a chemical equation which is too far from
equilibrium. One side of the equation consist of a small chemical society
(expert domain) with a great deal of knowledge about these so called
"chemicals", and the other side includes a large body of various users with
little or no knowledge (user or public domain). To bring a sensible
equilibrium in this equation, there is a deﬁnite need for transfer of
knowledge and expertise from the former domain to the latter. Although
this interdomain information transfer appears to be a thermodynamically
favorable process (large information gap) , until recently it has been
kinetically very unfavorable. This is due to the the fact that positioning a
qualiﬁed human expert at every conceivable user interface becomes rapidly
prohibitive both from resources (including human experts) and economical
perspectives. The only logical alternative appears to be the utilization of
some kind of a "magic catalyst" which can facilitate the transfer of
knowledge with high speed, accuracy and efﬁciency. In reality, the goal is
not to transfer the kowledge in advance and make experts of the public as a
whole,but rather one only need to facilitate access to information that is
accurate and relevant. Computers has truly played an indispensable role
as such a magic catalyst through various scientiﬁc databases and expert

systems.

It should be noted‘that the scope of the above rather abstract discussion is
not limited to chemistry only and is certainly applicable to any user/expert

2

domain interaction within any scientiﬁc discipline. Analytical chemists,
however, have long been concerned with the characterization and
identiﬁcation of various compounds. In this process, they rely on a vast
body of chemical knowledge and expertise in interpreting a great variety of
characteristic spectroscopic information about numerous compounds.
Certainly, these kinds of human experts do not, by any means, come easy or
free of cost. High levels of training along with many years of experience are
in fact invested into the production of these qualiﬁed individuals. It is
within this context that many efforts have been focused at reducing the
demands for human experts by their possible replacement with
computerized expert systems. Speciﬁcally, the ﬁeld of automated
compound identiﬁcation, perhaps more than any other area, has employed
this role of computers as interdomain information mediators. Today, with
the high-throughput computerized instrumentation and the scarcity of
human experts, the development of saphisticated procedures for
computerized processing of spectroscopic information must and has

become a high priority.

One should be cautioned, however, that to this date automated compound
identiﬁcation systems are still far from being perfect and the computers
have not, quite yet, turned into a spectroscopist in a box. They have at best
become a valuable tool for the experienced chemist and a marginally useful
tool in the hands of inexperienced users. There are still many problems
with spectroscopy itself as well as instrumentation, operators and
sampling errors. Great deal of work needs to be done toward
standardization of sampling and spectral data acquisition parameters,

software utilities and computers and networking protocols. Nevertheless,

3

we are not far from the time when a universal automated system across all
spectrosc0pic disciplines will become a reality. This will not only free the
experts from performing many routine and repetitive tasks but, when
combined with today’s communication technology, will greatly enhance
collaboration among world experts to investigate issues of global dimension
such as health and environmental matters. The network approach will
not, of course, help a user with one or two instruments which have
dedicated data systems. Therefore, localized automated systems with a
greater degree of intelligence in performance than those currently available

still need to be developed.

It is hoped that the brief discussion above can serve to generate some ‘
excitement on the part of the reader in regard to the area of automated
compound identiﬁcation. It also will hopefully make him/her appreciate
the impact of the research activities in the ﬁeld which may be of far greater
dimension than the mere curiosity of individual researchers. This chapter
begins with a a brief conceptual overview of automated compound
identiﬁcation pertinent mostly to IR spectroscopy. Subsequently, a new
chemometric approach for qualitative and quantitative infrared
spectroscopic analysis developed in this research is described. Finally, the
application of the method to the analysis of pure samples and mixtures and

to the resolution of unseparated chromatographic peaks is demonstrated.

AUTOMATED SPECTRAL PROCESSING
A CONCEPTUAL OVREVIEW

While chemists study the characteristics, properties and structure of
compounds based on ﬁrst principles as well as various spectroscopic probes
which may directly or indirectly reveal exploratory information, these
compounds ﬁnd numerous applications in the hands of users who are not
expert chemists. Therefore, the development of automated compound
identiﬁcation systems intended for users who are not expert spectroscopists

has become increasingly important.

The determination of the structure and identity of organic compounds is
typically done by spectroscopic methods such as infrared spectroscopy (IR) .
mass spectrometry (MS) , nuclear magnetic resonance (N.M.R) and
ultraviolet-visible (UV-VIS) spectroscopy. Among these, mass
spectrometry and infrared spectroscopy occupy the ﬁrst and second place
respectively, in terms of automated tools already developed for their spectral
processing. Although there exists a variety of computerized spectral
processing systems, these may all be broadly classiﬁed into two major
categories. Those based on spectral matching (library search) and those
relying on artiﬁcial intelligent programs (interpretive methods) often
referred to as "expert" or "knowledge based” systems. Regardless of the
approach used, the goal is to ultimately determine the identity of an

unknown compound based on various spectroscopic information.

In order to develop an appreciation of the basic mechanism operating in
automated spectral processing, a brief discussion of an abstract
mathematical model proposed by J. T. Clerc (1,2) is presented at this point.
The reader may refer to the original references for a more detailed
treatment. The Clerc's model also serves to make a clear distinction
between the two categories of automated spectral processing mentioned

above.

The underlying assumption of all approaches to spectra interpretation is
the fact that the spectrum of a compound is a function of its structure. This
ﬁmctional relationship may be depicted in the following form:

Spectrum = F (Structure) (1)

To obtain an unknown structure, one only needs to invert the function F
and apply it to the spectrum:

Structure = F '1 (Spectrum) (2)

At the ﬁrst glance, the operations suggested by equations (1) and (2) may
appear to be very simple. However, the theoretical evaluation of the
function F , i.e, the exact calculation of, for example, all IR frequencies
absorbed by a given molecule from basic principles, is not possible except for
very small or highly symmetrical molecules. The majority of compounds
are structurally too complex to be treated theoretically which leaves the
empirical data treatment as the only practical choice. This empirical
treatment of spectral data is, in fact, a task performed everyday by

6

practicing spectroscopists which may very well provide an insight for
approaching the problem of spectra interpretation. The way that a
spectroscopist performs structure elucidation by spectral interpretation is
by breaking the total function F into a set of smaller and simpler functions

Gi relating subspectra to substructures. The criteria for selection of Gi

functions is that they must be relatively simple and invertible:

Subspectrumi = Gi (Substructure) (3)

and,

Substructure = Gi'l (Subspectrum) (4)

The total structure may then be recovered by combining various

substructures:

Total Structure = z (substructures)i (5)
i

The utilization of the scheme suggested by equations (3), (4), and (5) does
not, however, lead to a unique total structure. This stems ﬁ'om the fact that
the simpliﬁcation offered by considering only partial Gi functions instead of
the total F function comes at the expense of losing context information. The
inﬂuence of structural surrounding (neighboring groups) on a given
substructure are no longer considered which can result in Gi'l functions
being fuzzy. Consequently several substructures are often found to be

compatible with a given subspectrum or vice versa.

The trend in alleviating this problem has been through the simultaneous
application of various sets of Gi'l functions obtained with the orchestrated

use of several spectroscopic techniques (3,4). This strategy is well founded
when one considers a spectrum of a compound as the projection of the
compound from structure space into spectrum space. Since some loss of
information is accompanied with any projection, ambiguous results can be
obtained when the reverse projection is performed. Just as the shape of an
object can be better deduced by combining different projections, the
structure of a molecule can also be determined with greater certainty when
spectral information from different spectroscopic techniques (i.e. different

projections) exhibiting different potentialities and limitations are combined.

Thus, the combined use of, for example, IR and MS spectral data for a
given compound is far more powerful than the single use of an extremely
precise and highly resolved spectrum of either. Additionally, other
parameters such as source of the sample and its physical properties may
also be incorporated into such schemes as constraints to further focus the
ﬂow of information toward a more strictly deﬁned possible structures, i.e.,
achieving selectivity enhancement among an otherwise informationally

homolog candidates.

The main steps for a general algorithm for spectra interpretation proposed
by Clerc is listed in table 5.1. These ﬁve steps are followed in almost all
automated spectra interpretation systems (4-9) differing mainly in the
emphasis placed on each step and the degree of sophistication used for their

implementation.

TABLE 5.1 : General algorithm for spectra interpretation

 

L partial structures

Apply the Gi'l functions to compile a list ofstructures possibly present
in the compound at hand.

consistent sets

From the list compiled in step 1 select all sets of structural elements
possibly present simultaneously in the compound at hand which are
self-consistent and not at variance with nonspectroscopic evidence.

Structure assembly

From all the sets generated in step 2 generate all possible full structures
that are in accordance with the rules of chemistry and not at variance
with nonspectroscopic evidence.

Spectraprediction

Predict the spectral data for the next tentative structure from the list of
step 3 , using either of the F function or approximate G functions, taking
into account the structural context.

Spectra comparison.

Compare the predicted spectrum with the experimental spectrum. If the
two spectra are closely similar, keep the tentative structure as a possible
solution. Then repeat step 4 and 5 until the list from step 3 is exhausted.
Then stop. The list generated in step 5 now contains all possible
solutions to the problem.

 

From reference (1).

The major consideration in step 1 is the selection of a complete set of G1'1
functions with an appropriate degree of fuzziness. Different complications
may arise if the range is made either too narrow or too wide. With a
narrow range, one can miss a substructural element in a complex
environment; and with a wide range, one may end up with too many
solutions which can drive the processes in step 2 and step 3 out of hand.
Steps 2 and 3 are not basically chemical problems, although they demand a
very involved and certainly nontrivial programing effort. Step 4 is
essentially an information recovery step. This means that the context
information lost in step 1 by considering isolated substructures is to be

recovered for the prediction of total spectra. In order for this step to be
useful and lead to a reduction of the list in step 3, the Gi functions used here

must be far more precise than the Gi'l functions used in inference step. It
is possible to achieve better precision by implementing more sophisticated
rules in this step than the inference rules used in the ﬁrst step. The
accuracy of step 4 is also very important. Erroneous prediction here can
cause the acceptable solution to be missed in the last step. Finally, the last
step consists of a similarity check. All the instrumental parameters
(reproducibility, spectrum registration, etc.) as well as the accuracy and
precision used in step 4 must be taken into account for an accurate

deﬁnition of similarity.

As mentioned previously, the general algorithm outlined in the above ﬁve
steps closely resembles the approach followed by human spectroscopists.
With a ﬁnite level of training and experience, the human analyst makes

many logical decisions on an everyday basis and also learns from his/her

10

experiences. For this fundamental reason, the interpretive methods based
on rule programing and full utilization of the above general algorithm have
received a great deal of attention in recent years. To this end, one of the
most notable example of interpretive software for use with infrared spectra
is the "Program for Analysis of Infrared Spectra" (PAIRS) developed by
Woodruff and Smith in 1980 (9-13) .

This program originally designed for liquid phase spectra, has been later
modiﬁed to also accommodate vapor phase spectra (14) . The software
consists of two Fortran programs, a rule compiler, an interpreter, and a set
of interpretation rules written in an English-like language called
CONCISE. These concise rules are converted into integer strings by the
rule compiler and stored for later use by the system for interpretation of the
unknown spectra. An important feature of the PAIRS program which is
responsible for much of its success is that the rules and the interpreter are
kept separate as opposed to being integrated in one program. This enables
the user to change rules or teach PAIRS new rules without being an expert
computer programmer. One version of PAIRS is available to run on IBM
systems, and a most up-to-date version exists for DEC VAX systems. Some
version of PAIRS also exist on Nicolet (10,15) and Digilab (16) and IBM (17)

FT-IR instruments.

Since the interpretive methods are not the focus of this research, they will
not be further discussed here. However, the reader may refer to several
recent reviews on the general subject (18) and as applied to infrared (19) and
mass spectrometry (20) .

