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This is to certify that the
thesis entitled
EFFECT OF STORAGE TEMPERATURE AND PACKAGING
SYSTEMS ON THE QUALITY OF PACKAGED
FROZEN CARROTS

presented by

DARIO MARTINO

has been accepted towards fulfillment
of the requirements for

_MA.S_IEB__ degree in W

Major professor

Date g/Z/ﬂs

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EFFECT OF STORAGE TEMPERATURE AND PACKAGING SYSTEMS ON
THE QUALITY OF PACKAGED FROZEN CARROTS

by

Dana Martino

A THESIS

Submitted to
Michigan State University
in partial fulﬁllment of the requirements
for the degree of

MASTER OF SCIENCE

Department of Packaging

2003

ABSTRACT

EFFECT OF STORAGE TEMPERATURE AND PACKAGING SYSTEMS ON
THE QUALITY OF PACKAGED, FROZEN CARROTS

By

Dario Martino

Frozen packaged vegetables may be exposed to ﬂuctuating environmental
conditions during storage. This may result in an increase in amount of frost in the
headspace of a packaged frozen vegetable, and thus consumer avoidance, and
problems with weight compliance may occur.

The main objective of this work was to understand the origin of frost ice and
test the effect of frozen storage and packaging systems on product quality. A
secondary objective was to develop a mathematical relationship to predict frost
ice formation.

Baby cut carrots were blanched, frozen and stored at -20 °C in nylon/PE and

LDPE bags and vacuum and atmospheric ﬁlling processes, as well as perforated
and non-perforated bags were used. Samples were drawn at monthly intervals for
6 months and evaluated for product weight loss, product color and product
appearance. Half of the carrots were transferred to a retail refrigeration unit one
week prior to evaluation.

Frost ice was found to increase with storage time and selection of material.
Frost decreased when the permeability of the package material was altered using
perforations. The less frost ice, the more positively the carrots were perceived by
a paneL

The frost ice data ﬁt well (R2>0.70) as a quality parameter using a first order
kinetic-like expression. Using data from different packaging systems and storage

temperatures, model parameters were calculated to predict frost formation.

To my dear wife and daughter.

ACKNOWLEDGEMENTS

I would like to thank Dr. Bmce Harte, my major advisor, for his endless
support and valuable advice throughout the duration of this research. I would
also like to thank Dr. Gary Burgess for his precious help for data modeling, Dr.
Janice Harte for her valuable advice on sensory evaluation design and Dr.
Jerry Cash for his insightful comments, as well as for serving as my
committee advisors. Likewise, I need to thank the Center for Food and
Pharmaceutical Packaging Research (CFPPR) for providing part of the funds

for this work.

I also appreciate the help from Ms. Fang Li from the Statistical
Consulting Service for her invaluable help performing the statistical analysis of
the data. Dr. Mohammad Siddiq, in charge of the Food Science Processing
Plant, Mr. Mike Hamelin, from Michigan Grocers and Mr. Charles Lawlar,
manager of Country Markett Store are graciously thanked as well. Special
thanks are also to faculty, staff and students in the School of Packaging and
the Food Science department for the support and help with the sensory

evaluation.

TABLE OF CONTENTS

INTRODUCTION ....................................................................................................... 1
CHAPTER 1 .............................................................................................................. 5
LITERATURE REVIEW ............................................................................................. 5
1. Freezing vegetables .............................................................................. 5
1.1. Pre-freezing processes .................................................................. 5

1.2. Freezing systems - Freezing rate and quality ................................ 6

1.3. Crystal formation ............................................................................ 8

2. Frozen Storage and Retail .................................................................... 9
2.1. Temperature ﬂuctuations and crystal growth ............................... 10
2.1.1. Water migration ..................................................................... 10

2.1.2. Irreversibility of water removal — in-pack desiccation ............ 11

2.2. Product Effect .............................................................................. 13

2.3. Process Effect .............................................................................. 14

2.4. Packaging Effect .......................................................................... 15
2.4.1. Material and Packaging types ............................................... 15

2.4.2. Inﬂuence of package parameters on quality ......................... 17

2.4.3. Quality changes associated with packaging ......................... 19

2.5. Combined effects and mathematical models ............................... 20
2.5.1. Kinetic models ....................................................................... 20

2.5.2. Temperature distribution and ﬂuctuating temperature ........... 22
CHAPTER 2 ............................................................................................................ 25
MATERIALS AND METHODS ................................................................................. 25
1. Materials .............................................................................................. 25
1.1. Carrots ......................................................................................... 25

1.2. Packaging Materials ..................................................................... 25

2. Processes ........................................................................................... 27
2.1. Freezing and Frozen storage of the samples ............................... 27

3. Methods .............................................................................................. 27
3.1. Temperature monitoring ............................................................... 27

3.2. Frost and glassy ice content ........................................................ 3O

3.3. Moisture content determination .................................................... 32

3.4. Color Assessment ........................................................................ 32

3.5. Product appearance — Sensory Evaluation .................................. 33
CHAPTER 3 ............................................................................................................ 38
RESULTS AND DISCUSSION ................................................................................ 38
1. Frost content and origin ....................................................................... 38

2. Temperature proﬁle ............................................................................. 48
3. Moisture content .................................................................................. 51
4. Color evaluation .................................................................................. 52
5. Appearance - Sensory evaluation ...................................................... 54
6. Model for frost ice formation ................................................................ 58
CHAPTER 4 ............................................................................................................ 62
CONCLUSIONS ...................................................................................................... 62
APPENDIX A .......................................................................................................... 65
APPENDIX B ........................................................................................................... 69
APPENDIX C .......................................................................................................... 75
APPENDIX D .......................................................................................................... 82
APPENDIX E ........................................................................................................... 88
APPENDIX F ........................................................................................................... 91
BIBLIOGRAPHY ..................................................................................................... 99

vi

LIST OF TABLES

Table 1. Typical freezing processes and heat transfer mediums, and their
coefﬁcients * ............................................................................................. 7
Table 2. Properties of selected barrier ﬁlms used for frozen food systems" .. 16
Table 3. Bag Materials Speciﬁcations ............................................................ 26
Table 4. Parameters for impulse sealing ........................................................ 26
Table 5. Maximum predicted amount of moisture transported through package
at 100% RH and 0 °C after X months ..................................................... 43
Table 6. Average amount of frost (grams) after X months for air freezer/retail
freezer sub.treatment .............................................................................. 44
Table 7. Average amount of frost (grams) after X months for air freezer sub-
treatment ................................................................................................. 44
Table 8. Average glassy ice amounts (%) for unots in bags exposed to air
freezer/retail freezer and air freezer sub-treatment after X months ........ 46
Table 9. Weight loss in carrots held in a vacuum oven (2’14 in of Hg) and at
atmospheric conditions ........................................................................... 48
Table 10. Average temperatures for air freezer (AF) and air freezer/retail
freezer (AF/RF) treatments ..................................................................... 50
Table 11. Frost ice and moisture content amounts of commercial packages of
frozen baby out carrots ........................................................................... 52
Table 12. Model parameters for frost formation for packed carrots under air

freezer (AF) and air freezer/retail freezer (AF/RF) sub-treatments ......... 60

vii

Table A 1. Average frost and glassy ice amounts (%) for carrots in bags
exposed to air freezer/retail freezer sub-treatment after X months ......... 65
Table A 2. Average frost ice amounts (%) for carrots in bags exposed to air
freezer sub-treatment after X months ..................................................... 65
Table A 3. Average Whiteness Index values for carrots in bags exposed to air
freezer/retail sub-treatment after X months ............................................. 66
Table A 4. Average Whiteness Index values for carrots in bags exposed to air
freezer sub-treatment after X months ..................................................... 66
Table A 5. Average rating scores for appearance of carrots upon package
opening (frost on them) at month 6 ......................................................... 67

Table A 6. Average rating scores for overall appearance of carrots at month 6

................................................................................................................ 67
Table A 7. Average rating scores for appearance of carrots (A-NPE, air

freezer sub-treatment) upon package opening (frost on them) at two

different months ...................................................................................... 68

Table A 8. Average rating scores for overall appearance of carrots (A-NPE, air

freezer sub-treatment) at two different months ....................................... 68
Table B 1. Treatment code number and description ...................................... 69
Table B 2. Sub-treatment code number and description ................................ 69

Table C 1. Water vapor transmission rate (WVT R) data for the LDPE material
at three temperatures: 20, 15 and 10 °C ................................................. 75
Table C 2. Water vapor transmission rate (WVT R) data for the nylon/PE

multiplayer material at three temperatures: 20, 15 and 10 °C ................. 76

viii

Table C 3. Average permeability values for the Nylon/PE multilayer material at
three temperatures: 20, 15 and 10 °C ..................................................... 77
Table C 4. Permeability data for the Nylon/PE multilayer material at 100% RH
................................................................................................................ 78
Table C 5. Permeability values for the LDPE material at three temperatures:
20, 15 and 10 °C ..................................................................................... 78
Table C 6. Permeability data for the LDPE material at 100% RH .................. 78
Table C 7. Activation energies and permeability values for plastic ﬁlms at 0 °C
and 100% RH ......................................................................................... 80
Table C 8. Maximum predicted amount of moisture transported through

package at 100% RH and 0 °C after X months ....................................... 81

Table D 1. Average frost and glassy weight values (grams) for air freezer sub-
treatment for different months ................................................................. 83
Table D 2. Average net weight values (grams) for air freezer sub-treatment for
different months ...................................................................................... 83
Table D 3. Average calculated initial water amounts in carrots (grams) for air
freezer sub-treatment for different months .............................................. 83
Table D 4. Average calculated ﬁnal water amounts in carrots (grams) for air
freezer sub-treatment for different months .............................................. 84
Table D 5. Average calculated dry matter weight in carrots (grams) for air
freezer sub-treatment for different months .............................................. 84
Table D 6. Average calculated ﬁnal moisture content in carrots (in %) for air

freezer sub—treatment for different months .............................................. 84

Table D 7. Average experimental ﬁnal moisture content in carrots (in %) for air
freezer sub-treatment for different months .............................................. 85
Table D 8. Average frost weight values (grams) for air freezer/retail sub-
treatment for different months ................................................................. 85
Table D 9. Average net weight values (grams) for air freezer/retail sub-
treatment for different months ................................................................. 85
Table D 10. Average calculated initial water amounts in carrots (grams) for air
freezer/retail sub-treatment for different months ..................................... 86
Table D 11. Average calculated ﬁnal water amounts in carrots (grams) for air
freezer/retail sub-treatment at different months ...................................... 86
Table D 12. Average calculated dry matter weight in carrots (grams) for air
freezer/retail sub-treatment for different months ..................................... 86
Table D 13. Average calculated ﬁnal moisture content in carrots (in %) for air
freezer/retail sub-treatment for different months ..................................... 87
Table D 14. Average experimental ﬁnal moisture content in carrots (in %) for

air freezer/retail su b-treatment for different months ................................ 87

Table E 1. Model parameters for frost formation for packed carrots under air
freezer and air freezer/retail sub-treatments (straight line) ..................... 88
Table E 2. Model parameters for frost formation for packed carrots under air

freezer and air freezer/retail sub-treatments ........................................... 89

LIST OF FIGURES
Images in this thesis are presented in color
Figure 1. Experimental Layout: treatments (packaging systems), and sub-
treatments (air freezer or air freezer/retail) ............................................. 28
Figure 2.Temperature data logger location inside the freezer and retail
freezer cabinet ........................................................................................ 29
Figure 3. Reference standards for carrots upon package opening ................ 34
Figure 4. Reference standard for overall appearance of carrots after frost
removal ........... 34
Figure 5. Appearance of frost ice on atmospherically packed samples ......... 39
Figure 6. Comparison of frost ice during time for “air freezer” bags (V-NPE
bag data not included) ............................................................................ 40
Figure 7. Comparison of frost ice during time for “air freezer/retail freezer”
bags (V-NPE bag data not included) ....................................................... 40
Figure 8. Appearance of glassy ice on vacuum packed samples .................. 47
Figure 9. Temperature proﬁle from retail freezer cabinet and air freezer..49
Figure 10. Temperature at top and bottom in retail freezer cabinet ............... 49
Figure 11. Color change in carrots exposed to air freezer/retail freezer sub-
treatment ................................................................................................. 53
Figure 12. Color change in carrots exposed to air freezer sub-treatment ...... 53
Figure 13. Results of overall appearance of baby cut carrots (after frost ice
removed) from packaging systems within the same month (month 6) ....55
Figure 14. Results of appearance of baby cut carrots upon package opening
(frost on product) from packaging systems within the same month (month

6) ............................................................................................................. 55

xi

Figure 15. ANOVA and multiple comparison results of baby cut carrots upon
package opening (frost on product) from packaging systems within month
6 .............................................................................................................. 56
Figure 16.Results of overall appearance of baby out carrots (after frost ice
removed) for A-N PE for two months ....................................................... 57
Figure 17. Results of appearance of baby cut carrots upon package opening
(frost on product) for A-NPE for two months ........................................... 57
Figure 18. Kinetic-like model for frost formation for bags in air freezer/retail
freezer sub-treatment .............................................................................. 59
Figure 19. Kinetic-like model for frost formation for bags in air freezer sub-

treatment ................................................................................................. 59

Figure C 1. In P at 100% RH VS. 1/T .............................................................. 79

Figure E 1. Straight-line model for samples exposed to air freezer/retail sub-
treatment ................................................................................................. 88

Figure E 2. Straight-line model for samples exposed to air freezer sub-
treatment ................................................................................................. 89

Figure E 3. Models for frost formation for bags in air freezer/retail sub-
treatment ................................................................................................. 90

Figure E 4. Models for frost formation for bags in air freezer sub-treatment .. 90

Figure F 1. Temperature proﬁle of Air freezer sub-treatment - All 6 Months ..92
Figure F 2. Temperature proﬁle of Air freezer/Retail freezer sub-treatment —

month 1 ................................................................................................... 93

xii

Figure F 3. Temperature proﬁle of Air freezer/Retail freezer sub-treatment —
month 2 ................................................................................................... 94
Figure F 4. Temperature proﬁle of Air freezer/Retail freezer sub-treatment —
month 3 ................................................................................................... 95
Figure F 5. Temperature proﬁle of Air freezer/Retail freezer sub-treatment —
month 4 ................................................................................................... 96
Figure F 6. Temperature proﬁle of Air freezer/Retail freezer sub-treatment —
month 5 ................................................................................................... 97
Figure F 7. Temperature proﬁle of Air freezer/Retail freezer sub-treatment -

month 6 ................................................................................................... 98

xiii

 

 

INTRODUCTION

Industrial freezing as a means of food preservation has been used in
the United States for several decades. The original quality of the food product
is not improved by freezing; instead, temperature reduction results in the
retardation of the processes that reduce product quality.