Although research in the development of various interpretive algorithms in
recent years has led to many useful concepts for spectral interpretation
with computers, they have not yet found widespread application in the
chemical laboratory. The library search, on the other hand, has become a
mature technique and is routinely used for compound identiﬁcation and

structure elucidation.

In library search systems, the spectra are not interpreted at all; and
therefore, no interpretation rules are used in the process. Theses systems,
as the name suggests, rely on a large library of reference spectra of known
compounds. The unknown spectrum is then compared to all library
members and a list of best hits from those references exhibiting closely
similar spectra to that of the unknown is presented to the user. Whether
any of the compounds in the hit list are exactly identical or only bear some
structural similarity to the unknown is something that must be entirely

determined by the user.

Although a variety of matrices are used for spectral matching, the
mechanism operative in a library search can also be understood in terms of
the ﬁve steps outlined in Clerc's general algorithm. Since library search
systems make no attempt at interpreting the unknown spectrum, step 1
(partial structure) is skipped entirely. Consequently, the selection of
consistent sets of partial structures (step 2) is no longer applicable. It
should be emphasized, however, that these two steps which essentially
utilizes spectroscopically relevant information are perhaps the most
important steps in interpretive methods and are responsible for making
them distinctly different from search systems. In step 3 (structure

' 12

assembly) all of the library members are considered as possible structures;
and since the library contains the real spectra of all references, Step 4

(spectrum prediction) becomes a straightforward retrieval operation.

The only major step left in the library search is that of spectra comparison
(step 5). Since all library entries are compared to the unknown spectrum,
the accuracy of the search results is critically dependent upon the quality of
similarity measure employed. In this step, a bad performance by the
similarity ﬁlter will produce a hit list dominated by noise structures with
no relation to the unknown. For successful results, the library search also
relies on high quality reeference spectra, uncontaminated samples, and the

presence of unknown in the data base.

For interpretive methods, this ﬁnal step is generally much less critical.
With a fairly rational structure generation step, a rather limited number of
tentative structures with reasonable similarity to that of the unknown is
obtained. This makes step 5 not to be a very involved operation for these

systems.

In summary, it may be concluded from the above discussion that, while
from two distinctly different perspectives, both interpretive and library
search systems share the same ultimate goal of structure elucidation of
unknown compounds. This goal of structure elucidation is very explicit in
an interpretive system as opposed to implicit in library search. Note that
the cornerstone assumption of all library search systems is that if two
compounds have similar spectra, then they must have similar structure.

In fact as the search routine compares spectra, the user compares

13

structures and the success of the search system may be evaluated by how

closely these two processes match.

The most striking difference between the two systems is perhaps on the
degree of intervention by the human expert. This human intervention
varies from zero to a minimal level in search routines compared to an
extensively involved level in interpretive systems. The integrated use of
both matching and interpretive algorithms may be more fruitful in aiding
the human interpreter than either alone. Since matching system require
the least human intervention, they may be utilized ﬁrst. If a plausible
match is not found in the library, an interpretive algorithm can be
subsequently used to provide structural information. This information can
then be utilized as feedback to the matching routine in order to insert
compounds of appropriate structures into the library. McLafferty and
Stauffer (21) have, in fact, used the combination of PBM (Probability Based
Matching) matching routine (22,23) and STIRS (Self Training Interpretive
and Retrieval System) interpretive algorithm (24) with mass spectral data.

As the interpretive systems ﬁnd their history in the earlier work in
matching routine, the future development in both ﬁelds can also be very
much linked. Most likely, attempts at developing a hybrid between the two
types of algorithms tailored to a speciﬁc application domains will be the
trend of the future.

The remainder of this chapter describes the theory and applications of a
new method developed in this research for computerized infrared analysis

of organic compounds. The advantages of the new method are evaluated for

14

the analyses of both pure samples and mixtures using a small reference
library. The performance of the method is then compared to other
conventional search routines such as absolute differences and dot product
matrices. Finally, the application of the proposed method as a curve
resolution technique is described. Before all this, a brief history of
automated compound identiﬁcation with infrared spectroscopy is

presented.

'15

s,

[1‘

ll

.1 ‘53

I.

I

LIBRARY SEARCH BY INFRARED SECTROSCOPY
A BRIEF HISTORY

Infrared spectroscopy is one of the oldest and well established techniques
for the identiﬁcation of organic compounds. The identiﬁcation ability of IR
stems from the fact that the peaks in the spectrum generally correspond to
two types of vibrational modes, those that are characteristic of complete
molecular structure and those that are directly associated with speciﬁc
functional groups. This combination of the ﬁngerprint region (1400 - 400
cm'l) and the functional group frequencies makes the IR spectrum one of
the most structurally sensitive types of information that can be obtained.
Therefore, strong evidence for the identity of an unknown is provided by the
exhibition of a close match between a peak-to-peak comparison of the
unknown spectrum with those of references. This is, of course, the

foundation of library search systems.

In early days, compound identiﬁcation was done by manual comparison of
an unknown spectrum with those of reference compounds stored in
reference books. The advantages of this approach are twofold. First, the
reference spectra are in their original analog forms; thus, no loss of
information through digitization has occurred. Second, the human brain
is used as a pattern analyzer for similarity matching which is essentially
unsurpassed by any computer. However, the great deal of time and effort
that must be invested in manual searching of a large library of reference
compounds makes the appeal of this approach quickly disappear -

16

Researcher then began to look into a more efficient way of matching
unknown spectra with a digitized library of reference spectra via the use of
computers. The earliest attempt which made use of punch cards for
representation of digitized spectra, was reported by Kuentzel (25) in 1951
and later by Baker and coworkers (26) in 1953. A mechanical card sorter
was used to search for the cards with similar patterns. This system was
subsequently made to operate with an automatic card sorter and eventually
turned into a computer readable format known as the ASTM (American
Society for Testing and Materials) database. The ASTM database basically
consisted of binary representation of spectra along with some limited
structural information. Though the information in this database was
limited, it did certainly serve as a good beginning for automated library

search routines.

With the advances in computer technology and the introduction of magnetic
tapes and disks with greater storage and faster retrieval times, some
workers began utilizing them for faster search times based on ASTM
system (27-29). Various techniques such as spectral compression (29-31) ,
118811 COdiDE (32) and ﬁle inversion (33) have been used to further decrease
the search time and storage requirements. It should be pointed out
,however, that the decrease in search time and storage requirements which
is frequently achieved by the use of spectral compression comes at the cost
of a reduction in the information content of the spectra in the library. The
condensation of the spectra is typically done by deresolving both the
intensity and resolution producing thereby library spectra that are usually
inadequate for visual inspection One solution to this problem reported by

Lowry and coworkers (34) utilizes low resolution spectra for search

17

purposes while storing higher resolution spectra on a hard disk for visual
display and inspection. To this end, a very interesting paper by Divis and
White (35) describes an IR spectrum compression technique based on
deconvoluted band representation which can reduce the storage space for 4
cm'1 resolution by 95% with minimal loss of spectral information content.
However, because of the requirement of spectra reconstruction, the search
time is longer in a deconvoluted band compressed library compared to a

digitized reference library.

The main disadvantage of the ASTM system mentioned earlier is in the
way that the spectral information is represented (binary format) . In this
system, the spectrum is partitioned into a series of equally spaced intervals;
and the presence or absence of a peak in a given interval is indicated by
setting a corresponding bit to one or zero, respectively. Aside from the
problems associated with peaks on the interval borders, the system
essentially does not utilizes the intensity and peak-shape information

contained in an infrared spectrum.

A major step forward in IR library searching was the development of the
EPA vapor phase library by Azaraga (36). This was the ﬁrst large library
which utilized true digital IR spectra acquired with an FT-IR spectrometer.
The low noise level and the wavelength accuracy offered by the FT-IR
instrument has provided the basis for many ﬁill spectral search methods in
use today (37-45). A detailed review of various methods for computer
retrieval of spectral data by Hippe and Hippe (46) and a through evaluation
of various aspects of library search techniques by Zupan (47) are available in
the literature.

18

 

l

‘3';

IE :1

is!

However, the automated IR spectral analysis has been, for a long time,
suffering from the lack of a large, high quality and easily accessible
database. In this regard, it has certainly lagged behind other techniques
such as, for example, mass spectrometry. This is in part due to the fact
that the IR absorption band consists of both peak shape and ﬁequency
information as opposed to a series of mass/intensity pairs found in mass
spectrometry. Therefore, automated IR spectroscoy requires the use of fully
digitized spectra which has only recently become available on a routine
basis.with the introduction of FT-IR instruments. The after-the -fact
digitization of existing large IR databases is neither cost effective nor free of
error. Therefore, work must be done toward the creation of a large and

high quality FT-IR based database.

Moreover, in terms of the quality of IR spectra, the lack of a standard
method for spectral quality assessment such as the one developed for mass
spectrometry (48) poses a serious problem. Although the design and
implementation of a quality index is more difﬁcult for IR than MS, it is
nevertheless of extreme importance and a prerequisite for the development
of high quality IR library. Fortunately, the Coblentz society (49) has
recently begun work at addressing both the issue of IR spectral quality and
the coordination of efforts toward the creation of high quality database.
When these issues are properly resolved in the near future, it will deﬁnitely
generate a new wave of enthusiasm and activity in the area of computerized

Infrared spectral analysis.

19

At present, there are several computerized IR databases in existence.
These include a rather large database (ASTM) gathered on grating and
prism instruments along with various smaller FT-IR based systems. The
ASTM database contains over 100,000 spectra in binary format compiled
during 1950-1974. Although this library is no longer updated, it is still
available on magnetic tape and distributed by Sadtler Research
Laboratories (50). A collection of 94,000 spectra originally obtained on
grating instruments has also been digitized by Sadtler; in addition a
smaller vapor phase FT-IR database containing about 8600 spectra has been
produced. However, these are only available as parts of instrument data
systems which incorporates the database. Due to the advantages of PVT-IR
mentioned before, all other existing databases are essentially FT-IR based
systems. Most of these systems are relatively small in size with differing
and undeﬁned spectral quality. Some of the most popular ones are

mentioned here.

A condensed phase FT-IR database containing about 11,000 spectra is
available in book form (51), ﬂoppy disk for searching (52) or on nine track
magnetic tapes for use with any instrument or computer system available.
This database has been created collaboratively by Nicolet Instruments and
Aldrich Chemical Company using only Aldrich chemicals. Another FT-IR
database available in a computer readable format is that of the Georgia
State Crime Laboratory (49) which contains 1422 solid phase spectra of
various drugs. In 1979, Satdler Laboratories under the direction of Leo
Azarraga, created a library of some 3300 vapor phase FT-IR spectra. The
public domain availability of this database has greatly facilitated the
development and testing of search algorithms. This library is being

2)

continuously updated and current version containing about 10,000 spectra

is commercially available from Sadtler Laboratories.

 

THE DEVELOPMENT OF A CHEMOMETRIC APPROACH
FOR COMPUTERIZED IR ANALYSIS OF PURE AND
MIXTURE SAMPLES

Introduction

It was pointed out in the previous section that the research activities in
computer-based spectral identiﬁcation of organic compounds has been
continuously increasing in recent years. This recent momentum in the
ﬁeld can be attributed to the introduction of inexpensive microcomputers
with high speeds and large storage capabilities as well as the availability of
a large number of digitized reference spectra. This has, in turn, resulted
in the development of a wide range of library search algorithms and
spectral interpretation systems. Today, the number of routine qualitative
analyses performed by computerized spectral matching far exceeds those of
any other method.

The problem is, however, that while one rarely deals with pure samples in
real life situations, almost all spectral matching algorithms are designed to
handle pure compounds. This certainly poses a serious limitation to the
use of these systems in real life applications. As it will become more
evident later, to be able to achieve automated mixture analysis one has to
inevitably utilize more involved mathematics than the typical matching

factors employed in most ordinary library search routines.

r:-

-_--1

One attempt at handling mixtures has been through the concept of reverse
searching. In reverse searching, the reference is compared to the
unknown. The question is how many features of the reference spectrum
exists in that of the unknown. A mixture will obviously contain some
features from all the pure components found in it. When these features are
combined, it will not probably match any member of a library containing
only pure spectra. The best matches will, at best, be the spectra of the
mixture. More likely however, they may just be spectra that contain
various combinations of the functional groups from the pure components of

the mixture.