Moisture migration from the product is the main phenomenon which
occurs in frozen foods, and thus has major effects on the chemical and
biochemical properties of frozen foods (Pham and Mawson, 1997). It
manifests itself in several ways: moisture loss by sublimation, moisture
absorption, and redistribution in foods or food components, frost ice formation,
recrystallization, and drip loss during thawing. In all these cases, and
especially when frost ice is formed, the appearance of the product is
negatively affected. Consumers may avoid buying frozen products when they
see frost ice. Further, since in-package ice is discarded in net weight control,
serious problems with label compliance can occur.

Packaging is one of the most important factors that can effect the ﬁnal
quality of the frozen product. Retail packaging systems used by the frozen
food industry can be classiﬁed into three categories depending on the product
end-use: a) atmospheric ﬁlling into simple bags and skin-packaging for loose
uncooked product or cuts; b) modiﬁed atmosphere packaging (MAP) and
vacuum packaging, for precooked products, and c) ovenable and dual-
ovenable packaging for prepared foods. Frozen, minimally processed
vegetables, are typically packaged in atmosphere, in bags and are often

found to develop frost ice.

It is widely accepted that quality deterioration in foods during frozen
storage is temperature dependent. Fluctuations of temperature are believed to
negatively affect the product. During post-freezing, storage and handling, two
factors: temperature ﬂuctuation and packaging, converge. Jul (1984) stated,
“The importance of temperature ﬂuctuations and handling abuses in the retail
stores are clearly an area in need of further clariﬁcations”. There exists limited
data regarding the effect of retail conditions on quality of frozen, packaged
vegetable products. Temperature ﬂuctuations during retail are believed to
represent an important issue affecting the quality of the product since they are
often large and rather frequent. In turn, packaging systems can inﬂuence
these quality changes and can help reduce their negative effects.

Semi-empirical kinetic models are often developed to describe the
change in quality of frozen foods. Bhattacharya et al. (1988), Lanari and
Zaritzky (1991), Chen et al. (1989), and Lanari et al. (1993) used kinetic
models to explain color changes. Drip and cooking losses were modeled
using kinetic relationships (Bhattacharya et al., 1988), and water holding
capacity and lipid autooxidation (Chen et al., 1989). Since the rate of quality
loss during the post-freezing phase (i.e.: distribution, storage, retail cabinet
display, in-home freezer storage) is temperature dependent, methods are
often developed that can predict or determine the temperature distribution
within a product. Among them, numerical models are regarded as the most
accurate, and yet most difﬁcult to develop. Heat transfer analysis has been
approached in several different ways. Sastry and Kilara (1983) modeled the

thermal response of stacks of frozen peas exposed to energy-conservation

practices involving di-thermal storage cycles. Zuritz and Singh (1985)
simulated the effect of storage temperature changes through a simulated
frozen-food distribution channel using a ﬁnite element model. Mittal and
Parkin (1985) studied the response of frozen foods in insulated containers to
a step change in the ambient temperature. Moureh and Derens (2000)
presented a ﬁnite-volume heat transfer model which predicted temperature
within a pallet as a function of time, storage conditions, product initial
temperature, palletization pattern and thermal characteristics of the products
and packaging.

Though numerical models have been widely used, the most fruitful
work is often in ﬁnding simple, accurate equations for food products that can
be used with relatively little effort. In addition, of the models cited, only few
take into account the effect of packaging. Some of the models cited include
some quality parameters, though none included frost ice formation as a
quality parameter.

In this study, frost ice development, product color change and general
appearance of frozen baby cut carrots (a widely available product) was
investigated from a packaging perspective.

The importance of this work is in exploring the effects of different
packaging systems on quality attributes of a frozen product during the post-
freezing phase.

Speciﬁc objectives include:

a) To describe the physical phenomena that occurs within the

headspace of the packaged frozen vegetable during post-freezing conditions.

b) To study the effects of four packaging systems on product quality.
Nylon/PE multilayer bags using atmospheric and vacuum packaging and
perforated and non-perforated LDPE bags; two post-freezing conditions:
frozen storage and frozen storage and retail temperatures; and time, a six
month storage period, all were investigated as to the quality of the frozen
packaged baby out carrots in terms of product color, general appearance and
frost ice formation.

c) To develop mathematical relationships using curve-ﬁtting
experimental data, that can be used to evaluate the potential quality damage

of a food product exposed to similar post-freezing conditions.

CHAPTER 1

LITERATURE REVIEW

1. Freezing vegetables

1.1 . Pre-freezing processes

Vegetables are prepared for freezing by cleaning and washing to
remove ﬁeld dirt and to reduce microbial contamination. Grading, material
handling, peeling and inspecting are followed by blanching. The blanching
process consists of heating the vegetable for a sufﬁcient time to inactivate
most of the enzymes responsible for off-ﬂavors and undesired product color
change during storage. Oftentimes, vegetables are heated at 190 °F (88 °C)
for several minutes or a half-minute at 212 °F (100 °C)(Desrosier and
Tressler, 1977). Presence of catalase and peroxidase in blanched product
has been used as common indicators even though they have not been
speciﬁcally indicted as causative agents of frozen vegetable deterioration.
Blanching times take into account the type and size of the vegetables. The
time recommended for vegetables also depends on the ﬁnal use of the frozen
product. For example, “boil-in-the—bag” vegetables are essentially fully cooked
before freezing since little or no additional cooking takes place during the
heating process prior to use. Vegetables are blanched either in steam or hot
water. Hot water at 200 to 205 °F (93 to 96 °C) is mainly used for large
vegetables (i.e.: artichokes) while steam is used for smaller ones. Carrots are

usually blanched using steam; blanch times vary from 2 to 8 min. depending

on size, maturity and texture. For example, vegetables with 0.375 in diameter

often require 2 to 3 minutes in hot water (Desrosier and Tressler, 1977).

1.2. Freezing systems — Freezing rate and quality

A convenient classiﬁcation of food freezing systems can be made
according to the rate of freezing or the rate of movement of the ice front from
the product surface to the thermal center of the food. The freezing rate
depends on the thermal properties of the food and the ratio of the freezing
medium to the food product surface (medium heat transfer coefﬁcient).
Effectively, the freezing rate of a small product (large surface area to volume
ratio) is controlled by external freezing conditions, whereas that of a large
product (low surface area to volume ratio) is controlled more by the internal
characteristics of the product (George, 1997). Table 1 lists heat transfer
coefﬁcients of common freezing processes.

The faster the formation of ice (faster freezing rate, higher heat transfer
coefﬁcient), the smaller and more numerous are the ice crystals. An
operational deﬁnition of freezing rate in cellular systems is that fast freezing
produces internal freezing in cells and slow freezing produces cellular
dehydration and only extracellular ice. When there is a low rate of heat
removal with either low or high cell water permeability, the rate of change of
the unfrozen matrix is slow, and water can transfer from the interior fast
enough to minimize internal supercooling. Under these conditions the cell
dehydrates, with the loss water becoming external ice crystals. It should be
noted that internal cell freezing is more likely to happen in animal cells than in

vegetable cells (Haard, 1997).

Table 1. Typical freezing processes and heat transfer mediums, and their

coefficients *

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

cream freezer)

 

 

mixing of food product

 

I— Type of Freezer Conditions H (W/m7 K) I
Cold store — T i Still air 5-10 _'_—"
Air-blast freezer Air velocity-:25 mls _ I I 17-ZO__—_4

Aux/WE'RE" "— 26-30
Tunnel freezer Counterflow of air and product 15.50
Contact freezer Contact to cold plate 50-120
Fluidized-bed freezer Suspending airstream 80-120
CWOQGFII‘3 freezer (liquid Gas zone (prefreezing) 40-60
N2) Spray zone (freezing) 100—140
Liquid immersion Circulating brine 60—90

Specialized refrigerant 500-600

Scraped-surface heat Contact to cold surface and 1500-2000
exchanger (Le. ice

 

 

* adapted from George (1997).

Although the relationship between freezing rates and product quality is

not totally clear for many food products, the cumulative effect is very important

for vegetables and fruits. Considerably better results (i.e. in texture) were

reported when freezing fruits very quickly rather than by slow freezing (Jul,

1984).

It is inevitable that a portion of water in a product, will evaporate during
air blast freezing (IIR, 1986). The amount of evaporated water will be smaller
at a higher freezing rate, provided all other factors are constant. When the
product is enclosed in a water vapor proof package before freezing, the
proportion of moisture which escapes from the package will be negligible.
However, frost may be deposited on the inside surface of the package by the
same amount as moisture evaporating from the product. In particular, this is
found when there is an air gap in the order of millimeters between the surface
of the product and the intemal surface of the package (IIR, 1986). Faster
freezing methods are more effective in reducing this frost formation by
reducing the surface temperature of the product quickly to a value where the
rate of moisture evaporation or sublimation is low.

Since commercial operations strive to reduce the time spent on
freezing, these operations tend to use faster methods. lndustrially, diced,
sliced and baby cut carrots are individually quick frozen (IQF) in ﬂuidized

freezing beds (Desrosier and Tressler, 1977).

1.3. Crystal formation
Crystal formation results from a change of state of the water in the
food. Temperature is the fundamental descriptor of the state of any system.
At high temperatures, molecular motions tend to be more rapid, and collision
frequencies for the same molecular densities are greater. As temperature
falls, the molecular motions decrease (Desrosier and Tressler, 1977).
For the temperature to decrease, heat may have to be removed from a

product. Transformation from liquid water to crystalline ice is a typical ﬁrst-

order phase transition. In foods, the presence of a soluble solute alters the
phase relationships between ice and water due to a vapor pressure reduction

effect (colligative effect), which lowers the freezing point of the water.

2. Frozen Storage and Retail

The temperature history of the frozen product after freezing is the most
important factor determining product quality. Zaritzky et al. (1982), cited two
types of temperature changes that frozen product may be exposed to during
post-ﬁeezing: 1) ﬂuctuations in the temperature of the storage chamber, and
2) sudden increases in temperature during loading and unloading of the
product during transportation and distribution. Temperature ﬂuctuations such
as those previously cited are unlikely to pose any problem as long as they are
sporadic and small, and an “effective average” is maintained under an
appropriate temperature. However, temperature ﬂuctuations during the retail
sales period represent an important factor affecting the quality of the product
since they are often large and rather frequent (Jul, 1984).

Gortner et al. (1948) presented one of the eariy studies dealing with
ﬂuctuation in the storage temperature. They showed that an increase in the
rate of desiccation at the surface of the product occurs, especially when
exposed to temperatures above zero °F.

Quality changes and moisture loss from the product (or in-package
product desiccation) during frozen storage are more serious than what occurs
in freezing because of the storage duration (Bevilacqua and Zaritzky, 1982;
IIR, 1986). The combined effect of temperature ﬂuctuation (time-temperature

tolerance of product), product type and initial quality, process and packaging

system determine the ﬁnal quality of the frozen product, and should be

addressed when developing mathematical models to predict quality changes.
The effect of these variables on common product quality parameters

and product desiccation (in-package desiccation) is explained in the next

section.

2.1. Temperature ﬂuctuations and crystal growth

Change in shape and size of the ice crystal in frozen foods may occur
due to periodic variation in temperature during storage. The greater the
amplitude of these variations, the greater the change and rate of cellular
destruction. Crystals grow by the adherence of water molecules to formed
crystals. Small crystals are unstable compared to larger ones and will grow
where free water molecules are present (Reid, 1983). Even when small ice
crystals are formed by very rapid freezing, crystal growth during storage can
take place and damage the cell wall. It is well known that such growth will be
greatly accelerated by ﬂuctuating temperatures. Several researchers (Martino
and Zaritzky, 1988; Martino and Zaritzky, 1989); (Bevilacqua and Zaritzky,

1982) found crystal growth to occur under ﬂuctuating temperature conditions.

2.1.1. Water migration
In addition to changes in size and number of ice crystals, storage
conditions can markedly inﬂuence the location of water within a product. This
is a consequence of the temperature dependence of the vapor pressure of
water. Water vapor will transfer from a region of higher vapor pressure to a

region of lower vapor pressure. Since temperature ﬂuctuation cannot be

10

avoided in storage, temperature gradients will exist within products. These
gradients will reverse in direction during a cyclical pattern. This does not
mean, however, that the moisture transfer also reverses; especially at the
surface of the product and within internal cavities. When the void volume is
colder than the solid volume, moisture easily transfers within it. It is more
difﬁcult for the moisture to transfer back to the initial location when the void
volume is warmer than the solid volume, and so there is an overall tendency
for moisture to move toward surfaces and leave the denser regions of the
product. If there is a void space around the product in the package, moisture
will transfer into this space and tend to accumulate on the surface of the

product and the internal surface of the package (Jeremiah, 1996).