Another major approach for direct spectroscopic analysis of mixtures is
that of curve ﬁtting. A common assumption among all these techniques is
that there is a linear relationship between concentration and absorbance
(Beer’s law is obeyed), whereby each component in the mixture acts
essentially as an independent absorber. Therefore, the mixture spectrum

must be a linear combinations of component spectra.

Within the framework of curve ﬁtting, there exist two basic approaches for
solving the problem of the identiﬁcation of unknown mixtures. One
approach is to perform a single ﬁt of all reference spectra to the unknown,
i.e., to determine the linear combination which provides the best
approximation of the mixture spectrum. Each reference spectrum should
contribute in proportion to the concentrations of the corresponding
compounds in the mixture. Compounds not present in the mixture should
yield coefﬁcients approaching zero. Therefore, any compound producing a

coemcient above a certain threshold value would, in principle, be present in

23

 

the unknown. However, the number of ﬂoating point operations required
to carry out such an analysis is extremely large precluding the use of this
approach in conjunction with a large library. If one assumes that the
unknowns are only binary mixtures, one can achieve a signiﬁcant
reduction on the number of required math operations. Nevertheless, for a
library containing N compounds, there would still be N(N-1)/2 combinations
to be tested making the approach time-prohibitive for a large database.

The second approach is to perform the inverse of the above process, i.e., to
ﬁt the spectra of each library member by linear combinations of the spectra
of a series of mixtures. In this approach, one always needs to compute only
N ﬁts regardless of the number of pure component in the mixture. If the
mixture is composed of n components, for example, the n compounds in the
mixture can be identiﬁed from the 11 best ﬁts. The algorithm for such
analysis can be easily drived within the framework of factor analysis as
demonstrated by Gillette and Koenig (52-53) . A dramatic reduction on the
number of ﬂoating operations (- N(NPTS) n2) is generally achieved with the
use of this approach because the number of components in a typical
mixture is often much smaller than the number of reference compounds in
the library. The successful application of this approach, however, depends
upon the availability of a series of mixtures containing independent
concentrations of the same compounds which may not always be

obtainable.

The method described in this research will also attempt to ﬁt the unknown
spectrum (pure or mixture) to linear combinations of library specrta. The

unique feature of the method is that all library spectra are

24

orthonormalized. This results in a computationally more efﬁcient analysis
(to be discussed later). In fact, the use of . a orthonormalized library
combines the best features of both previously mentioned approaches in that
it is computationally efﬁcient and it can be done with a single sample of the
mixture. Next, the necessary mathematical background for undrestanding
the proposed method is presented.

MATHEMATICAL BACKGROUND

An Algebraic and Geometrical Description of
the Gram-Schmidt Orthonormalization process

The Gram-Schmidt orthonormalization process is a method employed by
mathematicians for the generation of coordinate systems in
multidimensional space. First, to develop an appreciation as to how an
orthonormalized vector space may be constructed, a number of vector
algebra deﬁnitions pertinent to the process are brieﬂy presented. A reader
familiar with these deﬁnitions may skip this part. An analytical as well as
a geometrical step by step description of the Gram-Schmidt process followed
by the application of the method to IR spectroscopy and the generation of an
orthonormalized library of IR spectra is discussed.

Let’s begin by considering a given vector space. A vector space V may be

deﬁned as any set of arbitrary objects on which two basic operations of

a. Addition, and
b. Multiplication by scalars (real numbers)
5

1;!

are deﬁned. By addition, it is meant that for each pair of objects x and y in
V one can associate an element x + y called the sum of x and y ; and scalar
multiplication is a rule of associating an element kx called the scalar
multiple of x by k for each x in V and each scalar k. Each element in this
set is then called a vector.

A very useful vector operation is that of linear combination. If a given

vector y can be expressed as the sum of scalar multiples of a set of other

vectors like S = (x1, x2, ...., xn) such as:

y=k1x1+k2x2+ ........ +knxn

where k1 , k2,...,kn are scalars, then vector y.is called a linear combination
of vectors x1, x2,...,x,,l . The set of vectors S = (x1, x2,...,xn) is said to span a
vector space V if they are in V and every vector in V is expressible as a

linear combination of (x1 + ..... + xn). In general, a set of vectors S may or

may not span the vector space V. If they span the space, then every vector
in V can be expressed as a linear combination of (x1, x2, ..., xn) and if they
do not, then some vectors are so expressible while others are not. If all the
vectors in V that are expressible as linear combinations of S are grouped
together, a subspace is obtained which is referred to as the space spanned
by S. These spanning sets S are very useful in many situations, for it is
often possible to study a vector space V by ﬁrst studying the vectors in the
spanning set S, and then extending the results to the rest of V. It is
therefore, desirable to keep the spanning set S as small as possible. The
question of ﬁnding this smallest spanning set for vector space V depends on

'3

the notion of linear independence. For instance, if for the spanning set S,

the following vector equation,

k1x1+k2x2+ ...... -l~k,,xn = 0

has only one solution, namely

k1=0,k2=0,kn=0

then S is called a linear independent set. The set of S will be linearly
dependent if there are other solutions. An important conclusion which
may be drawn from the concept of linear independency is that a linear
independent set for an n-dimensional space (Rn) can contain at most 11
vectors. This simply means that a set in It2 with more than two vectors or a

set in R3 with more than three vectors are linearly dependent.

Utilizing the ideas of linear independency and a spanning set, one can
arrive at a precise deﬁnition for the basis and dimension of a vector space.
We normally think of a line as being one dimensional, a plane as two
dimensional, and space around us as three dimensional. This intuitive
notion of dimension may be made more precise in the following general

way. A set of vectors S is called a basis for a given vector space V if

a. S is linearly independent
b. S spans V

When a vector space V contains a ﬁnite set of vectors S = (x1, x2, ...., xn)

which forms a basis, than V is called ﬁnite dimensional. Also it is a known
property of vector spaces that if S is a basis for vector space V , then every

set with more than n vectors (elements) is linearly dependent.

These properties of vector spaces allow the interpretation of the idea of
dimension. The dimension of a ﬁnite dimensional vector space V is deﬁned
to be the number of vectors in a basis for V. Since as mentioned previously,
a basis for a vector space is some linearly independent set of vectors that
span the vector space, it follows that the dimension of a vector space is the
minimum number of vectors required to span the vector space. As an
example, consider the vectors ( 1, o, 0) , ( o, 1, 0)., and ( o, o, 1) in R3 (3-D
space). These are just unit vectors in R3 and any vector in R3 may be
expressed as a linear combination of these three unit vectors. Notice, that
no two vectors are sufficient to span R3, but four vectors, say, ( 1, 0, 0 ) ,
(0, 1, 0 ) , ( 0, 0, 1.) , and ( 1, 1,.1 ) contains redundancy. Any one of them
could be omitted and the remaining three would span R3. Of course, not all
setsofthreevectors spanR3. Thevectors(1,0,0),(0,1,0),and(1,1,0)
although three (the dimension) in number, do not span 3-D space. These
vectors are linearly dependent and so one is redundant. The concept of
linear independence is, therefore, the key in consideration of the dimension

of vector spaces.

To be able to fully apply vector ideas to Euclidean geometry, one must have a
means of introducing concepts such as length, angle and perpendicularity.
These concepts can be deﬁned by introducing a kind of multiplication of

vectors called dot product or inner product . The dot product of two vectors
x = (x1, x2,...., xn) and y = (y1, y2,....,yn) is deﬁned to be a number

x.y=x1y1+x2y2+ ..... +Xnyn.

The length (norm) of a vector can then be easily expressed in terms of the

dot product. For instance, one can think of the length of x = (x1, x2, x3) in R3
as the distance from the origin to the point with coordinates (x1, x2, x3) . It

can be simply shown using Pythagorean theorem that the length of vector x
in R3 is given by:

lxl2=x12 +x22+x32

2”32) = (x..x)1’2

or lxl =(x12+x2
which simply says that the length of a vector can be obtained by computing
the dot product of the vector with itself and taking the square root of it. It is
also possible to obtain information about the angle between two vectors x
and y using the concept of dot product. If 0 is the angle between x and y,
then the dot product or Euclidean inner product x . y is deﬁned by :

lxllleosG ifx¢
-x'y= 0 ifx:

Il'll-

0andy 0
Dandy 0

This relationship simply suggests the following information about 0 :

EL)

31

p.

.15?

[(1

9 is acute ifand onlyif x.y>0
9 is obtuse if and only if x . y<0
9 =1t/2 ifandonlyif x.y=0

Based on this information, two vectors x and y are said to be orthogonal if
their dot product is zero. Obviously, two vectors are orthogonal if and only if
they are geometrically perpendicular. Thus, the dot product can be very
useful in situations where it is desired to decompose a vector into a sum of
two perpendicular vectors. For example, for two nonzero vectors such as x

and y, it is always possible to write y as :

y8V1+V2

where v1 is a scalar multiple of x and v2 is perpendicular to x as shown

below.

 

 

   

V1 x

The vector v1 is called the orthogonal projection of y on x and v2 is called the
component of y orthogonal to x. the vectors v1 and v2 can be easily obtained.

Since v1 is a scalar multiple of x, then:

v1 kx

y =v1+v2=kx+v2

in

Q

...J.

. d

an:

Taking the dot product of both sides with x,

x.y = (kx + v2 ).x = klxl2 + v2.x

Since v2 is perpendicular to x ; v2 . x = 0 and

 

 

 

x .
k = 3’;
l x l
and therefore:
x ° 5’ . . .
v1 = 2 - x is the orthogonal projection of y on x and
lxl
x ' y .
v2 = x - - x 18 the component of y orthogonal to x
lxl

The several concepts of vector algebra which have been reviewed so far, can

now be used in the construction of an orthonormal basis described next.

CONSTRUCTION OF ORTHONORMAL BASIS
The Gram-Schmidt Process

In the previous section, some basic concepts of vector algebra such as
norm, dot product, and linear independency along with a few important
characteristics of vector spaces such as spanning set, basis, and dimension
were brieﬂy reviewed. A good understanding of these ideas are essential to

the comprehension of orthonormal basis discussed here.

31

As mentioned before, the basis for a given vector space is deﬁned as a set of
linearly independent vectors which span the vector space. In many
problems which involves the use of vector spaces, the selection of a basis for
the space is at the discretion of the problem solver. The best choice is
naturally to select a basis which will simplify the solution of the problem at
hand. Often times, this best choice is a basis in which all vectors are
orthogonal to one another. This means that all pairs of distinct vectors in
the set are perpendicular. An orthogonal set in which each vector has a

norm of one (unit length) is called orthonormal.

These conditions are very useful and are, in part, the motivation behind
ﬁnding an orthonormal basis for vector spaces, since it is exceptionally

simple to express a vector in terms of an orthonormal basis. For example if

a vector space V has an orthonormal basis v1, v2....., vn , then any vector x

in V can be expressed as a linear combinations of basis vectors by :
x=clvl+ .......... + CjVj‘l’ ....... + Cnvn

Then the coefficients cj can be computed easily

X.Vj= c1v1.vj + ...... + cjvj.vj+ ......... + cnvn.vj

n
O
:l'
:..

.5?
:l'
2+
C

so that

Cj = x.vj

S]:

D7

.7

IL}?

(3

There is yet another interesting and useful aspect to an orthonormal set. If

v1 , v2 ,...., vn are an orthonormal set of vectors in the vector space V, and Z
denotes the space spanned by this orthonormal set, then every vector x in V
can be expressed in the form

x = 21 + 22
where 21 is in Z and 22 is orthogonal to Z by letting,

21 = (x.v1)v1 + (x.V2) V2 + ..... + (x.vn)vn

z2=x-(x.v1)v1 -(x.V2)v2- ...... -(x.vn)v,,

This process is geometrically shown below.

 

 

 

Generally it is possible to construct an orthonormal basis for any ﬁnite
dimensional vector space. One of the well known ways of constructing such
a set is the Gram-Schmidt procedure which is done in the following way.

Suppose that V is any nonzero n-dimensional vector space with a spanning

set S = (x1 , x2 , ...... , xn ) . The Gram-Schmidt process for construction of

an orthonormal set 0 = (01 , o2 , ...... , on ) would involve the following steps:

Step 1. Pick any of the x's, say x1 , and simply normalize it ,

 

Notice that

 

Step 2. To construct a vector 02 of norm one that is orthogonal to 01 ,
one may pick another of the x's , say x2 , and compute the

component of x2 orthogonal to the space Z1 spanned by 01

and then normalize it ; that is,

Projzlxg = (x2 . 01) 01 projection of x2 on 01

The orthogonal component of x2 orthogonal to 01 can be
deﬁned as vector y2 given by :

Y2 = x2 - Projzlxz = x2 - (12 -01)01

Note that y2 is perpendicular to 01 , for

Y2-Ol = Xz-Ol - (12.01)(01.01)
= 22.01 - 22.01 = 0

34

To obtain a unit vector 02 perpendicular to 01 , we normalize

Y2.
x2415 - 09°. ya
- lx2'(x2 ' 01)01| - |y2l

 

 

02

Of course, the vector y2 cannot be zero because that would

imply that x2 and 01 are linearly dependent.

Step 3. Having found 01 and 02, one chooses x3 and forms its

projection on the space Z2 spanned by 01 and 02 :
PTOjZZX3 = (X3.01)o1 + (xa.02)02

Similarly, the component of x3 orthogonal to the space Z2
shown by vector y3 , is obtained by subtracting this projection

from x3

ya = xa -(xa-01)01 - (Xe-0902

As before it can be checked that y3 is perpendicular to both 01

and 02 ,

y.is-01 = (Is-01) - (23.01) = 0
3'3-02 = (Xe -02)-(13-Oz) = 0

(a) —> X 1 ( 1 st reference vector)

(b) —-> O 1 ( 1st orthonormalized vector)
length

Component of X2
orthogonal to Z1

 

(c) 02 E 21

A ml
7 r

\ Orthogonal projection of X2 on Z1

Component of
X3 orthogonal
to Z2

 

 

Figure 5.1 The geometric representation of the Gram-Schmidt
orthonormalization process.

The orthonormalized vector 03 is then obtained by

normalizing y3 ,

 

The geometrical vector representation of the above three steps

is shown in Figure 5.1.

One proceeds in this way , successively computing 01 , 02 ,... ,

o,- . To ﬁnd om one computes:

 

Yj+1 = xj+1 - (xj+1 .0001 - ...... - (xj+1-Oj)oj
y. 1
and o.+1 = 3+
’ lyrll

It can be continued until one runs out of x's in the independent set. Since
the orthonormal set 01 , 02 , ..... , 0,, obtained in this way is linearly
independent, it will be an orthonormal basis for the n-dimensional space V.
6 Also, it can be shown that at each stage in the process, the vectors 01, 02 ,

.oj form an orthonormal basis for the subspace spanned by x1 , x2 ,...., xj .

In the next section the utility of the Gram-Schmidt procedure to Infrared

Spectroscopic analysis is discussed.

4‘!

F5.

Application to the Qualitative and Quantitative analysis of
Pure and Mixtures using IR Reference Spectra

The theoretical considerations of the application of the Gram-Schmidt
procedure to the qualitative and quantitative analyses of pure samples and
mixtures using IR spectral data is described here. First, it is important to
point out that, in principle, the proposed method is technique independent
and is certainly amenable to any spectrometric technique capable of
generating compound speciﬁc spectra (predictor vectors). However, all the
work in this research has been done on an Infrared Spectrometer and,

therefore, the discussion below is pertinent only to IR spectroscopy.

To be able to effectively utilize the Gram-Schmidt process, an n-dimensional
vector approach must be used. This can be achieved by treating an IR
spectrum of a given compound as an n-dimensional (composed of n

resolution elements) vector such as

S=(Il,I2, .......... ,In).

Secondly, a set of linearly independent vectors which spans the sample
space must be available. This is, in fact, equivalent to the very basic
assumption of all spectroscopic techniques (i.e., IR) that a given compound
has a unique spectrum different from all other compounds. Thus, the

spectra of a set of reference compounds will constitute a set of linearly

38

(I)
I,

(J '
41»
C)

,
Pp,

jut”.

‘I‘W'lh
:WW

I
lite

independent vectors. Therefore, once a library of IR molecular spectra is
available, it can serve as a spanning set for an n-dimensional space of

unknown compounds.
S = (S1 , S2 , ...... , Sn); Sparring set

This spanning set can be transformed into an orthonormalized basis using
the Gram-Schmidt procedure. Analogous to the manner explained in the

previous section, the ﬁrst orthonormalized spectrum 01 is made

proportional to the ﬁrst molecular spectra by a normalization constant :

01 = T11 s1 ; T11 = (31 -Sl)1/2

Note that 01 is normalized

(01.01) = T; (sis) = 1.0

Next 02 can be computed as

o _ [824320901]
2" 2 112

 

The denominator assures that 02 . 02 = 1.0 and also note that 02 is
orthogonal to 01 since

(8.0 )-(0 .0)
(02.01): 2 1 1 1 21,2=o.o
[(SZ.S2.)- I(Sz.01)l]

 

The Nth spectrum is orthonormalized to the previous library using

N-l
[SN-2(SN.Oi)Oi]

0 i=1
N N-l 2
[(SN.SN)-ZI(SN.Oi)I

i=1

 

]1/2

The analysis of a sample is then performed by computing the projection of
the sample spectrum into this orthonormalized reference space. Since a
pure sample can be thought of as a mixture containing only one
component, let’s consider a more general case of a mixture. A mixture
spectrum M00 is presumed to be a linear combination of the orthonormal
spectra which span the reference space represented by th following

equation:

N
Mm = 213 0,0.) (1)

where Ma, ) denotes the absorbance of the mixture at the wavelength 2., N is
the number of reference spectra and Pi = (M . Oi ) are the projections of the
mixture in the 0; direction. For a mixture of n components, there are only
n < N nonzero projections. The concentrations of the components are

obtained from these projections. If a given projection is less than or equal to

zero, the corresponding reference compound is presumed to be not present

in the mixture.

Considering that the orthonormal spectrum can be expressed as linear

combinations of the molecular spectra
oi = 2 Th. sj (2)

then the mixture spectrum can be transformed to the molecular basis

using

N
Mo.) = 2 Pi Oi (3)

i=1

Substituting for Oi from equation (2) yields

i

N'
Mm = 21' Pi 2 Th. SJ. (4)

18 j=l

Rearranging the summation indices one obtains
i i; ”
M(k)=. ( .PiTij)Sj=.1stj (5)

Assuming that each component in the mixture acts as an independent

absorber and the interaction among various components are negligible

41

vi-

b'.‘

~i‘

(Beer's Law is obeyed), one can then use Beer's Law to evaluate the

coefficients Kj . For each of the component spectra Sj (A), Beer's Law for a

given pathlength may be written as:

S. = C. a. (6)

Substituting the Beer's Law expression for each Sj into equation (5) for a

mixture

n C.

is
0.) ‘ SJ
jg], jg], Cjo

(7)

Comparison equation (7) with equation (5) yields

Ci
K: = a

J
where Cj is the concentration of jth component in the mixture and Cj is the
concentration of the same component in a standardized reference solution.

Note that equation (7) is the sum over the entire library containing all
possible components. The coeﬁicients Kj may be interpreted as :

C.
.01? = Positive value ; Component j present
K = .i
J
c 0 ;Component j absent

Note, that not only positive coefﬁcients for various components would be
indicative of their presence in the sample (qualitative analysis), but the
magnitude of these coefﬁcients is also directly proportional to their
concentrations. Therefore, one is able to obtain both qualitative and
quantitative information for both pure samples and mixtures from a single

run.

Another important characteristic of the Gram-Schmidt transformation is

its lower triangular growth at the time of library inception, that is :

01=Tllsl

02=TmSl+T2232

03=T3131+T3232+T3353

ON = TN1 81 + TN2 $2 + ............................. + TNN SN

Where Tij are the transformation coefﬁcients and S1 , ..... , SN are the
reference spectra in the library. Figure 5.2 also shows a simpliﬁed version
of the Gram-Schmidt transformation for ten hypothetical reference spectra
A through J .

MOLECULAR SPECTRA

 

“HEQHIFJUOw>

 

ORTHONORMALIZED SPECTRA

 

A

AB

ABC

ABCD

ABCDE
ABCDEF
ABCDEFG
ABCDEFGH
ABCDEFGHI
ABCDEFGHIJ

 

Figure 5.2 Representation of Gram-Schmidt transformation for
ten hypothetical compounds.

44

An important consequence of this triangular growth of the library is that it
allows for an efﬁcient partial analysis for a given target compound. As can
be seen from the simpliﬁed ﬁgure 5.2, if one is, for instance, interested in
compound I, it appears as a part of only two orthonormalized reference
spectra; and thus, the projections in only these two directions has to be
computed for its analysis. It may, therefore, be desirable for the
compounds that are most frequently analyzed to be placed at the beginning
of the library at the time of library inception. This results in tremendous
saving in time and computational effort. Most other matching factors used
in conventional search routines search the entire library regardless of
whether a given compound is at the beginning or the end of the library.
This‘ convenient feature of an orthonormalized library can be
mathematically expressed in a more general way. To determine the
concentration of reference compound N in a given mixture, only one

projection need to be computed

CN N
KN=EF= iXNPiT =(.MON)TNN

and similarly for compound N-1 , only two projections are to be computed

N-1,N-1

KN-1=.iPiTi -1=(M'ON-1)T +(M ON)T N1,N

In general, N - j + 1 projections are needed to determine the concentration

of the jth reference compound.

EXPERIIVIENTAL RESULTS

Several categories of experiments have been designed and carried out to
assess the extent to which the theoretical advantages attributed to the use of
an orthonormal reference library are realized in its applications. First, the
application of the method as a search algorithm for the identiﬁcation of
pure compounds is investigated. Next, the potential advantages of the
orthonormal method to the computerized qualitative and quantitative
analysis of mixtures is demonstrated. The performance of the method is
then compared to two matching algorithms most frequently used in
conventional library search, namely, the Dot Product and the Absolute
Difference. Finally its utility as a curve resolution technique for resolving

unseparated chromatographic peaks is examined.

All of the experimental work has been done on a dispersive Infrared
Spectrometer ( Beckman Acculab 1 ) interfaced to a microcomputer through
a 12 bit analog to digital converter. The manufacturer speciﬁed resolution
of the instrument was 4 cm'1 , but resolution of 6 - 8 cm'1 were practically
observed due to the age of the instrument. All reference, unknown, and
mixture spectra contain 450 absorbance values over the IR frequency range
of 600 - 4000 cm'1 . A 0.025 mm NaCl cell was used for all spectral
acquisition. Each absorbance value was obtained as the logarithm of an
average of 20 data points digitized by the ADC at a rate of 20 conversion per
second. All of the chemical magenta were from Fisher Chemical Company

46

 

 

 

which were used without any further puriﬁcation. The samples were all
prepared by volume; and therefore, the reported results are all in fractional

volumes.