2.1.2. Irreversibility of water removal - in-pack desiccation

Another phenomenon that may magnify the effect of temperature
ﬂuctuations is irreversibility. When a capillary porous food is packaged in a
vapor barrier ﬁlm, a drop in the storage temperature cools the wrap, causing
water vapor to frost onto it, and a decrease in the water vapor pressure.
Moisture inside the pores sublimes and diffuses to the package wall until the
food itself equilibrates to the new temperature. When the environment warms
up again, the ice on the package sublimes but does not diffuse back into the
pores, since the pores may have partially collapsed or shrunk due to loss of
the water that gave them structural strength. In addition, ice may bridge some
of the pore openings, preventing further resorption. The loss of unfrozen water
with consequent ion concentration and chemical penetration, may also impair

the water binding capacity of the food. Thus, after a cooling-wanning cycle,

11

there may be a net migration of moisture from the inside of the food toward

the surface and the package wall. This frost ice is a common feature in

packaged food and may occur even in foods that are wrapped after freezing

(Erickson and Hung, 1997). This phenomenon is commonly known as in-pack

desiccation, and its mechanism was summarized by the lntemational Institute

of Refrigeration (IIR, 1986) as follows:

a)

b)

“A layer of air between product and package is subject to temperature
variations. If the outside temperature decreases, die temperature of the
inside surface of the packaging will drop below the product surface
temperature and ice from the product will sublime and condense on the
inside the package.

When the ambient temperature increases, the process is reversed and

the water vapor will condense on the product surface.

0) As the cooling-heating cycle is maintained, the crystals on the product

surface tend to follow the package temperature more closely (due to
quicker thermal response) than the mass of the product, and this
results in further sublimation of ice from the product to the package
surface. Frost in packages of frozen foods can amount to 20 % or even
more of the product weight. Furthermore, desiccation of the surface
layers exposes an increased surface area, and thus greater access to
oxygen. Thus, the rate of the quality deterioration at the product
surface may increase”.

The effect of temperature ﬂuctuation depends on the average storage

temperature —effective temperature— (Jul, 1984). The effect of temperature

12

ﬂuctuation on desiccation increases with higher storage temperatures, due to

increased ice sublimation.

2.2. Product Effect

The most important factors affecting product water loss during storage
are: the surface to volume (or weight) ratio, product surface condition,
physiological state of the product, product variety, production area and
practices, and chemical potential of the water in the product.

The surface to volume (or weight) ratio gives an indication of the
potential for product water loss. Small pieces of food have a high area to
volume ratio and are more susceptible to moisture loss. Most produce
contains more than 80 % water (Wills et al., 1989), and their volume (or
weight) could give a fair estimate of the amount of water that it contains.
Product surface area controls rate of water loss, according to Fick’s law of gas

diffusion, as described by Sastry (1985):

J = KHzo A (F’s - P») (1)
Where:

J: rate of water diffusion through the surface of the product (Kg 3")

A: product surface area (m2)

PS: water vapor pressure at product surface (KPa)

Pm: environmental water vapor pressure (KPa)

Kmo: area-based overall mass transfer coefﬁcient of water from the

‘ KPa ’1). The resistance coefﬁcient involves

product (Kg rn‘2 s‘
resistance of the boundary layer“, surface of the product; intercellular air

spaces, cell wall, cytoplasm, cell membranes, etc.

13

The condition of the product surface directly affects the A term of
equation (1). The driving force for water diffusion is represented by the water
vapor pressure difference (PS - Pm), and the resistance by the reciprocal of the
overall mass transfer coefﬁcient KHzo. The following factors can be regarded
as inﬂuencing either the driving force or the resistance term.

The physiological state, and variety of the product can affect water
movement. For example, a young, vegetative tissue with many stomata and
thin cuticles (e.g. baby spinach) loses more water than storage organs with
lots of suberin (e.g. potatoes).

Seasonal, climatic and production differences influence the make—up of
the product and also affect the water loss from the product.

The initial amount of water available for movement is best represented
by the chemical potential of the water in the product which in turn depends on

the make-up of the product (i.e. sugar content).

2.3. Process Effect
The process which the product has been subjected to, prior to freezing
has a signiﬁcant effect on water loss and quality during storage. Blanching
substantially increases the keeping quality in frozen storage (Dalhoff and Jul,
1963). Antioxidant treatment prior to freezing has been found to help maintain
the original quality of ﬁsh (Jul, 1984).
Simple coating treatments prior to freezing (i.e. with dressings) appear

to reduce in-package desiccation (Jul, 1984)

14

2.4. Packaging Effect

2.4.1. Material and Packaging types

Among the most common plastic materials used by the frozen food
industry are, polyamides (i.e. nylon), polyesters such as polyethylene
terephthalate (PET), or its crystallized form (CPET), polyoleﬁns such as low-
density polyethylene (LDPE), and polypropylene (PP), polyvinyl chloride
(PVC), polyvinylidine chloride (PVDC) and ethylene vinyl alcohol (EVOH).
Average permeation values for the different plastic ﬁlms are presented in table
2.

The systems used to package frozen food products for retail sale can
be classiﬁed into three categories depending on the product end-use: a)
atmospheric ﬁlling into simple bags and skin-packaging for loose uncooked
product or cuts; b) modiﬁed atmosphere packaging (MAP) and vacuum
packaging, for precooked products, and c) ovenable and dual-ovenable
packaging for prepared foods. The ﬁrst two categories are of importance in
this study.

Atmospheric ﬁlling in bags is the most common choice for packaging
uncooked vegetables, because of process simplicity and material low cost.
Skin-packaging, however, is a modiﬁed form of vacuum packaging, in which
the air between two ﬁlms (one of which is heated), or a ﬁlm and a backing
material is withdrawn and the ﬁlm is formed around the food (Pham and

Mawson, 1997). This process is primarily used for frozen meat products.

15

Table 2. Properties of selected barrier ﬁlms used for frozen food systems"

 

 

 

 

 

 

 

 

 

 

 

Film Water Vapor Transmission Oxygen permeability (cm3
Rate (g m‘2 day'1 at 38 °C rn‘2 day“1 atm‘1 for 25 um
and 90 °/o RH) ﬁlm at 25 C)

Polyester (oriented) 25-30 50-1 30
PVDC-PVC copolymer 1.5-5.0 8-25
(Saran)
LDPE 18 7800
HDPE 7-10 2600
Nylon-6 84-31 00 40
Polypropylene (oriented) 6-7 2000
Plasticized PVC 1540 500-3000
Polystyrene (oriented) 100-125 5000
EVOH 16-18 3-5

 

 

 

 

 

* adapted from Balasubramaniam and Chinnan (1997)

MAP systems comprise the enclosure of food product in gas
environment that has been changed or modiﬁed (Young et al., 1988). Vacuum
packaging involves packing the product in a pouch or tray made of a good
oxygen barrier ﬁlm, removal of air from the package with subsequent package
sealing. Due to reduction of the oxygen level inside the package, this method
has been reported effective in inhibiting microbial growth for a number of food
products (Bramsnaes, 1969; Piazza, 1988; Steinbuch, 1979). However,

vacuum packing alone has been reported not to be as effective as blanching

16

(i.e.: inferior organoleptic quality) for long term quality preservation of frozen
spinach (Bimbaum et al., 1979).

In a packaged frozen product, frost forms whenever there is headspace
(IIR, 1986). Thus, vacuum packaging is often proposed as a way to reduce
that headspace and minimize frost formation. For this process to be effective,
uniformity (regular shape and size) of the product is important. When
vacuum-packing loose, relatively large and rigid products, gaps remaining will
allow water migration into the depressurized headspace, and thus ice can
form.

In gas packaging, the product is stored in an atmosphere containing an
appropriate composition of nitrogen and carbon dioxide. This technique is
often used for frozen storage of precooked, ready to eat products (Pham and
Mawson, 1997). Since this process does not reduce the headspace, water

migration and thus frost can still occur.

2.4.2. Inﬂuence of package parameters on quality

The following package material parameters can have an effect on the
quality of the food product: gas and water vapor penneabilitles and material
thickness, packaging process, package surface area, transparency and
product storage environment (ambient gas composition and storage
temperature) (Lee et al., 1991 ).

Package material permeability is an important factor affecting
headspace composition (Pham and Mawson, 1997). Several studies have
shown that materials and processes that excluded oxygen, delayed lipid

oxidation and/or prevented loss of color especially in meat products (Brewer

17

and Harbers, 1991a); (Ahn et al., 1998; Bhattacharya et al., 1988; Fonnanek
et al., 2001; Houben et al., 2000; Jo et al., 1999; Lanari and Zaritzky, 1991;
Nam and Ahn, 2003; Wang et al., 1995). It has been shown that packaging
materials with low oxygen transmission rates (OTR) had less microbial
spoilage (Kotzekidou and Bloukas, 1996). The use of oxygen barrier ﬁlms has
been reported to help retain carotenoid and ascorbic acid in oxygen sensitive
foods (Thakur and Arya, 1988).

Films with high water vapor permeability can lead to dehydration of the
product, protein denaturation and lipid oxidation (Jul, 1984). For vegetables, it
is desired that water vapor permeability be low enough to minimize
dehydration and related quality changes. However, a package with low water
permeability could trap excessive moisture, which may lead to undesirable
microbial growth (Pham and Mawson, 1997). This water becomes frost at
freezing temperatures. Several studies have related the presence of frost to
the package permeability and concluded that less frost formation occurs using
permeable materials (i.e.: cartons) compared to less permeable plastic bags
(Mendez Bustabad, 1999; van den Berg, 1966).

Temperature also affects the permeability of a ﬁlm. A ﬁlm that creates a
near-ideal headspace at ambient conditions may not necessarily provide the
same headspace in frozen storage.

Transparency of the package material can greatly affect the quality of
food products that are susceptible to light, infrared radiation and ﬂuctuating
temperatures (Pham and Mawson, 1997). Light-impenneable materials would
likely improve surface color retention of foods containing carotenoids (i.e.:

carrots), since they would reduce the degradative effects of short wavelength

18

radiation (Ahvenainen and Malkki, 1985). Transparent packaging materials
did not protect refrigerated food in the critical wavelength range of 400-500
nm. Odors and color changes were notable after several hours of exposure to
ﬂuorescent light (Mortensen et al., 2002).

The package environment signiﬁcantly affects the quality of the frozen
food. Within a display cabinet, the shelf position of the food product will
determine the amplitude of temperature changes it experiences (Jul, 1984). In
home freezers, these temperature ﬂuctuations may be higher than those in
commercial storage due to less precise thermostats (Pham and Mawson,

1997)

2.4.3. Quality changes associated with packaging

Color, nutritional loss, biochemical and sensory changes, frost
formation, moisture loss, drip loss, and overall appearance are among the
quality changes associated with packaged frozen foods. In this section, the
quality changes discussed will be color, weight loss and frost formation.

Frost formation is related to the permeability of the package material,
and ﬂuctuating temperatures. Klose et al. (Klose et al., 1955) measured the
effect of storage temperature in the range of — 10 °F to +10 °F on frozen
turkeys wrapped with materials of different penneabilities and found that while
the eating quality was not impaired, the appearance of the product was
negatively affected by appreciable product moisture loss deposited as frost
inside the package. Frost not only increased with temperature, but its

formation in the - 10 °F to +10 °F range was about twice that for the

19

approximate mean temperature (0 °F). This indicates “a marked tendency for
the cycling temperature to ‘pump’ water out of the food”.

The amount of frost ice formed in commercial products was also
studied. van den Berg (1966) surveyed packaged frozen vegetables and
found that about 80% contained frost greater than 2%. In addition, it was
found that frozen vegetables had a tendency to clump — product ﬂowed less
freely — in plastic bags; freezer burn (light-colored patches) was more
widespread as the amount of frost increased. The author reported that in
extreme dehydration, peas were found to shrivel and to have turned brown.

Chen et al. (1989) explained that the decrease in drip loss and color
change in frozen hamburger meat was related to the “migratory
recrystallization” process (simultaneous sublimation-diffusion-condensation of
ice), which was facilitated by a large air space volume, approximately 0.55

dm3, between the food and the package wall.

2.5. Combined effects and mathematical models

2.5.1 . Kinetic models
Semi-empincal kinetic models are often used to describe the change in
quality of frozen foods. These models facilitate package design resulting in a
product with desired quality attributes (Reid, 1997). A food constituent can be
characterized by a quality attribute, p. The change in p as a consequence of

deteriorative reactions can be described as:

dp/dt = -k p" (2)

20

Where p is the food quality attribute, t is time, k is the rate constant,
and n is the order of reaction. Quality degradation during frozen storage is
often modeled using linear relationships (zero order reaction) or as a ﬁrst
order reaction. Mathematical derivations of reaction kinetics can be found in
the literature (Arabshahi and Lund, 1985; Labuza and Riboh, 1982). The rate
constant, k, is temperature dependent, and usually can be modeled using an
Arrhenius relationship. However, the Arrhenius equation does not account for
the effects of pressure and concentration of reactants. It was originally
developed for monoatomic reactions though many authors have reported
success when modeling quality loss. Packaged food quality has been studied
using different quality attributes such as change in color (Bhattacharya et al.,
1988); (Lanari and Zaritzky, 1991); (Chen et al., 1989); (Lanari et al., 1993),
drip and cooking losses (Bhattacharya et al., 1988), water holding capacity
and lipid autooxidation (Chen et al., 1989). Lanari et al. (1993), used a ﬁrst
order kinetics to explain the decay in skin and vacuum packaged frozen and
frozen, and thawed beef. Lanary and Zaritzky (1991), used a ﬁrst order model
to explain the effect of vacuum packing on myoglobin oxygenation during
storage of frozen and partially frozen beef. Chen et al. (1989) calculated
apparent activation energies for lipid meat auto-oxidation, discoloration and
changes in water holding capacity using the Arrhenius equation.