Identiﬁcation of Pure Compounds

To examine the usefulness of the orthonormal method for the identiﬁcation
of pure compounds, a small library containing ten IR reference spectra 1
was compiled. All of the spectra were orthonormalized using the
previously described Gram-Schmidt process to obtain an orthonormalized
reference library. Independent samples from each of the reference
compounds were prepared and labeled as A through J and were treated as
unknowns. The IR spectrum of each unknown was acquired, normalized,
and subsequently projected into the orthonormalized reference library. The
computed coefﬁcients were then used as a match factor. Three
independent runs of the above experiment were performed and the
computed coefficients were averaged. The results obtained are shown in

table 5.2 .

Note that the projection step is actually a ﬁtting process where the
unknown spectrum is approﬁmated as a best ﬁt by the linear combinations
of the reference spectra in the library. The relative error of the ﬁt is

computed from the following formula:

47

Table 5.2 Analysis of Pure Samples

 

 

 

 

Unknowns
References A B C D E
Cyclohexane 0.986 0.054 -0.044 -0.220 -0.002
Hexane -0.0005 0.922 0.038 -0.030 -0.035
Toluene 0.010 0.017 1.00 0.070 0.016
Methylene chloride 0.006 -0.006 -0.007 1.15 0.008
Methyl acetate 0.002 0.006 0.029 0.003 1.02
Ethyl acetate -0.002 -0.005 -0.033 -0.013 0.064
Carbon tetra-Cl -0.0030 0.003 0.015 0.022 0.008
Propanol 0.004 0.004 -0.003 -0.008 0.007
Acetone -0.008 0.003 0.002 0.009 -0.094
Acetic acid 0.007 0.0009 0.004 0.013 0.011
Relative
Errors (x103) 4.41 2.23 7.81 5.66 4.41

 

 

 

 

 

Table 5.2 continued

Unknowns
References F G H I J
Cyclohexane —0.037 -0.002 0.087 -0.022 0.081
Hexane 0.028 -0.015 -0.031 0.027 -0.116
Toluene 0.021 0.030 0.070 0.026 0.067
Methylene chloride -0.024 -0.042 —0.046 -0.020 -0.027
Methyl acetate 0.073 0.011 -0.006 0.052 0.100
Ethyl acetate 0.902 0.007 0.033 -0.036 0.074 '
Carbon t-chloride 0.017 1.01 0.028 0.004 0.029
Propanol -0.002 0.004 0.873 -0.008 0.017
Acetone 0.020 0.0007 -0.003 0.98 0.002
Acetic acid 0.003 0.0005 -0.004 -0.008 0.942
Relative
Errors (x103) 3.40 3.37 4.01 4.16 3.75

 

r
0

V-s’

\1

,

'J

Amax -
_1_ - . 2
(449)J(M(7t) M00) d1

1min
8 = ).max

|_ J' (M00)2 d). _l
1min

Where M0) is the actual unknown spectrum and M' (X) is the best ﬁt. The

 

 

 

computed relative errors for each of the unknowns are also listed in table
5.2. A fairly small magnitudes of the relative errors (~10'3 ) indicates a

performance of a good ﬁt in each case.

Table 5.3 shows the results obtained when samples that are not represented
in the library are analyzed. These samples include: A, benzonitrile ; B,
diethylamine ; C, m-xylene ; D, methyl sulfoxide ; and E, bromopropane.
When compared to the results for compounds present in the library (table
5.2) , there are a number of points to be noted. First there is a rather broad
distribution of the fractional volumes contributed by various library
members. Second, there is at least one large negative contribution to the ﬁt.
These are the result of trying to approximate the unknown spectrum as a
linear combination of reference spectra which do not contain many features

common to it.

Another striking difference is the magnitude of the relative errors for these
samples relative to those of table 5.2. Notice that an order of magnitude
increase in relative error (~10'2 ) is obtained for all the samples. Similar
results have been obtained for numerous other analyses involving

compounds that are not included in the library. This certainly indicates

50

m H“! '

Table 5.3 Analysis of Pure Samples not Present in the Library

 

 

 

 

 

Unknowns—

References A B C D E
Cyclohexane 0.099 0,060 0.029 0.630 -0.310
Hexane -0.260 0.330 0.232 —0.730 0.770
Toluene 0.770 0.310 0.788 1.13 0.270
Methylene chloride 0.016 0.150 -0.320 -0.530 -0.053
Methyl acetate 0.093 -0.110 0.060 -0.320 0.085
Ethyl acetate -0.068 0.090 -0.012 0.760 0.100
Carbon t-chloride 0.323 0.070 0.244 0.090 0.130
propanol 0.019 0.040 -0.013 0.180 0.001
Acetone 0.016 0.020 -0.005 -0.150 -0.022
Acetic acid -0.012 0.020 -0.002 -0.070 0.021
Relative

Errors (x103 ) 27 25 18 35 23

 

that the magnitude of the relative errors can be effectively used as another
dimension of information to indicate the presence or absence of a substance

in the library.

The results obtained, clearly demonstrate that the use of orthonormal
reference spectra is an effective approach to the identiﬁcation of pure
compounds. The method gave unambiguous identiﬁcations in all cases
where the unknowns were present in the library. Some small contributions
from other components of the library were frequently observed for these
samples, such as 5.4% cyclohexane for sample B (Hexane). Often times,
this discrepancy can be eliminated through the use of non-negativity
constraints (next section). However, errors of this magnitude do not pose a

serious problem in applications where the sample is known to be pure.

For those samples not present in the library, the orthonormal method was
not only capable of classifying the unknown compound by structural
similarities to those of the library, but also the relative error proved to be a
reliable indicator of whether or not the substance was represented in the

library.

The most notable advantage of the orthonormal method, however, is that it
allows for a more general analysis that is capable of identifying the
components of the mixture. This feature of the method is demonstrated in

the next section.

ANALYSES OF MIXTURES

Most computerized compound identiﬁcation systems are designed to handle
pure samples. Quite often, ambiguous and unreliable results are obtained
if the sample happens to be impure or a mixture. Since most analysts
frequently encounter samples of a complex mixture, it is certainly
advantageous to be able to perform mixture analysis with the same
computational efﬁciency as that of pure samples. The computation
mechanism operative in the orthonormal procedure successfully allows for
this type of analysis, since it is indifferent to the nature of the sample. the ‘
same computation (projection step) is performed regardless of whether the
sample is pure or a mixture which accounts for computational efﬁciency.
Moreover, several additional measures have been undertaken in order to
improve the quantitative accuracy of the method as applied to mixture

analysis. These measures include the following:

1 . The imposition of the Non-negativity Constraints (NNC)

2. Analysis over a selected Active Region (AR)

3. Analysis using mixture references

As will be seen from the obtained data, the use of one, two, or all three of the

above measures can often result in a signiﬁcant improvement of

quantitation results. A brief description of each of theses steps is presented
at this point.

1. Non-negativity Constraints (NN C)

As was seen from the data for the analysis of pure samples, after the ﬁrst
projection is performed a number of reference spectra receive negative
coefﬁcients. Since negative coefﬁcients are physically meaningless, one
may impose a non-negativity constraint on the ﬁt by forcing it to have only a
positive solution. This may be done simply by removing from consideration
those spectra which received negative coefﬁcients in the previous analysis.
The process is then repeated in an iterative fashion until all coefficients
become positive. As will be seen from the data, for all cases examined this
process converges to a non-negative solution with a relative error

comparable to those of the unconstrained analysis.
2. Analysis over an Active Region(AR)

After an NNC operation is repeated a number of times, one usually obtains
a positive solution consisting of coefficients for the components that are
quite possibly present in the mixture. It was found that further
improvement in quantitation can be obtained by repeating the analysis over
a selected region (active region) of the reference spectra. The criteria for
selection of the active region is to ﬁnd a spectral window within which the
most differences in reference spectra occur. Although this process can be
placed under computer control, for all the reported data in this work they

were determined by displaying the reference spectra on the monitor and

54

visually searching for areas where they are the most different and at the

same time exhibit a good degree of spectral features.
afitﬁngtheunknownmixturebyasetofreferencemixtures

It was pointed out before that the orthonormal method is indifferent to
whether the sample is pure or a mixture. The same argument is certainly
true for the reference spectra. The reference spectra may well consist of a
series of mixtures of known and varying concentrations and of a given set of
components. The spectrum of the unknown can then be projected into this
mixture space from which the individual component concentrations of the
unknown mixture may easily be computed. Note that this is very similar to
a typical factor analysis experiment (39). Therefore, the orthonormal
method can be complementary to the results obtained by a factor or
principal component analysis. The ability to use mixture reference spaces
can also be very useful in quality control applications where it may be
desired to check a large samples of mixtures containing a deﬁned set of

components with known ranges of concentrations.

To assess the utility of the orthonormal method to the qualitative and
quantitative analyses of mixtures, a series of experiments with various
samples of mixtures have been carried out. A small orthonormal library
containing ten reference compounds has been employed. Table 5.4 shows
the list of compounds in the library and their corresponding abbreviations
used for ease of tabulation. All of the mixtures have been prepared.by

volume, and the results are reported as percent fractional volumes.

Table 5.4 List of compounds in the library

 

 

Names Abbreviations
Cyclohexane ( CYC )
Hexane ( HEX )
Toluene (TOL )
Methylene chloride ( MCL )
Methyl acetate ( MAC )
Ethyl acetate ( EAC )
Carbon tetrachloride ( CTC )
O - Xylene ( OXY )
Acetone ( ACT )
Acetic acid ( ACA )

 

Table 5.5 shows the results obtained for MDITURE 1 which was in fact a
sample of pure acetone. Although it is evident from the ﬁrst analysis that
the sample is not a mixture and is certainly pure acetone, the application of
the NNC operation tow times resulted in higher quantitative accuracy. The
analysis on the selected active region in this case does not lead to any
further quantitation improvement. In fact, for this sample it might not
have been necessary to do this step at all since the quantitative result of the
previous step was highly accurate. Also notice that the standard deviations
remain relatively constant for all the steps pointing to the performance of a

good ﬁt in each case.

The results for MDITURE 2 which consisted of 50% cyclohexane and 50%
hexane is shown in Table 5.6. Again the results from the ﬁrst run clearly
point to cyclohexane and hexane as the only two major components. The
application of the NNC three times leads to excellent qualitative analysis,
since the list is narrowed down to the only two components present in the
mixture. Although somewhat improved by the NNC operations, the
quantitative results are still largely at error. Signiﬁcant improvement in
quantitation is realized in this case by repeating the analysis over the
selected region of 600 - 1900 cm4. The standard deviations of the ﬁts remain
fairly constant indicating a good ﬁt in each step. Also, an important point
that was previously alluded to was the frequent observance of small
fractional volumes (~7%) contributed by some library members at the time
of the ﬁrst projection. For instance, looking at the data from the ﬁrst run,
methyl acetate (6.8%) may well be suspected to be present in the sample.
This suspicion is soon eliminated after the ﬁrst NNC application where the
coefﬁcient for methyl acetate turns negative. As will be seen from all the

57

Table 5.5 : Results of the analysis of MIXTURE 1

MIXTURE l : ACT (100%)

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

CYC HEX TOL MCL MAC EAC CTC OXY ACT ACA
1.6 -1.0 0.50 0.70 2.0 -1.4 -1.2 -1.6 105 -050
SD=0.05
NNC(1)
CYC TOL MAC ACT
-0.08 -275 0.24 102.5 SD=005
lNNC<2>
MAC ACT
-0025 10025 31330053
6 A31 (600-1000) Cm‘1
MAC ACT
- 3.19 103.19 SD =0.025

 

 

 

 

 

Table 5.6 Results of the analysis of MIXTURE 2

MIXTURE2 : CYC (50%) , HEX(50%)

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

CYC HEX TOL MCL MAC EAC CTC OXY ACT ACA
32.8 74.6 - 9.4 - 3.5 6.8 - 7.3 1.3 1.4 2.0 1.5
INN C (1) SD = 0.043
CYC HEX MAC CTC OXY ACT
32.42 79.14 - 1.43 - 1.52 - 9.13 0.53
SD = 0.055
NNC (2)
CYC HEX ACT
33.69 68. 41 - 2.10 SD = 0.061
lNNC (3)
CYC HEX
34.10 65.89 SD = 0.063
-1
A - R 1(600 - 1900) Cm
CYC HEX
49.88 50.11 SD = 0.062

E}

subsequent data, the small fractional volumes (not present in the mixtures)
are consistently eliminated by the successive NNC operations This is very
useful for it can provide greater assurance as to whether, for example,
methyl acetate is actually present in the sample or the 6.8% coefficient is
simply caused by the redundancy of the choices from which the best ﬁt is to

be computed.

Table 5.7 shows the results for a ternary mixture (MIXTURE 3) consisting
of 30% cyclohexane, 40% ethyl acetate, and 30% acetone. Similar to the
previous samples, the method is successful in reducing the list down to the
three components actually present in the sample with better than 5%

quantitative accuracy.

The next two experiments with MDITURE 4 and MDITURE 5 are carried
out to compare the improvement in quantitation realized by ﬁtting the
unknown mixtures by a reference space of mixture spectra relative to those
of a typical analysis performed for previous samples. MIXTURE 4
consisted of 80% cyclohexane and 20% hexane and the results of the ﬁrst
projection along with the subsequent NNC operations for this mixture are
shown in Table 5.8. Notice that the two components in the mixture are
correctly identiﬁed, but the quantitative results are about 9% in error.
Using the 88.8% cyclohexane and the 11.11% hexane from table 5.8 as an
initial estimate of their concentrations, two independent mixtures of
cyclohexane and hexane with concentrations within the above range were
prepared. The spectra of the two mixtures were acquired and subsequently
orthonormalized. The spectrum of MDITURE 4 was then projected into

this two dimensional mixture space. The results obtained are shown in

6)

Table 5.7 Results of the analysis of MDCTURE 3

MIXTURE 3 : CYC (30%) ;EAC (40%) ;ACT (30%)

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

CYC HEX TOL MCL MAC EAC CTC OXY ACT ACA
63.1 -3.27 -6.43 7.03 -5.87 59.5 -532 -18.1 43.4 -355
SD=0.13
NNC (1)
CYC MCL EAC- ACT
35.6 - 3.55 39.0 28.9 SD = 0.15
NNC(2)
CYC EAC ACT
34.4 37.1 28.5 SD = 0.15

Table 5.9. The results of table 5.9 clearly show a signiﬁcant improvement in
quantitation compared with the data in table 5.8. In fact, an increase in

quantitative accuracy from about 9% to better than 0.5% is achieved.

The analysis of MIXTURE 5 was carried out in a similar manner to that of
MIXTURE 4. This mixture was a four component system containing: 30%
cyclohexane, 20% methyl acetate, 30% ethyl acetate, and 20% acetone. The
results obtained from a typical orthonormal analysis and those of using a
mixture space are shown in Table 5.10 and 5.11, respectively. It is again
evident from the results in table 5.11 that the accuracy of quantitating
individual components in MIXTURE 5 has greatly improved.

The overall results shown and the experience with numerous other
samples prove that the orthonormal method is an effective approach to the
analysis of mixtures. The method is particularly useful in applications
where a list of possibilities either eldsts or can otherwise be predicted. To
this end it is also amenable to those situation where it is of interest to

analyze a large number of samples in a relatively short period of time.

As can be seen from all the cases examined, the qualitative and quantitative
results obtained from even the ﬁrst run were quite reliable. The use of non-
negativity constraints proved to be a reliable method of not only eliminating
the suspected compounds (small positive coefﬁcients), but also of providing
greater assurance about the components that are actually present in the
unknown mixture. It is also important to point out that mixture analysis

with the orthonormal method requires only one run on a single sample.

 

—-—'

Table 5.8 Results of the analysis of MIXTURE 4

MIXTURE4 : CYC(80%) ; HEX(20%)

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

CYC HEX TOL MCL MAC EAC CTC OXY ACT ACA
79.7 20.6 -2.68 -204 8.26 -9.88 1.35 -5.33 2.95 -244
¢NNC (1) SD=°~°9
CYC HEX MAC CTC ACT
93.2 13.9 -420 -3.27 0.361
SD=O.10
NNC(2)
CYC HEX ACT
89.3 14.4 -3.77 813:0”
lNNC(3)
CYC HEX
88.8 1111 SD=0.10
63

 

Table 5.9 Results of the analysis of MDCTURE 4

MIXTURE4 : CYC (80%) ; HEX(20%)
(Fitting into Mixtures)

 

MIXﬂlBES 010.121 HEW 99.28.19.)
(1) 90 10 48.2
(2) 70 30 51.7

CYC% = 0.9 * C(1)+ 0.7 * C(2) = 79.6%

HEX% 0.1 * 0(1) + 0.3 * C(2) = 20.3%

 

 

Table

 

 

Table 5.10 Analysis of MIXTURE 5

MIXTURE 5 : CY C (30%) ;MAC (20%) ; EAC (30%) ;ACT (20%)

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

CYC HEX TOL MCL MAC EAC CTC OXY ACT ACA
71.1 -105 3.30 2.15 19.0 47.5 1.94 -251 39.0 -2.90
lNNC (1) SD=0-13
CYC TOL MCL MAC EAC CTC ACT
39.3 - 19.3 4.89 14.6 32.2 - 0.44 28.8
SD=0.14
lNNC(2)
CYC MCL MAC EAC ACT
, - 14.0 .0 .
331 2.71 31 245 813:0”
lNNC(3)
CYC MAC EAC ACT
13.7 . .
32.3 299 243 SD=015

 

Table 5.11

Analysis of MDITURE 5

MIXTURE 5 : CY C (30%) ;MAC (20%) ; EAC (30%) ;ACT (20%)

(Fitting into Mixtures)

 

 

MIXTURES CYC MAC EAC ACT C (%)
(1) 30 30 2) 26.9
(2) 35 15 2) 14.9
(3) 32 15 25 71.3
(4) 30 15 % - 13.1

n
%X = 2 X(n) x can
in].
CYC % = 32.5% (30)
MAC % = 19.3% (20)
EAC % = 26.2% (30)
ACT % = 23.2% (20)

This

hall

spat
01111
call
lam

mil

This is rarely the case with any other method particularly in regard to the

quantitative analysis of multicomponent systems.

An exception to this rule is, of course, when the procedure of ﬁtting into
mixtures is employed. Therefore, one must only attempt to utilize mixture
spaces when it is intended to obtain results of high quantitative accuracy.
Otherwise it should be avoided since the use of this step does, in fact,
compromise the above mentioned advantage of the orthonormal method,
namely that of single shot analysis. However, the potential applications of
mixture spaces are limited to circumstances where the components of the

mixture are not only known but also available in pure form.

is 13
search
lbrarj
algor'l
of the
either
Spectl
used

abovi

toot
Spect
06cm
Prod
ﬁle:
one

slim

CONIPARISON OF THE ORTHONORMAL METHOD TO OTHER
LIBRARY SEARCH MATRICES

As was previously pointed out, the identiﬁcation of compounds by library
search is done by comparison of the unknown spectrum with a digitized
library of reference spectra. A variety of matrices have been used as search
algorithms (matching factors) to match the unknown spectrum with those
of the library. The tow most common matrices are based on computing
either the dot products (DT) or the absolute differences (AD) of the unknown
spectrum and the spectra of the library compounds. Many other commonly
used matching routines are, in fact, based on various derivatives of the
above two matrices. With the use of the dot products, the reference spectra
are actually normalized to unity by one’s dividing through by the square
root of the sum of the intensities. The dot products of the unknown
spectrum with those of the library members are then computed, and the
occurrence of a dot product approaching unity is considered a match. A dot
product of exactly one is only possible if the two compounds are identical
(identity search). Otherwise, a dot product with closest approximation to
one may be obtained for the reference compound with the most structural

similarity to that of the unknown (similarity search).

When the absolute differences approach is used, the unknown is identiﬁed
as the reference spectrum that yields a difference closest to zero. Notice
that both procedures fundamentally rely on the uniqueness of the spectrum

of every compounds. This is, in fact, an implicit linear independency

$

assumption in both methods, although no attempt is made by neither of the

methods to insure that this assumption is not violated.

In this section the performance of the orthonormal procedure is compared
to those of the dot products and the absolute differences methods. In this
regard, the results obtained by all three methods for a same set of samples
on the same ten member library are compared and evaluated. The dot

products were computed from the following formula:

1/2
.1 .i .i
2 2
“mi = 2M1 542 Mi 2 Si]
Jg'lmin J'Jmio J‘Jmis

where Mj is the jth data point in the unknown spectrum and 8;; is the jth
data point in the spectrum of the ith compound in the library. As can be
seen from the formula, the reference spectra are normalized in DP but not

orthogonalized. The average absolute differences were computed from
.6 2 '"5 ‘ Sﬁ'
‘ l = , . T -
.J I)“

A total of six samples (A through F) were examined in this study. Samples
A, B, and C were cyclohexane, hexane, and acetone, respectively. Sample
D, diethylamine, and sample E, cyclohexane, were not present in the
library; and ﬁnally sample F was a binary mixture consisting of 20%

cyclohexane and 80% hexane. The results of the analyses of these samples

by orthonormal, dot products, and absolute differences are shown in tables

5.12, 5.13, and 4.14, respectively.

As can be seen from the data in these tables, samples A, B, and C are
correctly identiﬁed as cyclohexane, hexane, and acetone, respectively, by
each of the three methods. The results of the analyses of many other
samples have shown that all three methods exhibit the same predictive
power when the samples are pure. No noticeable advantage is shown by the
orthonormal method in regard to pure samples. Examining the results
obtained for sample D (not present in the library) indicate that the absence
of sample D (diethylamine) from the library may be easily revealed from the
magnitude of the relative error in the orthonormal method. To a lesser
degree of reliability, one may also arrive at the same conclusion from the
DP and AD results for this sample. Notice that all the dot products are
considerably less than one, and none of the absolute differences are

approaching zero.

Sample E (cyclohexane) was also not present in the library, but its spectrum
is very similar to the spectra of library compounds cyclohexane and, to a
lesser extent, toluene. The results of the analysis by the orthonormal
method is certainly in agreement with these facts. Again, notice that the
magnitude of the relative error points to the absence of cyclohexane from
the library, while the coefﬁcients of 0.75 for cyclohexane and 0.30 for hexane
is indicative of its structural similarity to both compounds.