Kinetic parameters are often complex summations of all individual
elementary reactions. In these models, it is assumed that the temperature

history of the product is known.

21

2.5.2. Temperature distribution and ﬂuctuating temperature

The rate of quality loss during the post-freezing phase (i.e.: distribution,
storage, retail cabinet display, in-home freezer storage) is temperature
dependent (Pham and Mawson, 1997). Methods which can predict or identify
the temperature distribution within a product are useful in assessing the loss
of quality due to ﬂuctuating temperatures during post-freezing.

When developing temperature prediction models, data from freezing
processes were initially used to develop the models. Later, these models were
adapted to post-freezing studies. According to Scott (1987), these models can
be categorized into two groups: 1) those producing freezing time estimations,
and 2) those producing temperature distribution proﬁles within the food. For
post-freezing studies, models which predict temperature distribution within the
food are of most related.

Most of these models are based on either heat transfer analysis or
coupled heat and mass transfer analysis (Delgado and Sun, 2001). The heat
transfer analysis is usually taken as a one-dimensional problem (Scott, 1987),
and has been approached in several different ways. These approaches range
from analytical to numerical methods. Analytical techniques often assume
pure components, constant heat transfer coefﬁcients and change of phase at
a speciﬁc, single temperature (Scott, 1987). However, food products, which
are a solution or mixture of components, do not have a speciﬁc, single
freezing point. The change of phase takes place over a wide range of
temperatures because as the mixture freezes, the liquid portion becomes
more concentrated with solute, and the freezing point is depressed further.

Thermal properties, which are composition-dependent, become signiﬁcantly

22

temperature dependent as well. Thus, it has been argued that analytical
methods are inadequate for predicting temperature changes within a product
during freezing, or during periods of ﬂuctuating temperatures close to the
phase change (Zuritz and Singh, 1985). Numerical methods have the
advantage that they can analyze the effect of the phase change over a range
of temperatures, and can be considered to be the most accurate if they are
implemented correctly (Delgado and Sun, 2001).

Numerical models (i.e. using ﬁnite difference or ﬁnite element methods)
and studies have not taken into account packaging since it has been assumed
that it would have a beneﬁcial effect in terms of damping temperature
fluctuations (due to the stagnant air in the headspace). The fluctuation period,
temperature range and package size have been regarded as the more
important factors (Zuritz and Singh, 1985).

Sastry and Kilara (1983) modeled the thermal response of stacks of
frozen peas exposed to energy-conservation practices involving di-thennal
storage cycles. The authors reduced the model to ordinary conduction heat
transfer with temperature dependent thennophysical properties. Zuritz and
Singh (1985) simulated the effect of storage temperature changes through
simulated frozen-food distribution channel using a ﬁnite element model. Mittal
and Parkin (1985) studied the response of frozen foods in insulated
containers to a step change in the ambient temperature.

More recently, Zuritz and Sastry (1986) and Scott and Heldman (1990)
demonstrated that the contribution of packaging materials to the total
resistance is important when the convective resistance of the surrounding air

is negligible. The resistance due to the packaging interface is greater than

23

 

zero but less than inﬁnity. Thus, the assumption for thin packaging materials
that there is no resistance to heat ﬂow, should be corrected when modeling
the temperature response of a food product especially when using insulatory
material.

Moureh and Derens (2000) presented a three-dimensional ﬁnite-
volume heat transfer model which predicted temperature within a pallet as a
function of time of exposure, storage conditions, product initial temperature,
palletization pattern and thermal characteristics of the products and
packaging. They reported good agreement with actual temperatures recorded
at different locations in the pallets of packaged frozen ﬁsh when placed in
closed or open dock areas.

Though there is great promise in numerical models, it is difﬁcult to
develop precise prediction methods since accurate experimental data are not
easy to obtain. Furthermore, heat transfer coefﬁcients are hard to estimate
with errors of 10 to 20 % reported (Delgado and Sun, 2001). The most fruitful
work is still in ﬁnding simple, accurate equations for foods with regular shapes

that can be used with relatively little effort.

24

CHAPTER 2

MATERIALS AND METHODS

1. Materials

1.1. Carrots
Baby cut carrots were used in all experiments. They were selected to
have uniform size and shape. Fresh carrots were purchased from a local
supplier (Steve’s Produce, Lansing, MI) and were blanched prior to packing.
The blanching process was performed using a steam blancher (Dixie, Inc.).
The product was heated in steam at 90 psig for 3.5 minutes in 20 lb-batches
and then cooled for about 10 minutes at room temperature. The carrots were

then packed into the test packages and sealed.

1.2. Packaging Materials

Two different packaging materials (nylon/polyethylene multilayer and
polyethylene) were used (Table 3). For the nylon/polyethylene multilayer ﬁlm,
vacuum and atmospheric packaging processes were employed. Vacuum
packaging was performed using a MULTIVAC A 300/16 machine (MULTIVAC
INC., Kansas City, MO) set at -800 mbar. For the PE structure, punctured and
non-punctured bags were used. Punctured PE bags were fabricated by
poking six evenly distributed perforations (about 0.5 mm in diameter) in both
sides of the bag material.

All bags, with exception of the vacuum processed ones, were sealed

using a SENCORP SC-12 impulse heat sealer (Sencorp Systems Inc.,

25

Hyannis, MA) at the settings listed in table 4. The bags used for the vacuum

process were sealed using the MULTIVAC A 300l16 machine.

Table 3. Bag Materials Speciﬁcations

 

 

 

 

 

LDPE Nylon/PE multilayer
Supplier VWR Koch Supplies Inc.
lntemational (Kansas City, MO)
(Batavia, IL)
Thickness, mils 3 3
Composition LDPE (0.75 mil nylon, 2.25 mil
polyethylene)
Dimensions from seams, cm 18 x 13.5 18.5 x 13

 

 

 

 

 

Table 4. Parameters for impulse sealing

 

 

 

 

Parameter LDPE Nylon/PE
multilayer
Impulse time, s 1.1 1.5
Dwell time, s 1 1
Jaw pressure, psi 34 34

 

 

 

 

 

26

2. Processes

2.1. Freezing and Frozen storage of the samples

The packaged samples (at lb per bag) were frozen using an air freezer
set at -20 °C (-4 °F) with an airﬂow estimated to be about 200 feet per minute
(ALNOR Series 6000-P velometer (Niles, IL). Once frozen, the samples were
kept in the freezer until they were evaluated or moved to the retail cabinet.
One week prior to evaluation, half of the bags designated for each packaging
material/process combination were moved to a retail cabinet (Zero-Zone |nc.,
Waukesha, WI) set at -26 °C (-15 °F) at a local store. Figure 1 presents a

layout of the experimental design.

3. Methods

3.1. Temperature monitoring

Temperatures in the air freezer, and commercial retail cabinet were
monitored and recorded throughout the experiment using a set of OMEGA
OM CP IFC 101 temperature data loggers (+l- 1 °C). In all cases, the devices
were set to record the temperature every 5 minutes. Retail cabinet: A
temperature data logger was attached to the lower frame of the retail cabinet
at the same tray level as the samples were placed. Only the lower level tray
was used for sample placement throughout this study (Figure 2). Air freezen 3
temperature dataloggers were used for approximately two months each to
record the temperatures inside the freezer. The device was placed in the

middle tray throughout the entire experiment (Figure 2).

27

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Retail Cabinet

Samples in
lower tray

 

   

Air Freezer

Temperature
datalogger

Figure 2.Temperature datalogger location inside the freezer and retail freezer

cabinet

29

3.2. Frost and glassy ice content
Evaluations included: a) package weight loss; and product weight change;
and b) amount of frost ice formed inside the bag.
a. Package gross weight was initially (prior to freezing) determined to 0.1 9,
using an electronic Ohaus balance (Florham Park, NJ), and again at the
end of storage. The difference in weight is expressed as a percentage of

the initial weight according to the following relationship:

p,.(M) (3)

Where,

PM: package weight loss, %

W0: initial package gross content weight, 9

WF package gross content weight at moment of evaluation, 9

b. Frost ice crystals were separated by shaking the contents of each

package into a standard sieve (0.263 in opening). This was done in a
chamber at 38 °F. Frost which adhered to the inside of the bag was
removed using a dry cloth. To prevent temperature differences and water
condensation from interfering with the performance of the electronic
Ohaus scale, the content of each bag without frost, was then weighed, as

quickly as possible at 23 °C. The frost content is assumed to be the

difference between the ﬁnal gross weight and the weight of the contents,

30

without frost, and the empty bag. This is expressed as a percentage of the

initial net weight according to the following relationship:

Wt - (Wb + WC)
W0 — Wb

 

Frost content percent : [ ] x 100 (4)

Where,
W0: initial (prior to freezing) package gross content weight, 9
WP package gross content weight at moment of evaluation, 9
W), = empty bag weight (without frost), 9

WC = contents weight (without frost), g

c. Glassy ice (from frozen vacuum packed carrots): The glassy ice was
allowed to melt in a room at 38 °F for 30 min. Then the sample was placed
on a standard sieve (0.263 in opening). This was done in a chamber at 38
°F. Ice which adhered to the inside of the bag was removed using a dry
cloth. To prevent temperature differences and water condensation from
interfering with the performance of the electronic Ohaus scale, the content
of each bag without the glassy ice, was then weighed, as quickly as
possible at 23 °C. The frost content is assumed to be the difference

between the ﬁnal gross weight and the weight of the contents, without

31

glassy ice, and the empty bag. This was expressed as a percentage of the

initial net weight according to equation 4:

3.3. Moisture content determination

The method used is an adaptation of the AOAC Ofﬁcial Method 984.25
(AOAC, 2000) for moisture determination in frozen french-fried potatoes.

A sample of product was cut in pieces, homogenized and weighed in
aluminum dishes in duplicate (ca 10 g), using a Mettler balance accurate to 0.1
mg. Dishes containing the test samples were subsequently dried in a vacuum
oven at 55—60 °C +l- 3 °C for 24 h. When cool, they were weighed as quickly as
possible.

The moisture content (MO) was then calculated as a percentage,
according to:

M.C.°/o=[M‘—M2
M

 

_ )xmo (5)

1 0
Where

Mo: mass in g of tared dish;

M1: mass in g of tared dish, and undried test sample

M2: mass in g of tared dish and dried test sample
3.4. Color Assessment

Samples of product thawed for one day were assessed using the

calorimeter. A DZS-PC2A Hunterlab Colorimeter (Reston, Va) was used for color

32

evaluation on L (lightness), a (redness), and b (yellowness) values. Reported
results are averages of two readings of every replicate (after ﬁrst reading, the
sample was rotated for a second reading, to avoid interference due to sample
orientation).

The severity of surface white coloration was estimated by Whiteness
Index (VIII, in the range of 0 to 100), which is expressed as (Bolin and Huxsoll,

1991):

WI = 100 — ((100 — L)2 +a2 +b2 )0-5

Where a, b and L were the Hunter parameters. The numerical scale of WI is from
0 to 100, where higher WI values represent more severe white surface

discoloration (Bolin and Huxsoll, 1991).

3.5. Product appearance - Sensory Evaluation
The objective of this study was to identify whether time or treatment had
an effect on the appearance of the product. Thus, it comprised two evaluations:
a) Carrots at package opening (degree of frost covering carrots), and b) product
overall appearance (color, size, shape and texture combined) after frost

removed. Following there is a summary of the major features of this evaluation.

33

Rating
9.0 to 7.0 6.9 to 5.0

       

Figure 3. Reference standards for carrots upon package opening

Rating
9.0 to 7.0 6.9 to 4.0 3.9 to 1.0

 

Figure 4. Reference standard for overall appearance of carrots after frost

removal

34

Digital pictures of the samples were evaluated using a panel of 15
subjects. To test the effect of time, pictures were taken of two replicates at month
one and month six of a speciﬁc treatment (nylon/PE multilayer bags,
atmospherically ﬁlled and exposed to retail). In order to test the effect of
packaging systems, digital pictures were taken of two replicates of every
treatment at month 6.

For carrots upon package opening (degree of frost on the carrots), the
panelists rated each picture using a scale from 1.0 (worst condition) to 9.0 (best
condition); reference standards were available and are presented in Figure 3.
Likewise, for product overall appearance, the panelists judged each picture on a
scale from 1.0 (worst condition) to 9.0 (best condition); reference standards were
available and are presented in Figure 4.

Pictures: Due to the length and nature of this study, it was decided that
pictures of the samples could be used. Potential advantages were the
convenience of avoiding the handling of the refrigerated product and short panel
evaluation times. It was assumed that potential drawbacks could be related to
poor picture resolution or inconsistent characteristics (contrast, color, brightness,
size, zooming) of the pictures and thus impaired evaluation. To assure a fair
picture quality, all pictures were taken with a high-end digital camera -NIKON
Coolpix 995 of 3.1 megapixels (Tokyo, Japan)-, in the same room, with the same
lighting. The samples where placed on a table over a black background at the
same distance of the digital camera placed in a tripod. The digital camera

controls (brightness, zooming, color, contrast, etc.) were set to the same

35

manufacturer default values throughout the study. The digital pictures of the
samples were screened and found to be representative of each treatment type.
Pictures were then electronically uploaded to a computer and presented in a
questionnaire using SIMS 2000® sensory evaluation software (Sensory
Computer Systems, Morristown, NJ).

Panel: 15 panel members were selected on the basis of past performance
in an initial screening of carrot pictures. Panelists attended one 15—min training
session and two 45-min evaluation sessions each on separate days. All 15
panelists rated each picture. Panel members were informed that the samples
were digital pictures taken of the actual carrots. The identity of the individual
samples was not disclosed.

Location: The panel worked in a room speciﬁcally used for sensory
evaluation studies at the Food Science Department at Michigan State University.
The room was lit with 40-watt ﬂuorescent bulbs. Members worked individually in
7 booths and no discussion took place during evaluation. Each booth was
equipped with a seat, desk, computer terminal and monitor. The questionnaire
was displayed separately in each monitor and members could enter their
responses by clicking the computer mouse. The standards (Figures 3 and 4)
were displayed in every questionnaire and could always be seen by scrolling up
the screen. For carrots upon package opening (frost on the carrots), the samples
were presented in sets in a balanced, random order. Likewise, for the overall
appearance determination, the samples were presented in sets in a balanced,

random order. For this determination, the panel was informed in the electronic

36

questionnaire that overall appearance of the carrots was a combination of
product texture, ﬁrmness, color and shape.

Statistical evaluation: The experiment was treated as a qualitative
descriptive analysis for attributes including a BIB (balanced incomplete block
design) rating test, and the results were evaluated using an analysis of variance

(ANOVA) test according to Meilgaard et al (2000).

37

CHAPTER 3

RESULTS AND DISCUSSION

The following results refer to the treatments, sub-treatments, and time
variable for the different samples. There were four treatments: 1) Frozen carrots
atmospherically packed in nylon/PE multilayer bags (A-NPE), 2) Frozen carrots
vacuum packed in nylon/PE multilayer bags (V-NPE), 3) Frozen carrots
atmospherically packed in LDPE bags (LDPE), 4) Frozen carrots,
atmospherically packed in perforated LDPE bags (P-LDPE). The sub-treatments
were two: 1) Samples exclusively kept in the air freezer (air freezer sub-
treatment), 2) Samples kept in the air freezer and then moved to a retail freezer
cabinet one week before analysis (air freezer/retail freezer sub-treatment). The
duration of the experiment was 6 months. Samples were analyzed at monthly

intervals.

1. Frost content and origin

Throughout the six months of storage, an increase in frost ice amount was
noted in all treatments and sub-treatments. The ice amount was the highest for
vacuum packaged samples, though the ice formed had a hard, glassy-like
character, not the typical frosty appearance. For the rest of the samples, the ice
formed had a frosty appearance regardless of the packaging material or sub-
treatment (Figure 5), and frost determination was carried out as described in the

methods section. The difference between the two methods may make a

38

consistent comparison of the data difﬁcult. Thus, the statistical analyses that
follow only consider the carrots atmospherically packed in perforated and non-
perforated LDPE bags and in nylon/PE bags. Analysis of the data from vacuum

packed carrots is considered separately (pg. 46).

 

Figure 5. Appearance of frost ice on atmospherically packed samples

Sample data (except vacuum packaged) was processed using a
nonparametric model (Appendix B), and tested to determine whether the
treatments, sub-treatments, and time had any effect on frost formation. It was
found that time had a signiﬁcant effect on increase in frost (p<0.05) (Figures 6
and 7). A Kruskal-Wallis test (Appendix B) was used to perform a multiple
comparison analysis of the data. It showed a consistent tendency of frost to

increase over time, regardless of treatments or sub-treatments.

39

3.50 —*
3.00 I
2.50 ~

- 2.00 -
1soI
1ooI
0.50 i
0.00 I I I I I
0 1 2 3 4 5 6 7

Time, months
+ P-LDPE + LDPE —0----A-NPE

 

 

%

 

Frost

 

 

 

._4

Figure 6. Comparison of frost ice during time for “air freezer” bags (V—NPE bag

data not included)

 

 

 

 

 

400

350i r

1 -. :I//2
szso- I’M/-f/”'g § 1 2
gzoo- g 3
LL 1.50 —

1.007

050-

ooo I T I I I I

o 1 2 3 4 5 6 7

Time, months
A P-LDPE ., LDPE +A-NPE

Figure 7. Comparison of frost ice during time for “air freezer/retail freezer” bags

(V-NPE bag data not included)

40

It was also found that there was no significant difference in frost formation
between storage conditions and retail conditions, unless the bag had perforations
(p<0.05). Samples in perforated LDPE bags in the air freezer/retail freezer sub-
treatment had less frost compared to perforated LDPE bags in the air freezer
sub-treatment. No signiﬁcant difference in frost ice amount was found between
either frozen carrots in LDPE bags (with no perforations) under both sub-
treatments, or those packed in Nylon/PE multilayer bags under both sub-

treatments (air freezer and air freezer/retail freezer).

VVlthin the air freezer/retail freezer sub-treatment, the data does not show
a signiﬁcant difference between the treatments (material/process combinations).
However, within the air freezer sub-treatment, the data shows a signiﬁcant
difference in frost formation (p<0.05). Within this sub-treatment, frozen carrots
atmospherically packed in Nylon/PE bags had the highest frost formation,

followed by perforated LDPE bags and non-perforated LDPE bags.

Several additional studies were performed to determine the origin of the

frost. They are listed as follows:

St_udy_1_: The amount of headspace moisture in the air trapped inside the
package at the moment of packaging was estimated. By utilizing the relationships
of water content of the air at 23 °C (assumed temperature at the moment of
packing), the air density at that temperature, and the headspace volume inside

the package, it was possible to estimate the amount of water in the headspace

41

air inside the package. This relationship can be expressed using the following
equaﬁon:
WII = CfIfo X paII X PI. (6)
Where
WH= Moisture inside package headspace, g
Ca”H20= water content in air at 23 °C, 0.0175 g of water I g of air
pa”: density of air at 23 0C, 1.18 x 10‘3 g/cm3
PH: Package headspace volume, cm3
The moisture inside the package headspace at the moment of packaging
was estimated to be 0.00165 9., assuming the maximum headspace volume
could be 80 cm3. This amount is very small compared to measured frost

amounts (8 g - 20 9) (Appendix A). Thus, headspace moisture can be assumed

negligible in accounting for frost origin.

M: The water vapor permeability of the packaging materials was
determined using a PERMATRAN W 3/31 instrument (Mocon Inc., Minneapolis
MN) (Appendix C). Samples of the plastic materials were tested at 20 °C, 15 °C
and 10 °C (2 replicates each). During the actual test, one side of the ﬁlm was
exposed to 100% RH while the other side was kept at 0% RH. Activation
energies for water vapor permeation were determined and permeability
coefﬁcients for a storage temperature of 0 °C were calculated using an Arrhenius
equation (Appendix C). Table 5 shows the predicted amounts of moisture

transport through the package for up to 6 months, assuming a maximum storage

42

temperature of 0 °C during the entire 6—month period, and extreme relative
humidity conditions (100% RH inside-0% RH outside) on each side of the

packaging material.

Table 5. Maximum predicted amount of moisture transported through

package at 100% RH and 0 °C after X months

 

Month
1 2 3 4 5 6
q (non-perforated LDPE), 9 0.2863 0.5727 0.8590 1.1454 1.4317 1.7181
q (Nylon/PE multilayer), 9 1.1455 2.2910 3.4364 4.5819 5.7274 6.8729

 

 

 

 

 

 

 

 

 

 

 

 

Under these extreme conditions, the amount of moisture transported
through the nylon/PE material is higher than that through non-perforated LDPE
for any month. This is may be due to the higher water sensitivity of nylon.
Comparing these amounts to actual frost amounts (Tables 6 and 7), it is shown
that the amount of water vapor transported through the film ranged from 0.3 to
13.7% of the measured frost amounts in the LDPE bags, and 11.4 to 40% of the
measured frost or glassy ice amounts in the nylon/PE multiplayer bags. Thus, the
amount of moisture coming into or out of the package by permeation is generally
not signiﬁcant for LDPE bags, but may be important for Nylon/PE multilayer bags

during long storage periods.

43

Table 6. Average amount of frost (grams) after X months for air freezer/retail

freezer sub-treatment

 

 

 

 

 

 

 

 

 

 

 

 

 

Month
Packaging System 1 2 3 4 5 6
A-NPE 9.93 11.18 12.85 12.50 12.80 17.10
V-NPE 16.18 15.87 24.53 27.20 27.00 21.10
LDPE 8.82 12.05 10.98 11.68 11.33 12.43
P-LDPE 8.95 9.30 11.53 10.95 12.53 10.95

 

Table 7. Average amount of frost (grams) after X months for air freezer sub-

 

 

 

 

 

 

treatment
Month
Packaging System 1 2 3 4 5 6
A-NPE 10.35 11.95 12.18 14.05 13.83 14.03
V-NPE 14.75 19.38 25.28 22.97 20.28 17.00
LDPE 8.90 10.08 11.18 12.25 11.38 10.73
P-LDPE 10.10 12.03 12.20 12.65 12.53 10.78

 

 

 

 

 

 

 

 

Study 3: To determine how much of the measured frost came from

freezing the surface water that was on the carrots before packaging, fresh carrots

were blanched, packaged in nylon bags and subsequently frozen. The following

day, the bags were opened and the amount of frost ice was evaluated using the

same procedure as previously described. The amount of frost was approximately

10 g (2.6%), which is signiﬁcant when compared to the level of frost ice after the

ﬁrst month (Tables 6 and 7). Possible explanations for this could be related to

44

 

 

water made available by: a) the blanching-cooling-packing procedure used; and
b) the freezing process. Following blanching, the hot carrots were left at room
temperature to cool down. During cooling, the temperature at their surface
decreased in part as a result of water evaporation. Eventually, the water that
migrated to the product surface (to compensate for the difference in water vapor
pressure) remained there as a ﬁne ﬁlm since it would not have enough energy to
become steam. After cooling, the product was packed into their respective bags.
Thus, the surface ﬁlm of water could have been one of the sources that

contributed to the frost formed.

During freezing, a low rate of heat removal can lead to moisture
movement within the carrots through an osmotic mechanism, with the lost water
going to external ice crystals (Reid, 1997). The length of this process would be
mainly dependent upon the medium heat transfer coefﬁcient. The medium heat
transfer coefficient for the freezing method (neglecting packaging material) used
in this study is rather small (Table 1) meaning that this process would take long.
This is reinforced by the fact that in all evaluations, clumping of carrots was
noticed. Higher coefﬁcients would have been needed to decrease the freezing
duration and thus internal water migration. Thus, the duration of the freezing

process could have been one of the causes that contributed to the frost formed.

45

Vacuum packed baby carrots

Table 8 shows the data on ice for the vacuum packed baby cut carrots.
The ice formed in vacuum packed carrots in nylon/PE multi-Iayer bags did not
have the frosty appearance as did the rest of the samples (Figure 8). Instead, a
glassy-like ice coating developed probably due to product water migration under
negative pressure inside the package. A comparison with the data from the rest
of the packaging systems could not be done since the measurement procedure

(Materials and Methods) was different.

Table 8. Average glassy ice amounts (%) for carrots in bags exposed to air

freezer/retail freezer and air freezer sub-treatment after X months

 

 

 

 

 

 

 

 

 

 

 

Month
Sub-treatment #1 #2 #3 #4 #5 #6
2.22 2.45 2.79 2.76 2.75 3.03
AF/RF (0.14)‘5 (0.75) (0.18) (0.17) (0.48) (0.59)
2.30 2.59 2.67 3.06 2.98 3.05
AF (0.17) (0.17) (0.19) (0.23) (0.18) (0.22)

 

 

5 standard deviation

It is believed that the glassy ice formed due to the combined effect of
vacuum and a slow freezing process. Even though the headspace volume was
reduced as a result of vacuum packaging, there were still spaces between the
loosely packed and rigid carrots. Therefore, water from the core of the product

could migrate to the surface even more due to the vacuum. It appears that

46

vacuum packing of loose, relatively large product, creates a headspace of
signiﬁcant nature which allows water migration to occur due to the depressurized
space. In this experiment the use of a slow freezing method (Table 1) determined

the length of time that the product water was available for migration.

 

Figure 8. Appearance of glassy ice on vacuum packed samples

Study 4: This study was performed to assess the effect of vacuum
packaging, the weight loss of the contents of two bags of carrots was measured.
One bag of carrots was placed in a vacuum oven at 23 °C for 5 hours while the
other was left in the room at the same temperature for the same time. Weight
loss data from the bags is presented in Table 9.

From these results, it is readily noted that vacuum promotes greater

weight loss from the carrots than did a larger volume headspace with no vacuum.

47

Table 9. Weight loss in carrots held in a vacuum oven (2-14 in of

Hg) and at atmospheric conditions

 

Initial weight, 9 Final Weight, 9 Weight loss, 9

 

Vacuum 453.3 440.1 13.2

 

Ambient 468.1 464.4 3.7

 

 

 

 

 

 

2. Temperature profile

Temperature proﬁles of the air freezer and retail freezer are compared in
Figure 9. The higher peaks correspond to the defrosting cycles of the equipment.
For the retail cabinet the defrosting cycle takes longer, and occurs once a day,
while the air freezer has an average of 3 defrosting cycles —shorter in length —
during the same period.

In the “air freezer” sub-treatment, the packed carrots were exposed only to
the air freezer temperatures. In the “air freezer/retail freezer” sub-treatment, the
packed carrots were exposed ﬁrst to air freezer temperatures followed by a week

of exposure to the retail freezer cabinet temperatures (Appendix F).