The results of orthonormal analysis for sample F which was a binary
mixture consisting of 20% cyclohexane and 80% hexane show a number of

70

Table 5.1 2 Analysis using orthonormal method

 

 

 

 

Samples
References A B C D E F
CYC 1.00 0.07 0.02 -0.06 0.75 0.13
(0.99) (0.06) (0.00) (0.00) (0.72) (0.12)
HEX .07 1.00 0.01 0.56 0.14 0.94
(0.01) (0.94) (0.00)‘ (0.05) (0.13) (0.88)
TOL -0.06 -0.05 0.01 0.21 0.30 -0.07
(0.00)‘ (0.00) (0.00) (0.17) (0.15) (0.00)
MCL —0.01 -0.02 -0.01 0.19 -0.02 -0.04
(0.00) (0.00) (0.00) (0.19) (0.00) (0.00)
MAC 0.05 0.02 0.02 -0.17 -0.08 0.00
(0.00) (0.00) (0.00) (0.00) (0.00) (0.00)
EAC -0.06 -0.03 -0.01 0.17 0.10 0.00
(0.00) (0.00) (0.00) (0.00) (0.00) (0.00)
CTC 0.00 0.01 -0.01 0.01 -0.03 0.00
(0.00) (0.00) (0.00) (0.01) (0.00) (0.00)
OXY -0.01 0.01 -0.02 -0.01 -0.11 0.03
(0.00) (0.00) (0.00) (0.00) (0.00) (0.00)
ACT 0.02 0.03 1.02 -0.03 0.03 0.02
(0.00) (0.00) (L00) (0.00) (0.00) (0.00)
ACA -0.02 -0.02 -0.01 0.04 -0.01 -0.01
(0.00) (0.00) (0.00) (0.03) (0.00) (0.00)
Relative
Error (x 103) 4.6 4.9 3.3 8 17 3.6

 

 

 

Table 5.13 Analysis using the dot product

 

 

 

Samples
References A B C D E F
CYC 0.99 0.94 0.25 0.69 0.92 0.95
HEX 0.94 0.99 0.30 0.75 0.89 0.99
TOL 0.41 0.48 0.48 0.69 0.54 0.49
MCL 0.07 0.10 0.13 0.48 0.16 0.10
MAC 0.23 0.26 0.75 0.38 0.28 0.27
EAC 0.24 0.29 0.76 0.43 0.31 0.30
CTC 0.00 0.01 0.06 0.30 0.02 0.01
OXY 0.61 0.70 0.44 0.74 0.67 0.70
ACT 0.22 0.28 1.00 0.35 0.26 0.28
ACA 0.36 0.62 0.54 0.43 0.39

0.37

 

Table

RCTCTI

Cl

EA
CT
OX
AC

AC

 

Table 5.14 Analysis using the absolute differences

 

 

 

Samples
References A B C D E F
CYC 0.05 0.08 0.34 0.27 0.09 0.07
HEX 0.07 0.04 0.33 0.26 0.10 0.03
TOL 0.20 0.20 0.29 0.30 0.18 0.20
MCL 0.20 0.21 0.37 0.31 0.21 0.22
MAC 0.48 0.47 0.27 0.54 0.47 0.47
EAC 0.46 0.45 0.26 0.50 0.45 0.45
CTC 0.20 0.22 0.38 0.37 0.22 0.22
OXY 0.18 0.16 0.31 0.28 0.15 0.15
ACT 0.33 0.32 0.03 0.44 0.33 0.32
ACA 1.37 1.36 1,17 1.20 1.33 1.34

 

 

interesting points when compared to those of the other two methods. The
orthonormal method clearly indicates the presence of both components.
Notice that cyclohexane and hexane are the only two components that make
non-negative contributions. Both DP and AD, on the other hand, predict

sample F as being pure hexane.

In general, the successful application of DP and AD relies heavily upon a
perfect match between the unknown and a reference compound in the
library. This is something which is rarely achieved due to subtle changes
in external conditions, the presence of noise in the spectra, and changes in
other experimental parameters. As a consequence, the dot products and
absolute differences do not always give deﬁnitive identiﬁcations. Quite
often, these methods fail to discriminate between related compounds. In
particular, both methods are incapable of differentiating between a pure

compound and a mixture with similar spectrum.

In conclusion, the overall results for the six samples examined
demonstrate that the orthonormal method shows an equivalent
performance to that of the other two methods for identiﬁcation of pure
compounds. However, the orthonormal method is certainly more powerful
when it comes to samples not represented in the library or to those of
mixtures. This latter consideration is extremely important in those
situations where the IR spectra are used in conjunction with separation

techniques for peak identiﬁcations. This is the subject of the next section.

74

APPLICATION OF THE ORTHONORMAL METHOD TO
THE RESOLUTION OF SEVERLY OVERLAPPIN G
CHROMATOGRAPHIC PEAKS

Introduction

Major activities in the ﬁeld of chemical analysis today are directed toward
the analysis of complex mixtures such as food, biological ﬂuids and
environmental samples. The information content of the analytical signal
for such systems is multivariate in nature. The analytical chemist is often
charged with the task of decomposing this multivariate information into its
univariate components to determine the signal due to the analyte of interest
in the composite signal. Efforts to achieve this by means of chemical
separation are not only time consuming but often unsuccessful.
Instrumental approaches in dealing with the problem have been mainly
centered around the development of hyphenated instruments capable of
providing multivariate data from which univariate information may then
be extracted. This has resulted in the emergence of very powerful
analytical techniques such as GC/IR, GC/MS, GC/MS/MS and GC/IR/MS
which can generate large quantities of multidimensional data on a single
chromatographic peak. The major rate limiting step in further expansion
of this technology for complete utilization of its power appears to be the
maturity gap between the instrumentation on one hand and data

processing methodologies on the other.

‘ 75

Among the existing techniques, combined gas chromatography/infrared
spectroscopy and gas chromatography/mass spectrometry are thus far the
most established and well documented ones in regard to complex mixtures
(53-54). The utilization of such techniques, however, does not in any way
guarantee complete separation; and their full fruitfulness is not often
realized due to overlapping components. This may be considered a serious
pitfall in light of the fact that overlapping chromatographic peaks in
complex mixtures is a very commonly occurring event as pointed out by
Davis and Giddings (55). To this end, many mathematical methodologies
have been proposed to decompose an overlapping peak into its constituent

components.

O'Haver and Green (56) have shown the usefulness of derivative functions
for improving resolution. Application of the method, however, is limited to
situations where the experimental SM is high and also depends upon
relative intensities and separations in relation to the half width as

demonstrated by the same authors (57).

There have been many curve resolution techniques described for obtaining
the response shape where the selection of an appropriate choice depends
upon the amount of information available prior to the analysis. Lawton and
Sylvestere (58) have described a self modeling curve resolution (SMCR)
technique for estimating the component spectra of a series of binary
mixtures. This technique was extended by Borgen and Kowalski (59) to
resolve three components and by Sharaf (60) to obtain qualitative and
quantitative resolution of ternary mixtures using various

chromatographic/spectrometric systems. A similar approach was taken by
76

Vandeginste and coworkers (61) to resolve a three component mixture

eluting from an LC/UV-VIS system.

Factor analysis as well as principle component analysis using both IR (62-
63) and mass spectral (64-66) data have also been employed to study
mixtures of overlapping peaks. Although these latter techniques, contrary
to curve resolution, do not assume a predeﬁned peak shape, they do
however assume a constant peak shape over a range of relative
concentrations. This may not often be a valid assumption as pointed out by

Lundeen and J uvet (67).

Another approach within the framework of curve resolution is that of band
ﬁtting. These methods assume that the peak shape (either mathematical
or empirical) for each of the components of a composite proﬁle is predeﬁned
and that it remains constant at various concentrations. These are not only
severe assumptions and therefore susceptible to error; but when added with
the restriction of needing a considerable amount of advance information
about the system, these limitations make this alternative limited in scope.
Critical evaluation and the limitation of curve ﬁtting techniques have been
the subject of two excellent publications (68-69).

In this section the application of the orthonormal method to the qualitative
and quantitative resolution of fused chromatographic peaks is
demonstrated. Isenhour and coworkers have also made use the Gram-
Schmidt procedure in a different way for gas chromatogram reconstruction
(70). In this work, they have employed direct interferograms from a
GC/FTIR instrument. Their approach is similar to gas chromatogram

77

reconstruction from mass spectral total ion currents. After the collection of
several background interferograms, they are orthonormalized to represent
the background space. Subsequently, the orthogonal components of each
successively scanned interferogram to this background space are
computed. This orthogonal component will be a measure of the total

absorption of an IR active species in the light path.

To examine the utility of the orthonormal method to the resolution of
overlapping peaks, samples of pure compounds and mixtures of known
composition from structurally related components were prepared to
simulate pure and overlapping peaks respectively. Reagent grade
compounds from the Fisher company were used without further
puriﬁcation for preparation of all samples. All reference and mixture

spectra were collected in a similar manner described before.

The ﬁrst question to be asked about a given chromatographic peak
attributed to a given component is whether it is pure or composite. If
composite, the second question might be how many components are there
and at what relative concentrations are they coeluting. The proposed
method can offer reliable answers to these questions provided that a list of

possibilities is available.
Peak purity determination

In this type of application it is often desirable to determine the purity of a
peak which is perceived to belong to a given component. A typical example

may be regular analyses of a large number of samples for the presence of a

78

certain component. As mentioned earlier, the best results are obtained
when a list of possibilities either exists or can be provided as an output ﬁ'om
a larger search. The top twenty candidates containing the analyte of a
large search may, for example, be used for this purpose. Once such list is
available as a reference, all the reference spectra are orthonormalized
using the Gram-Schmidt procedure. Subsequently, the projection of the
peak spectrum into this orthonormalized reference space is computed. If
the peak is pure, a coefficient approaching unity will be obtained for the

peak component in the reference space.

The following experiment has been designed to test the applicability of the
proposed method for peak purity determination. A small reference space
containing nineteen compounds, many of which are similar, has been
selected and their IR spectra have been acquired. An orthonormalized
reference space has been constructed based on these IR spectra using the
Gram-Schmidt procedure. Pure samples have been prepared and under
the same conditions their IR spectra have been collected and subsequently

projected into the orthonormalized reference space.

Table 5.15 lists the results obtained for this experiment when sample A
(chlorobenzene) and sample B (chloroform) were examined against the
reference list. Notice that all components with negative coefficients are
assumed to be absent along with those having negligible positive
contributions. This non-negativity assumption will be exploited for
improvement of quantitative results later in this paper. Data in table 5.15
indicate that in both cases the samples are correctly identiﬁed as being pure
chlorobenzene with a coemcient of 0.974 (97.4%) and pure chloroform with a

79

Table 5.15 Peak purity determination

 

 

 

 

J _3__
Reference
compounds Coeff. (%) Coeff. (%)
Chlorobenzene 0.974 (97.4) - 0.080 (0.00)
Chloroform -0.000 (0.00) 1.002 (IN)
Nitrobenzene 0.012 (0.00) - 0.005 (0.00)
Acetonitrile 0.025 (0.00) - 0.033 (0.00)
Toluene 0.053 (0.00) 0.101 (10.1)
Hexane 0.000 (0.00) - 0.017 (0.00)
Acetone -0.047 (0.00) - 0.370 (0.00)
Benzene 0.021 (0.00) - 0.046 (0.00)
3-Me-3-pentanol 0.007 (0.00) - 0.004 (0.00)
Methylene chloride - 0.058 (0.00) 0.077 (0.00)
Methly acetate - 0.010 (0.00) 0.018 (0.00)
Dimethyl sulfoxide - 0.031 (0.00) - 0.012 (0.00)
M-xylene - 0.019 (0.00) - 0.013 (0.00)
Anisole 0.022 (0.00) 0.063 (0.00)
Me-benzyl alcohol 0.023 (0.00) 0.000 (0.00)
Aniline 0.034 (0.00) 0.077 (0.00)
Bromobenzene - 0.089 (0.00) 0.028 (0.00)
Isoamyl alcohol - 0.008 (0.00) - 0.004 (0.00)
Dimethyl foramid 0.006 (0.00) - 0.022 (0.00)
Relative
errors ( x103 ) 6.2 7.5

 

coefﬁcient of 1 .00 (100%). A good ﬁt is also indicated by the small values for

the relative errors of the ﬁt.

Although the coefﬁcient of exactly one (1.00) for chloroform in sample A can
be interpreted as clear indication of its purity, a 10% contribution from
toluene is nevertheless indicated. This may not be a serious problem in
many practical situations and could often times be rejected based on
chromatographic retention time consideration. Moreover, experience with
running many pure samples has shown that this kind of discrepancy is
observed from time to time as the method attempts to achieve a best ﬁt by
combining small positive and negative contributions from the rest of the
reference space. However a coefﬁcient of greater than 0.95 (95%) has never

been obtained except for pure samples.

The results obtained clearly demonstrate the effectiveness of the proposed
method for peak purity determination particularly considering the fact that
in many practical situations the analyst deals with yet a smaller set of
possibilities than the one employed in this experiment. Additionally, since
only one spectrum need be collected on a peak proﬁle, this technique may
prove to be especially useful in circumstances where a large number of

samples are to be processed rapidly for the presence of certain peaks.