48

32.00 -—-

27.00 Air blast freezer

22.00
’ 17.00
12.00

Retail freezer
cabinet

eF

Temperatur

9°§>NN
8888

 

-13.00
0.0 1.0 2.0 3.0 4.0 5.0
Time, days

Figure 9. Temperature proﬁle from retail freezer cabinet and air freezer

 

 

26.00 —ﬁiray level ,- ‘7 .. -_ Bottom tray level

u.

216.00 - v — i — —~ * — 2‘

3

E

§600 77- - - r- - -- - - I - 2,1 . ... EMT

34.00 I -I- -l I" ,- , I . ,_

‘1400 l r r r l , r f i
0 1 2 3 4 5 6 7 8 9 10 11

Time, days

Figure 10. Temperature at top and bottom in retail freezer cabinet

In an additional study to evaluate temperature differences between

different tray levels inside the retail cabinet (Figure 10), it was found that the

49

temperature at the top tray level achieved a higher peak during the defrost cycles
than that at the bottom tray level. A possible explanation could be that when the
evaporators, located on top in this retail freezer cabinet are working, the
temperature at the top appears to be fairly low, even lower than at the bottom
tray level. During the defrost cycle, the heavier, cold air located on top may
displace the warmer bottom air making the temperature at this part consistently

lower during the cycle.

Table 10. Average temperatures for air freezer (AF) and air freezer/retail freezer

 

 

 

 

 

(AF/RF) treatments
Sub- Month
treatments 1 2 3 4 5 5
AF (2.05 °F) (294 °F) (2.97 °F) (299 °F) (3.07 °F) (-3.07°F)
—18.91°C —19.41°C —19.43 °C —19.44 °C -19.49 °C —19.49 °C
AFIRF (1.19 °F) (233 °F) (-2.66 °F) (-2.87 °F) (284 °F) (3.14 °F) —
—18.44 °C -19.07 °C -19.25 °C —19.37 °C -—19.35 °C 19.52 °C

 

 

 

 

 

 

 

In this study, due to space constraint, the products exposed to the retail

freezer cabinet, were placed on the bottom tray. However, it may be that at the

top tray level (in this cabinet) the product might have been affected more due to

the higher peak temperatures during the defrost cycle.

From the recorded temperature data, it is noted that (except for the last

month), the air freezer/retail freezer sub-treatment had consistently higher

50

 

temperature averages than those of the air freezer sub-treatment (Table 10).
These higher averages are mainly due to the higher and longer defrost peak

temperatures in the retail cabinet.

3. Moisture content

The moisture content data of the carrots were analyzed using the same
non-parametric model used for ice results. Data from vacuum packaged carrots
were not included in this analysis. The analysis was performed to determine
whether the treatments, sub-treatments, and time had any effect on the moisture
content. The data showed that time resulted in statistically signiﬁcant decreased
moisture content (p<0.05). The analysis also showed that within different
treatments (air freezer and air freezer/retail freezer) frozen carrots packed in
perforated LDPE bags had signiﬁcantly lower moisture content (p<0.05) than
those packed in the other bags. Differences in the carrot moisture content among
the rest of the treatments were not statistically signiﬁcant. Differences found in
the moisture contents were small and were comparable to those predicted in a
mass balance calculation using the frost ice results (Appendix D).

In order to assess whether commercial products tend to show this same
trend, four bags of a similar brand of frozen baby cut carrots were purchased
from a local store and evaluated. All of the bags had the same lot number and
were analyzed for frost ice and moisture content using the same procedures

described in the methods section. Table 11 summarizes the results.

51

Table 11. Frost ice and moisture content amounts of commercial packages of

frozen baby out carrots

 

 

 

 

 

Standard

Bag #1 Bag #2 Bag #3 Bag #4 Average Deviation
Frost, g 11.60 9.70 11.40 12.40 10.90 1.04
Frost, % 2.25 2.07 2.50 2.70 2.27 0.21
C, % 85.20 87.46 86.27 85.58 86.31 1.19

 

 

 

 

 

 

 

 

The results clearly show that the frost amounts in commercial packages
and carrot moisture content are comparable to amounts in this study (Figures 6

and 7).

4. Color evaluation

The whiteness Index (WI) measures of white discoloration (Bolin and
Huxsoll, 1991). The higher the WI score, the more severe the white discoloration.
All carrot samples except the vacuum packaged ones were statistically examined
using a non-parametric model (Appendix B), to test whether the treatments, sub-

treatments, and/or time affected carrot white discoloration.

52

 

Whiteness Inde)

 

 

 

25 TTTjTITFTTTITTTTTTTIfITTTITTT

0 1 2 3 4 5 6
Time, months

 

... P-LDPE + LDPE + A-NPE

 

 

Figure 11. Color change in carrots exposed to air freezer/retail freezer sub-

treatment

Whiteness Index

 

 

 

Time, months

 

 

4.— P-LDPE + LDPE a— A-NPE i

 

 

Figure 12. Color change in carrots exposed to air freezer sub-treatment

53

Figures 11 and 12 show the WI data. The analysis showed that treatments
and time had an effect on the white surface discoloration. When the results were
sorted out by treatment, it was found that the discoloration was highest (p<0.05)
in carrots that were packed in perforated LDPE bags regardless of the sub-
treatment, followed by those packed in nylon/PE multilayer bags and those
packed in non-perforated LDPE bags. The main reason for white discoloration is
surface dehydration (Bolin and Huxsoll, 1991). The carrots lost more water due
to the presence of perforations and thus became drier and had higher WI values.
When the results were sorted by time, it was found that carrots had increasing Wl
values (p<0.05) regardless of treatment or sub-treatment. Though the changes in
WI were found to be statistically signiﬁcant, they were small. A probable reason
for this is that the packaging retarded whitening by encasing water (as frost)
around the carrots. Thus, frost on the surface of the carrots may have retarded

the white discoloration.

5. Appearance — Sensory evaluation

Overall appearance of the carrots (vacuum packaged not included) did not
differ signiﬁcantly (p>0.05) (Figure 13). Differences in product discoloration
values were low and could have been overlooked by the panel when rating the
digital pictures. Appearance differences, assumed to be a combination of product
texture, firmness, color and shape, could have been not sensitive enough.

For carrots having frost upon package opening (Figures 14 and 15), it was

found those packed in LDPE bags with perforations received signiﬁcantly higher

54

rating marks than the rest of the frozen packaged carrots (p<0.05). The panel
perceived that carrots with less frost ice must be better. This is consistent with

the frost ice data in this study (Figures 6 and 7) and with similar studies (Mendez

Bustabad, 1999; van den Berg, 1966).

 

9.00
7.00
O)
.E
ii 5.00 -
a:
3.00
1.00
A-NPE LDPE P-LDPE
Packaging system

I Air freezer/Retail freezer I Air freezer

Figure 13. Results of overall appearance of baby cut carrots (after frost ice

removed) from packaging systems within the same month (month 6)

 

9.00
7.00
5.00
3.00 .
1.00 .

Rating

 

A-NPE LDPE P-LDPE
Packaging system

I Air freezer/Retail freezer I Air freezer

Figure 14. Results of appearance of baby cut carrots upon package opening

(frost on product) from packaging systems within the same month (month 6)

55

The GLM Procedure

Dependent Variable: frostap frostap

Sum of
Source DF Squares Mean Square F Value Pr > F
Model 19 179.5728333 9.4512018 6.04 <.0001
Error 160 250.2246667 1.5639042
Corrected Total 179 429.7975000
R-Square Coeff Var Root MSE frostap Mean
0.417808 23.19434 1.250562 5.391667
Source OF Type III SS Mean Square F VaIue Pr > F
Treatment 2 53.13033333 26.56516667 16.99 <.0001
Subtreatment 1 4.70450000 4.70450000 3.01 0.0848
Treatment*Subtreatme 2 59.27633333 29.63816667 18.95 <.0001
RepTicateZ 14 62.46166667 4.46154762 2.85 0.0007

Least Squares Means
Adjustment for Multiple Comparisons: Tukey

frostap LSMEAN
Treatment LSMEAN Number
1 4.86666667 1
2 5.16833333 2
3 6.14000000 3

Least Squares Means for effect Treatment
Pr > |t| for H0: LSMean(i)=LSMean(j)

Dependent VariabIe: frostap

i/j 1 2 3
1 0.3855 <.0001
2 0.3855 0.0001
3 <.0001 0.0001

Figure 15. ANOVA and multiple comparison results of baby cut carrots upon

package opening (frost on product) from packaging systems within month 6

565

The panel perceived a difference in the appearance of frozen carrots in
the nylon/PE multilayer bags for the air freezer/retail freezer sub-treatment with
time as well (Figures 16 and 17). In both determinations those baby out carrots at
month 1 received higher marks than baby out carrots at month 6. Appearance

results for frost on carrots are consistent with frost determination data (Figures 6

and 7).

 

 

AQO'IVCD

 

Rating
A (.0 0'1 \I <0

 

 

Months

Figure 16.Results of overall appearance of baby out carrots (after frost ice

removed) for A-NPE for two months

Rating

ACOU'INCO

 

 

Months

Figure 17. Results of appearance of baby cut carrots upon package opening

(frost on product) for A-NPE for two months

57

6. Model for frost ice formation
Frost ice results were modeled using two relationships: a straight line of
the form:
y=mt+w U)
and a kinetic-like relationship:
I! = b + (a-b) eXP (Gt) (8)
Where
y = frost ice amount, %
t = time, months

a, b, m, w = models parameters

The results were modeled using a Tablecurve® 2-D V5 curve-ﬁtting
program (AISN Software, Mapleton, OR) and their graphs are shown in Appendix
E. Comparing the R2 values of both models (Tables E1 and E2), it can be seen
that frost formation was satisfactorily ﬁtted by equation (8) which is plotted in
Figures 18 and 19. The more parameters of equation (8) seem to help better ﬁt

the experimental data. Model parameters are listed in Table 12.

This ﬁrst-order kinetic-like model is consistent with previous work used to
model other food quality parameter changes (Chen et al., 1989; Lanari and
Zaritzky, 1991; Lanari et al., 1993). Both models predict the formation of frost at

the beginning of the storage time, probably due in part to the freezing process.

58

3.5

 

AA]. ._J
VT—I

Hi

 

 

 

 

 

3 1 I I J;
-—< lift—“TH: 4. 7w*
2.5 _______..___ 1/ ’3: ﬂaw—w 2 I

1.5 -

 

I—‘IH

Frost, %

0.5 '1

 

 

 

0 T T T I T T

0 1 2 3 4 5 6

Time, months

0 P-LDPE O LDPE 0 A-NPE

Figure 18. Kinetic-like model for frost formation for bags in air freezer/retail

freezer sub-treatment

 

 

 

 

 

 

 

 

 

3.5
3* I 1 I... i i
2.5 - 1»? _,_ -2.—M- " _.-.. 6“ ,
°\o 2 ﬂ _ éfxxsﬁ ? 1
a; /’ I
9 1.5 .
LL
1 .1
0.5 _.
0 T T T T T T
0 1 2 3 4 5 6

Time, months

0 P-LDPE o LDPE e A-NPE

Figure 19. IGnetic-like model for frost formation for bags in air freezer sub-

treatment

59

Table 12. Model parameters for frost formation for packed carrots under air

freezer (AF) and air freezer/retail freezer (AF/RF) sub-treatments

 

 

 

 

 

 

Sub-
treatment (AF/R F) AF

Treatment P-LDPE LDPE A-NPE P-LDPE LDPE A-NPE
1.75 1E-12 1.82 0.52 0.90 1.86

b, % (0.42)8 (7.93) (0.37) (3.80) (1.90) (0.33)
2.84 2.636 3.06 2.73 2.54 3.21

a, % (0.54) (0.11) (0.29) (0.09) (0.13) (0.28)
0.32 1.78 0.39 1.51 1.06 0.38

c, % month" (0.43) (3.02) (0.31) (1.71) (1.12) (0.26)
R2 0.81 0.70 0.90 0.71 0.70 0.93

 

 

 

 

 

 

 

 

 

5 Standard deviations are given in parenthesis

The ﬁrst—order kinetic-like model’s parameters appear to be consistent
with the experimental data. Frost ice formation on frozen carrots in LDPE bags
with perforations, from the air freezer sub-treatment, have a higher c value when
compared to similar ones from the air freezer/retail freezer sub-treatment. This
seems reasonable when we compare the average temperatures from both sub-
treatments (Table 10). At the higher temperatures in the retail freezer, the frost
ice tended to evaporate, lowering the c value. This could also be coupled with
changes in the relative humidity inside the retail freezer due to the more routine

opening of the door. However, this low c value could be misleading since there

60

could be less visible frost ice, even though the product itself may have lost

moisture.

The model parameters for carrots in non-perforated LDPE bags did not
appear to differ regardless of treatment. Furthermore, it can be noted that their
model parameters are not very different from those of LDPE bags with
perforations and kept in the air freezer. Thus, the effect of the perforations was
only noticeable when the package environment was subjected to higher

temperatures and decreases in relative humidity.

Assuming that the freezing process used in this study was related to the
initial formation of frost ice, it could be inferred that a different freezing process
would yield different initial amounts of frost. However, the c values are probably
independent of that process, and by relating them to the temperatures and

package permeabilities, could be used to estimate the frost formation.

61

CHAPTER 4

CONCLUSIONS

The following conclusions could be made:

With the freezing method used in this study, most of the frost ice was
found to be formed at the beginning of the storage time.

The cooling-warming cycle of a freezer may produce a net migration of
moisture towards product surface and package wall that becomes frost.
Frost ice increased with storage time.Frost decreased with perforated
package material, but product dehydration was noticed when stored in
these bags.

When the package material was not perforated, the amount of frost ice in
samples stored in air freezer and retail freezer cabinet was not
signiﬁcantly different.

When the product is frozen inside the package, vacuum packaging
promotes the formation of abundant ice with a glassy (not frosty)
appearance.

The less frost ice a package contained, the more positively it was
perceived.

Frost development may be modeled as a quality parameter

62

Recommendations:

The following recommendations could be made:

Better methods to determine frost ice and glassy ice amounts should be
developed.

Evaluate the effect of the use of different freezing methods on the frost ice
formation. The author believes that a freezing method with a high medium
heat transfer coefﬁcient (high freezing rate) produces less frost ice on
product. Furthermore, if vacuum packing were used with slow freezing,
abundant glassy ice would form. In this study, though carrots were uniform
in size and shape, their loose, relatively rigid nature left some interstices
which under vacuum increased the amount of water migration. The slow
freezing process determined the length that the water remained available
for migration.

Earlier data points (immediately after frozen) should be obtained to
improve modeling of data.

The moisture content determination procedure was probably not sensitive
enough for samples with such high concentrations of water making this
determination difﬁcult. An alternative approach would probably be to

measure the water holding capacity of the product.

63

Future work:

There are many potential avenues that could be taken. If kinetic-like
models want to be pursued, data modeling using temperature history would be
needed. Thus, model parameters could be studied in terms of temperature
changes and added variables (i.e. packaging permeability). lf numerical models
are of interest, one dimensional heat transfer could be assumed. Temperature
history of an entire package unit should be measured. In either case, more
experimental data (i.e. thermal characteristics), and improved determinations

methods would be needed for the models to be accurate.

64

APPENDIX A
RESULTS

a) Frost ice determination results:

Table A 1. Average frost and glassy ice amounts (%) for carrots in bags exposed

to air freezer/retail freezer sub-treatment after X months

 

 

 

 

 

 

 

 

 

 

 

 

 

Month
Treatment #1 #2 #3 #4 #5 #6

2.22 2.45 2.79 2.76 2.75 3.03

V-N PE (glassy) (0.14)5 (0.75) (0.18) (0.17) (0.48) (0.59)
3.63 3.48 5.42 5.91 5.89 4.71

A-N PE (frost) (1.31) (0.15) (0.97) (0.48) (0.97) (0.55)
1.99 2.68 2.48 2.59 2.53 2.80

LDPE (frost) (0.06) (0.48) (0.22) (0.27) (0.31) (0.30)
2.09 2.13 2.54 2.43 2.75 2.60

P-LDPE (frost) (0.28) (0.17) (0.22) (0.24) (0.25) (0.21)

 

 

‘5 standard deviation

Table A 2. Average frost ice amounts (%) for carrots in bags exposed to air

freezer sub-treatment after X months

 

 

 

 

 

 

 

 

 

 

 

 

 

Month
Treatment #1 #2 #3 #4 #5 #6

2.30 2.59 2.67 3.06 2.98 3.05

V-NPE (glassy) (0.17)° (0.17) (0.19) (0.23) (0.18) (0.22)
3.22 4.25 5.64 4.90 4.35 3.67

A-NPE (frost) (0.97) (1.38) (1 . 12) (1.03) (0.85) (0.07)
1.99 2.24 2.51 2.74 2.54 2.33

LDPE (frost) (0.13) (0.15) (0.18) (0.55) (0.59) (0.13)
2.25 2.60 2.71 2.91 2.78 2.53

P-LDPE (frost) (0.16) (0.10) (0.10) (0.18) (0.15) (0.23)

 

5 standard deviation

65

 

b) Color change results:

Table A 3. Average Whiteness Index values for carrots in bags exposed to air

freezer/retail sub-treatment after X months

 

 

 

 

 

 

 

 

 

 

 

 

 

Month

Treatment Initial #2 #3 #4 #5 #6
31.08 31.55 31.53 32.29 32.75 33.18
A-NPE (1 .41)‘ (0.77) (0.91) (0.91) (0.40) (0.65)
31 .08 31.36 32.83 31.95 33.44 34.03
V-NPE (1.41) (1.21) (1.05) (1.05) (1.09) (0.64)
31 .08 30.65 31.92 32.08 32.81 33.57
LDPE (1 .41) (0.42) (0.61) (1.16) (0.60) (0.58)
31 .08 31.75 32.71 32.59 33.97 34.89
P-LDPE (1 .41) (0.40) (0.43) (0.52) (0.28) (0.28)

 

5 standard deviation

 

Table A 4. Average Whiteness Index values for carrots in bags exposed to air

freezer sub-treatment after X months

 

 

 

 

 

 

 

 

 

 

 

 

 

Month
Treatment Initial #2 #3 #4 #5 #6

31.08 31.46 32.27 32.35 32.93 34.31

A-N PE (1 .41)s (0.84) (0.70) (0.36) (0.34) (0.60)

31 .08 32.14 33.33 32.59 33.68 33.77

V-N PE (1 .41) (0.85) (0.42) (0.88) (0.67) (0.83)
31.08 31.86 32.64 32.87 33.26 34.09

LDPE (1 .41) (0.85) (0.64) (1.04) (0.60) (0.99)

31 .08 32.12 33.19 33.63 34.47 34.62

P-LDPE (1.41) (0.87) (0.49) (0.74) (0.72) (0.66)

 

5 standard deviation

66

 

c) Appearance results

Table A 5. Average rating scores for appearance of carrots upon package

opening (frost on them) at month 6

 

 

 

 

 

 

 

 

Sub-treatment
Treatment Air freezer/Retail Air freezer

5.71 4.03

A-NPE (1.29)s (1.25)
4.86 4.96

LDPE (1.14) (1.14)
6.10 6.70

P-LDPE (0.72) (1.05)

 

5 standard deviation

Table A 6. Average rating scores for overall appearance of carrots at month 6

 

Sub-treatment

 

 

 

 

 

 

 

Treatment Air freezer/Retail Air freezer
5.30 5.27
A—NPE (1 .08)'°‘ (1.50)
5.24 5.77
LDPE (1.35) (1.39)
5.19 5.51
P-LDPE (1.09) (1.56)

 

5 standard deviation

67

 

 

Table A 7. Average rating scores for appearance of carrots (A-NPE, air freezer

sub-treatment) upon package opening (frost on them) at two different months

 

 

 

Month
1 6
6.27 5.71
(1.89)$ (1.29)

 

 

 

 

5 standard deviation

Table A 8. Average rating scores for overall appearance of carrots (A-NPE, air

freezer sub-treatment) at two different months

 

 

 

 

Month
1 6
5.75 5.30
(1.89)8 (1.08)

 

 

 

5 standard deviation

68

APPENDIX B

STATISTICAL ANALYSIS

A \Nllcoxon non-parametric model with Kruskal-Wallis test was performed for
frost ice and color change evaluations. ANOVA results under general linear
models were done for appearance studies. The statistical analysis was
performed with a SAS 8.02 System for VWndows. Tables 81 and 82 describe
relate the treatment and sub-treatment code numbers used for all the statistical

analyses. Next, the program script used in every case is shown.

Table B 1. Treatment code number and description

 

 

 

 

Treatment # Description

1 carrots atmospherically packed in Nylon/PE multilayer bags

2 Carrots atmosphen'cally packed in non-perforated LDPE bags
3 Carrots atmospherically packed in perforated LDPE bags

 

 

 

 

Table 8 2. Sub-treatment code number and description

 

 

 

Sub-treatment # Description
1 Air freezer/Retail
2 Air freezer

 

 

 

 

69

a) SAS 8.02 script for statistical analysis for frost and glassy ice (“wl”) data.

 

proc import out=work.book1
data file=" D: \wonl V3 . :-:l s " Excel ﬁle With data in columnwise
dbms=exce12000 replace; ﬁsmmw
getnames=yes;
run;
proc print;
run;
Proc glm ;
class Treatment Subtreatment Month;
model wl = Treatment Subtreatment Month Treatment*Subtreatment
Treatment*Month
Subtreatment*Month Treatment*Subtreatment*Month/ss3 ;

 

 

 

 

/*lsmeans Treatmrnt Subtreafmvnt Month Treatment‘ﬂnbtreatment
Treatment‘ﬁonth

Subtreatment‘Month Treatment“Battreatment‘Month/pdiff adjustztuke;
cl;‘/

output out=pred r=resid;

run;

Proc mixed ;

class Treatment Subtreatment Month;

model wl = Treatment Subtreatment Month Treatment*Subtreatment
Treatment*Month

Subtreatment*Month Treatment*Subtreatment*Month/ddfm=satterth outp=pred

I

Ir -. . - 1 _ - ~ ‘ . — -*C‘~' 4—- —..--

/ .lbﬂhdeS 'Tftnttﬁnrnt .Aqbtltmifﬁkflt lﬂontrt Trw:atnu:nt -rdhftwidtnhnii
'Treatnw11t*Mortt.

t: ‘- -—. +.--. ‘. -- 3v; ‘. .- w... w- ‘ ..,'; ‘ -4. .. ._ ..
QLMJtItnitﬁkait I Iﬂt.l .Irﬂﬁlﬂtulf .x.Lt:eeaﬁunutt*5LJ.th/Lu1.ff (+J;.uct~ttlnrn
~‘.+

bl, /

run;

proc univariate data=pred plot normal;
var resid;
run;

proc print data=pred;
run;

Proc sort;

by subtreatment;

 

 

To sort by treatment, type

 

 

 

 

run; ‘treatment’. To sort by months
erthM§

proc nparlway wilcoxon;

class treatment; SeIect the class by typing

b y s ubt reatment; 'treatmem”, ”months" or 'sub-

var Wl ' treatment'

run;

 

70

b) SAS 8.02 script for statistical analysis for moisture content (“mc”) data.

 

proc import out=work.book1
datafile=" D: \mc' 1112113 . xl s " Excel ﬁle With data in columnwise
dbms=exce12000 replace; ﬁmmmw
getnames=yes;
run;
proc print;
run;
Proc glm ;
class Treatment Subtreatment Month;
model mc = Treatment Subtreatment Month Treatment*Subtreatment
Treatment*Month
Subtreatment*Month Treatment*Subtreatment*Month/ss3 ;

 

 

 

 

'1 * \ .. "_ .. _‘ '7‘ ' . )-- »_ 7 “ .-\ _‘ > v. _ v .‘ . ‘ '__ 1. _ . ’1
/ lohnﬁduo ..:cdlfmxat .nabittntfﬁnﬂlf chill” lzvdffmrnt'.nﬂqCLLtUJLth
- .. 4 1,.
Treatment 1 "tr
'7“- —~ . 1 " ..
.:,.,.t,\.t.*tat’ 4 f'~f Hti I: 1“ t it‘ t‘“::»::' (wht‘fwzﬂ ’".: flit 1.11L.5.—“ Jrr j
41.9 ’
._, I ,
output out=pred r=res1d;
run;

Proc mixed ;

class Treatment Subtreatment Month;

model wl = Treatment Subtreatment Month Treatment*Subtreatment
Treatment*Month

Subtreatment*Month Treatment*Subtreatment*Month/ddtm=satterth outp=pred

I
/& .4 y . " v f, r '_“\ .5 '- y- 1 v-L r‘ in‘..'.'— ~§ - .
/ lLTZ-rTAUD .Irféldgr‘fi. .\,:..i.:t.:~-:1 Kt: . L“. t. Lit 11.?(‘111 .)'.41'1Tt:.‘it.f'ttt:‘I1L
"T‘ + f
ch‘-.1"tt-‘ ' \tt .1
7‘ v . ‘. i?! o t . >0‘1, .- .*.1
3‘4: tltsi “it‘ll: ;\‘l ' ' l, '1 t ' '1: fir—)d H ‘I 1’ r I L‘iLtt C111t1‘ _kg4*H
1"
.A] /
run“
I

proc univariate data=pred plot normal;
var resid;
run;

proc print data=pred;

 

 

 

 

run;

Proc sort;

by subtreatment; Tosort by treatment, type

run; "treatment". To sort by months
type "months”

proc nparlway wilgoxon;

Class treatment; Select theclass bytyping

by SUbt r eatment ; “treatment“, “months“ or ‘sub-

Vd I Wl ; treatment'

run;

 

 

 

71

c) SAS 8.02 script for statistical analysis for color change evaluation (Whiteness

index — "w1")

 

proc import out=work.bookl
datafile=" [i=2 \Z'iI onl 3:3 . :-:_.s " Excel ﬁle with data in colurnnwise
dbms=exce12000 replace; fmmmn
getnames=yes;
run;
proc print;
run;
Proc glm ;
class Treatment Subtreatment Month;
model WI = Treatment Subtreatment Month Treatment*8ubtreatment
Treatment*Month

 

 

 

 

Subtreatment*Month Treatment*Subtreatment*Month/ss3 ;

/*lsrxaans 'Frnafrwint .Wirfszanuw.1 Pkt;fh rT: atrm mt‘ k3 trraﬁwnvnt
Treatmvnt*Mwnth

Siugtrraatnv&.t*3ktntri Tre 1tnw-nttjiurﬁ reat r rn"anit /; ilttf aditnst:t1lkwy
Cl;‘/

output out=pred r=resid;

run;

Proc mixed ;

class Treatment Subtreatment Month;

model WI = Treatment Subtreatment Month Treatment*Subtreatment
Treatment*Month

Subtreatment*Month Treatment*Subtreatment*Month/ddtm=satterth outp=pred

I
I
l. e I ’T‘
7, v v e T p o 4 4 0- 79— . .
/ t ..... A T 1 ' _ . “ us 11*”.1. T.‘ .f
'7‘
P 0‘ F.

i .z 1T9
r: ”'9' 7': b.“‘ T" T 9" *5 7‘ 7". ‘ x: 3., 1“: T Fl]
e_.. t_ ,, A -- , A; L (.'.A -,

../
‘_4, /
run‘

I

./

proc univariate data=pred plot normal;
var resid;
run;

proc print data=pred;

 

 

run;
Proc sort;

b y t reatment; To sort by subtreatment, type
run ; ‘subtreatment'. To sort by

months type ‘months'
proc nparlway wilgoxon;
c .1 a s 5 month; Select the class by typing
by treatment;
var WI;

run;

 

 

“treatment”, “months" or 'sub-

heathen?