Resolution of overlapping peaks

In resolving overlapped peaks, the analyst is typically interested in
determining the number of components in a peak where there usually

exists a small list of possibilities. To test the applicability of the method to
81

such situations the following experiment has been designed. A small
reference set of four structurally related compounds —- benzene,
chlorobenzene, bromobenzene and nitrobenzene ——has been selected; and
in a similar way as explained in the previous section, their
orthonormalized base has been formed. Samples ranging from pure (pure
peak) to ternary mixtures (fused peaks) were prepared and their spectra
projected into the above reference space. The composition of the samples
were as follows: sample A, pure chlorobenzene; sample B, 50%
chlorobenzene and 50% bromobenzene; sample C, 30% benzene and 70%
nitrobenzene; and ﬁnally sample TD, 30% chlorobenzene, 20% bromobenzene

and 50% nitrobenzene.

Table 5.16 lists the results for this experiment. As evident from the data in
table 5.16, sample A is clearly identiﬁed as pure and samples B, C and D
are all successfully determined as being mixtures. An overall good
quantitative accuracy (average of 13%) is obtained with the exception of that
for sample B where 15% benzene is erroneously predicted. This may not
pose a serious problem if the peak was expected to be, for example, a
mixture of halogenated benzenes. In addition to the retention time
consideration mentioned before, the prediction of the presence of benzene
may be suspect based on the magnitude of the relative error of the ﬁt which
is approximately twice as large for sample B than for the others. Moreover,
due to domain independency of the method, one can use mass spectral data

to more easily distinguish benzene from its halogenated derivatives.

It is important to realize, however, that when applied to peak resolution,
there are two unique advantages offered by the proposed method

82

Table 5.16 Resolution of overlapping peaks

 

 

 

 

A B C D
Reference
compounds Coeff. Coeff. Coeff. Coeff.
( % ) ( % ) ( % ) ( % )
Benzene 0.03 0.15 0.29 - 0.05
(0.00) (15.0) (29.0) (0.00)
Chlorobenzene 0.96 0.53 - 0.08 0.32
(96.0) (53.0) (0.00) (32.0)
Bromobenzene - 0.05 0.48 0.08 0.14
(0.00) (48.0) (0.00) (14.0)
N itrobezene 0.03 - 0.03 0.71 0.51
(0.00) (0.00) (71.0) (51.0)
Relative
errors ( x103 ) 6.5 12 5.6 7.4

 

pal
pri
all
on
21;
till
185
col
3 P

CV8

Tim

DCE

 

particularly in contrast with such techniques as factor analysis (FA) and
principle component analysis (PCA). The ﬁrst and most important
advantage is that chromatographic resolving power will not impose a limit
on peak resolution. On the other hand, FA and PCA will totally fail if, for
example, two components are eluting from the column at exactly the same
time with the same peak proﬁle. All mixture spectra are identical in such
cases making the resolution impossible by these methods. Secondly, to
collect the maximum number of spectra possible during the eluting time of
a peak, FA and PCA are inﬂuenced by the rate of spectral acquisition
eventually determined by the instrumental background. The
orthonormalization procedure is not affected by this scanning speed
limitation since only one or a few spectra need to be acquired across the

peak proﬁle.

Analogous to other curve resolution techniques, however, the accuracy of
the proposed method will also suffer if the uniqueness of reference spectra
are not realized; in such cases some false positive results such as that
observed for sample B may be encountered. Therefore, the spectrometer's
resolution and S/N ratio can play an important role in this respect. Higher
quantitative accuracy would have deﬁnitely been obtained had an FT/IR
instrument been employed in place of the dispersive one used in this work.
The simultaneous use of complementary mass spectral data, nonetheless,

can greatly attenuate this problem.

As was shown in the previous section, a remarkable improvement in
quantitation is quite often realized by imposing a non-negativity constraint

(NNC) on the ﬁt. Table 5.17 lists the results when a ternary mixture is

84

T2

 

 

we.-.“ - Fm...-

Table 15.17 Results of imposing non-negativity constraint; obs. (actual)

 

 

 

Reference First Second Third
compounds run run,NNC1 Run,NNC2
Hexane 39 (30) 27 (30) 28 (30)
Acetone - 0.05 (0.0) - -
Acetonitrile - 0.09 (0.0) - -
Methyl acetate - 0.01 (0.0) - -
Methylene chloride - 0.01 (0.0) - -
Chlorobenzene - 0.14 (0.0) - -
Bromobenzene 0.01 (0.0) - 0.09 (0.0) -
Nitrobenzene - 0.00 (0.0) - -
Benzene 0.47 (30) 0.35 (30) 0.32 (30)
Toluene 0.79 (40) 0.47 (40) 0.40 (40)
Relative

errors (x103 ) 8.69 9.8 9.8

 

examined against a set of ten references. The mixture is composed of
hexane (30%), benzene (30%), and toluene (40%). Data in table 5.17 indicate
that after the ﬁrst projection (1 st run) six components receive negative
coefﬁcients. The mixture is identiﬁed as containing hexane, benzene and
toluene but the quantitative results are not as accurate. In the second run
with the NNC imposed, not only a signiﬁcant quantitative improvement is
achieved; but also the last component not in the mixture turns negative
leaving exclusively those components that are actually present in the
mixture. The application of the NNC in the third run results in yet further
quantitative accuracy. Note that the relative error remains fairly constant

indicating a performance of a good ﬁt in all three runs.

The results obtained in this section demonstrates that, as applied to the
resolution of overlapping peaks, the orthonormal method offers several
advantages. The method utilizes the whole multivariate IR spectral
information delivered by the instrument. It is an effective curve resolution
tool without requiring limiting assumptions on the chromatographic peak
proﬁle or resolution. However, an a priori knowledge of a small list of
possibilities is required. In cases where a peak component is not
represented in the reference list, a relative error of at least an order of
magnitude higher is obtained pointing to the absence of the component
from the list. Even under these circumstances, the compound in the
reference list most similar to the desired compound will be obtained thus
providing information about its type. The technique can be used in
conjunction with a large reference space arranged in the order of retention

times, and a small reference list within a given retention time window may

 

 

be examined for a given peak. In fact, it may be possible to adopt this

method to operate in real time as data are collected.

CONCLUSION AND FUTURE PROSPECTS

It was shown in the previous sections that the orthonormal method offers
several unique advantages for the automated spectroscopic identiﬁcation of
organic compounds. Namely, the method is computationally efficient and
allows for the analysis of both pure and mixture samples with the same
amount of computational efforts. The effectiveness of the orthonormal
procedure in dealing with mixtures appears to be well suited for
applications involving various chromatographic-spectrometric regimes.
The method is also capable of delivering both qualitative and quantitative
information with a single shot analysis of either pure or mixed samples.
Moreover, the technique independency aspect of the orthonormal algorithm
allows for the synergistic use of spectroscopic data from several techniques
to enhance conﬁdence on the ﬁnal outcome of the analysis.

However, since the primary objective of this research have been to explore
the extent to which the theoretical advantages associated with the use of
orthonormalized reference spectra can be met in practice, all of the
experiments have been carried out based on a small library. Little attention
has been given to the size of the library or other parameters such as
computational needs or the required computer time which may place a
limit upon the successful application of the method to large databases. It
is, therefore important to address some of these issues in order to provide

some insight into the nature of the factors which might cause the

' 87

rn

rn

orthonormal method to collapse. A number of these factors, from a strictly
theoretical point of view, are discussed here beginning with the possible
application of the orthonormal method as a search algorithm for a large

library.

Generally, for a search system to be useful, the results must be reliable for
large databases. In other words, it must be possible to distinguish between
a large number of library entries many of which are similar. The Gram-
Schmidt orthogonalization is essentially a normalized difference operation.
Similar components are selectively removed from each subsequent
reference vectors (spectra). In principle, the successive difference method
works as long as the number of vectors, that is reference spectra, is less
than the number of dimensions which is the number of data points in the
spectrum. ‘Problems can arise when there are too few dimension for doing
large data sets. By analogy, one should consider a three-dimensional
vector. If the Gram-Schmidt operation is performed on a one-dimensional
vector, the basis set is then a line. If two vectors are used to form the basis,
the basis is then a plane. The Gram-Schmidt then measures three-
dimensional vectors that lie perpendicular to that plane. Only the third
dimension is measured. If a three-dimensional vector is used for the basis
set, the basis is a volume. If one has only three dimension, there in no
orthogonal component to that volume. By analogy the same is true for 100
or 1000 dimensions. Therefore, the major question may be at this point
whether it is proﬁtable to have 1000 dimensions for each, for example,

infrared spectrum.

S}

0U

 

Another issue of concern may be the computational efforts demanded by the
orthonormal method. Note that this concern only applies to the
orthonormalization step at the time of library inception which is done only
once for a given library. The analysis step is, in fact, more efﬁcient than
other methods such as absolute differences and dot products. Once a
library is orthonormalized, the method allows for a more general analysis
without an additional sacriﬁce in computational efficiency. The generality
of the analysis is achieved by treating each sample as if it were a mixture
which also makes the method capable of dealing with the overlapped peaks
in GC-IR applications.

However, there are several points that are debatable with regard to the
concerned expressed earlier. One important point is that the computation
in the orthonormal method is kept at a minimum by partitioning the overall
procedure into a one-time only orthonormalization step and an
identiﬁcation step which is repeated for every sample. This is a favorable
division of labor because the orthonormalization requires ~ (NPTS)3 ﬂoating
point operations (FLOPS), whereas ~ (NPTS)2 FLOPS are required in the
identiﬁcation step.

Secondly, the concern with spectrum dimensionality in the orthonormal
method is to insure that the linear independency assumption is not
violated. Under these circumstances, however, the method yields accurate
qualitative and quantitative information as opposed to typical search
systems which only produce some list of possible candidates. It was pointed
out in an earlier section that methods such as dot products and absolute

differences make no attempt to insure that their reference library is

$

linearly independent. Thus, it is not uncommon for these procedures to be
applied in cases where the number of points in the reference spectra are
considerably less than the number of compounds in the library. Such
applications are bound to fail in cases where two or more compounds have

similar spectra, regardless of which identiﬁcation algorithm is employed.

It is also important to point out that there are a number of steps which may
be taken to reduce the severity of the problem of computational demand for
the orthonormalization part of the method. For instance, a library can be
built gradually; that is, each additional spectrum can be orthonormalized to
an existing library. In this way, the calculations can be spread out over
time. Of course, the computational efforts involved in orthonormalizing
each spectrum increases with the size of the library and may have to
eventually be done on a Mainframe. Furthermore, the use of preﬁlters and
specialized sublibraries will allow for a signiﬁcant reduction in the size of
any one library without a sacriﬁce in information content. Also notice that
with regard to the analysis of mixtures resulting from incomplete
separation, the size limitation is not a critical factor. Small libraries based
on retention index windows which are likely to include a small subset of the
compounds from the library may be selected and subsequently

orthonormalized.

Finally an important factor for the evaluation of any search system is the
amount of computer time necessary for a typical analysis. Since the
algorithm was originally written in BASIC to run on an Apple
microcomputer, the analysis times were fairly long. A better estimate of

the required computer time may be made from the results of an experiment

9)

 

 

 

performed recently. The algorithm was coded in C language to run on a
VAX-station 3200. A data set containing 108 primary mass spectra
acquired on a Finnigan TSQ-70 triple quadruple mass spectrometer was
orthonormalized. Every compound in the data set was then used as an
unknown and was subsequently analyzed using the orthonormal method.
The total CPU elapsed time for the analysis of all 108 compounds was 13.54
minutes. This amounts to approximately 7 .7 seconds per analysis .
Therefore, the analysis time for a library of 1000 compounds would be on the
order of a few minutes. However, the real answer to many of the concerns
discussed above can only be obtained through experimental investigation of
the performance of the orthonormal method for large data bases in the

future.

 

 

10.

11.
12.

13.

14.

15.

16.

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