 

 

 

72

d) SAS 8.02 script for statistical analysis for appearance evaluation

Appearance upon package opening (frost on carrots — “frostaﬂ

proc import out=work.bookl

 

 

data fileznp; f ros 81330111373 . 3;; c " Excel ﬁle with data in columnwise
dbms=exce12000 replace; rammn

getnames=yes;
run;

 

 

 

proc print;
run;
Proc glm ;
class Treatment Subtreatment replicateZ;
model frostap = Treatment Subtreatment Treatment*Subtreatment
Replicate2/5s3 ;
lsmeans Treatment subtreatment replicateZ/pdiff adjust=tukey cl;
output out=pred r=resid;
run;
proc univariate data=pred plot normal;
var resid;
run;
proc print data=pred;
run;

Overall appearance (after frost removed):

proc import out=work.book1

 

datafile=”D: cverappon 1.1.73 . :-:ls " Excel ﬁle with data in columnwise
dbms=exce12000 replace; f Ibn

getnames=yes;
run;

 

 

 

 

proc print;
run;
Proc glm ;
class Treatment Subtreatment replicateZ;
model overapp = Treatment Subtreatment Treatment*Subtreatment
Replicate2/ss3 ;
lsmeans Treatment subtreatment replicateZ/pdifi adjust=tukey cl;
output out=pred r=resid;
run;
Proc mixed ;
proc univariate data=pred plot normal;
var resid;
run;
proc print data=pred;
run;

73

Appearance upon package openingifrost on carrots) “frostap” vs. time:

 

 

 

 

 

proc import out=work.book1
data file: " D: f rc-s 1; ant: time-.313 " Excel ﬁle With data in columnwise
dbms=exce12000 replace; Immmn
getnames=yes;
run;
proc print;
run;

Proc glm ;

class month Subtreatment replicate2 replicatel;

model frostap = month Replicatel(month) replicate2 /s

random Replicatel(month)/ test;

lsmeans month/pdlff adjust=tukey cl
e=Replicatel(month);

output out=pred r=resid;

run;

proc univariate data=pred plot normal;

var resid;

run;

proc print data=pred;

run};

U)
to
s 0

Overall appearance (after frost removed) “overap” vs. time:

proc import out=work.bookl

 

 

data file: " L) : eve rat:-rtimt~:.9113" Excel ﬁle With data in columnwise
dbms=exce12000 replace; ﬁsmmi

getnames=yes;
run;

 

 

 

proc print;
run;
Proc glm ;
class month Subtreatment replicate2 replicatel;
model overapp = month Replicatel(month) replicate2 s83;
random Replicatel(month)/ test;
lsmeans month/pdiff adjust=tukey cl
e=Replicate1(month);
output cuf=pred r=resid;
run;
proc univariate data=pred plot normal;
var resid;
run;
proc print data=pred;
run};

74

APPENDIX C

PERMEABILITY STUDIES

Tables C1 and CZ show the WVTR values (two replicates) of each ﬁlm at
20,15 and 10 °C.

Table C 1. Water vapor transmission rate (WVT R) data for the LDPE material at

three temperatures: 20, 15 and 10 °C

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Temperature (°C)
20.0 15.0 10.0
Readings at WVTR, g/(m2 day) WVTR, g/(m2 day) WVTR, g/(m2 day)
equilibrium Cell A Cell B Cell A Cell B Cell A Cell 8
1 1.429 1.435 0.5597 0.5697 0.5438 0.5447
2 1.426 1.425 0.5956 0.5472 0.5847 0.5397
3 1.440 1.435 0.5847 0.5255 0.5430 0.5380
4 1.390 1.419 0.5897 0.5372 0.5363 0.5347
5 1.440 1.411 0.5171 0.5205 0.5205 0.5171
6 1.424 1.452 0.5213 0.5413 0.5197 0.5338
7 1.451 1.427 0.5096 0.5472 0.5247 0.5213
8 1.461 1.472 0.5697 0.5322 0.5372 0.5413
9 1.476 1.490 0.5213 0.5163 0.5071 0.5096
10 1.457 1.472 0.5322 0.5313 0.5113 0.5205
11 1.476 1.472 0.5397 0.5639 0.5180 0.5238
12 0.5280 0.5030
13 0.5255 0.5347
14 0.5672 0.5280
15 0.5664 0.5305
16 0.5730 0.5096
Average 1.443 1.446 0.5500 0.5336 0.5315 0.5295
Std. Deviation 0.025 0.026 0.0233 0.0171 0.0216 0.0115

 

75

 

Table C 2. Water vapor transmission rate (VWT R) data for the nylon/PE

multiplayer material at three temperatures: 20, 15 and 10 °C

 

Temperature (°C)

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

20.0 15.0
Readings at WVTR, g/(m2 day) WVTR, g/(m2 day) WVTR, g/(m2 day)
equilibrium Cell A Cell 8 Cell A Cell 8 Cell A Cell 8
1 4.164 3.832 2.676 2.517 1.996 1.774
2 4.147 3.812 2.721 2.542 1.952 1.722
3 4.192 3.837 2.731 2.522 1.893 1.735
4 4.176 3.869 2.701 2.512 1.893 1.676
5 4.136 3.854 2.672 2.524 1.837 1.682
6 4.137 3.885 2.708 2.514 1.846 1.634
7 4.227 3.879 2.710 2.504 1.798 1.706
8 4.181 3.840 2.728 2.523 1.802 1.692
9 4.180 3.829 2.682 2.537 1.723 1.643
10 4.149 3.910 2.709 2.531 1.757 1.665
11 4.151 3.812 2.749 2.502 1.788
12 4.202 3.853 2.732 2.521 1.749
13 4.145 3.843 2.740 2.552 1.757
14 4.146 3.796 2.724 2.510
15 4.176 3.851 2.700 2.527
16 4.179 3.871 2.699
17 4.137 3.873 2.698
18 4.223 3.855
19 3.888
Average 4.169 3.852 2.711 2.523 1.830 1.693
Std. Deviation 0.029 0.029 0.022 0.014 0.083 0.043

 

 

 

 

 

 

 

76

 

Tables C3 and C5 show the permeability values (average of two
replicates) of each ﬁlm at 20, 15 and 10 °C. Permeability values were calculated

using the following equation:

P=[i—)‘:—A).KIB=WVTR-AlE (C1)
Where,
WVTR = water vapor transmission rate, gl(m2 day)
| = ﬁlm thickness (both, nylon/PE multilayer and LDPE films: 3 mils)
Ap = water vapor pressure difference between the sides of the ﬁlm
(p1) and (p2), of which, p2 = 0 mmHg (at 0%RH), and p1: ps (at
100% RH), where p. is the saturated water vapor pressure at 20,

15 or 10 °C listed in tables C4 and C6. Then, Ap=ps

Table C 3. Average permeability values for the Nylon/PE multilayer material at

three temperatures: 20, 15 and 10 °C

 

 

 

 

 

 

 

Temperature (°C)
20.0 15.0 10.0
P, g mil/ (m2 day P, g mil/ (m2 day P, 9 mill (m2 day
Replicate mmHg) mmHg) mmHg)
A 0.713 0.636 0.596
8 0.659 0.592 0.551
Average 0.686 0.614 0.574

 

 

 

 

 

77

 

Table C 4. Permeability data for the Nylon/PE multilayer material at 100% RH

 

 

 

 

1 P, 9 mill (m2 day
Temperature, °C 1fl‘, K‘ ps (mmHg)* In P
mmHg)
10.0 0.004 9.209 0.574 -0.5554
15.0 0.003 12.788 0.614 -0.4880
20.0 0.003 17.535 0.686 —0.3766

 

 

 

 

 

* From Perry’s chemical engineers handbook (1984)

 

Table C 5. Permeability values for the LDPE material at three temperatures: 20,

15 and 10 oC

 

Temperature (°C)

 

 

 

 

 

 

 

 

 

20.0 15.0 10.0
P, 9 mil/ (m2 day P, 9 mill (m2 day P, 9 mill (m2 day
Replicate mmHg) mmHg) mmHg)
A 0.247 0.1290 0.1731
B 0.247 0.1252 0.1725
Average 0.247 0.1271 0.1728

 

 

Table C 6. Permeability data for the LDPE material at 100% RH

 

 

 

 

 

 

 

 

1 P, 9 mill (m2 day
Temperature, 0C 1fT, K‘ ps (mmHg)* In P
mmHg)
10.0 0.004 9.209 0.173 -1.7555
15.0 0.003 12.788 0.127 -2.0627
20.0 0.003 17.535 0.247 -1.3978

 

 

* From Perry’s chemical engineers handbook (1984)

78

 

To calculate the activation energies for water permeation (Ep) through
each ﬁlm, the Arrhenius relationship listed next was used. Tables C4 and C6
show the values of In P and 1/T plotted in graph C1 as y = m x + b.

In P=InPo-+—E—p[l] (CZ)
R T
Where,
T = temperature, K
P = ﬁlm permeability, 9 mill (m2 day mmHg)
Ep = activation energy, cal/(mol K)

R = ideal gas law constant, 1.987 cal/(mol K)

 

 

 

 

 

 

 

 

 

 

 

 

0.0000
0.5000 - e T -l e—m—v .- _-- 441
y = -1480.2x + 4.6673
-1.0000 < .1211: 0.97.73 ----_._
O.
S 9-
-1.5000 -- - - -— —- —— .. _
.2 0000 -i_ Y = 49.194331 8-39§_9__-__ __ qr _- __ _ ;
R2=0.2799 '
-2.5000 . . . . . .
0.003 0.003 0.003 0.003 0.003 0.004 0.004 0.004
1rr, K

o Nylon/PE multilayer =- LDPE

Figure C 1. In P at 100% RH vs. 1fT

79

Table C7 lists the activation energy for permeation of each ﬁlm, and their
extrapolated water vapor penneabilities from 10 °C to 0 °C using a variation of

the Arrhenius relationship

Ep 1 1
In P =lnP.+— ——— C3
2 ‘ R[T, 72] ( )
Where,
T1=283.15K
T2=273.15K

p1 = mm permeability at Ti. 0 mill (m2 day mmHg)
P2 = ﬁlm permeability at T2. 9 mill (m2 day mmHg)
Ep = activation energy, cal/(mol K)

R = ideal gas law constant, 1.987 cal/(mol K)

Table C 7. Activation energies and permeability values for plastic ﬁlms at 0 °C

and 100% RH

 

 

 

 

Films Slope Ep, cal/moi P, 9 mill (m2 day mmHg)
LDPE 2919.3 5800.6 0.1184
Nylon/PE multilayer 1480.2 2941.2 0.4738

 

 

 

 

 

80

Table C8 show the maximum predicted amount of moisture transported
through each ﬁlm up to 6 months. Values of this table were calculated using the

following relationship

p.t.A.
q-___.;A2

| (C4)

Where,
t= time, days
A = package area (both, nylon/PE multilayer and LDPE ﬁlms:
0.0528 m2)
q = amount of water permeated, g
P = ﬁlm permeability at 0 °C, 9 mill (m2 day mmHg)
l = ﬁlm thickness (both, nylon/PE multilayer and LDPE films: 3 mils)
Ap = water vapor pressure difference between the sides of the ﬁlm
(p1) and (p2), of which, p2 = 0 mmHg (at 0%RH), and p1: p5 (at
100% RH), where ps is the saturated water vapor pressure at 20,

15 or 10 °C listed in tables C4 and C6. Then, Ap=ps

Table C 8. Maximum predicted amount of moisture transported through package

at 100% RH and 0 °C after X months

 

 

 

 

 

 

 

 

 

 

 

Month
1 2 3 4 5 6
q (non-perforated LDPE), 9 0.2863 0.5727 0.8590 1.1454 1.4317 1.7181
q (Nylon/PE multilayer), g 1.1455 2.2910 3.4364 4.5819 5.7274 6.8729

 

81

 

APPENDIX D

MASS BALANCE CALCULATION AND MOISTURE CONTENT RESULTS

Notation:

W. = frost weight, g

Wd= calculated dry matter weight, 9

W": net weight, g

WWO = calculated initial weight of water in carrots, g
WM = predicted ﬁnal water weight in carrots, 9

Mo; = experimental initial moisture content, 87.5 %

Mcf= predicted ﬁnal moisture content, %

Equations for mass balance:
To calculate the initial amount of water in carrots:
Wm = Mei * Wn
To calculate the ﬁnal amount of water in carrots:
WM = Wwo - Wl
To calculate the dry matter amount:
Wd= Wn- Wm
To calculate the predicted ﬁnal moisture content:

Mgr: WM 1"‘100/ (WM + Wd)

82

treatment for different months

Table D 1. Average frost and glassy weight values (grams) for air freezer sub-

 

 

 

 

 

 

 

 

 

 

 

 

#1 #2 #3 #4 #5 #6
A-NPE 10.36 11.96 12.17 14.06 13.82 14.04
V-NPE 14.76 19.29 25.36 22.72 20.22 17.00
LDPE 8.90 10.09 11.18 12.27 11.43 10.74
P-LDPE 10.10 12.02 12.22 12.67 12.51 10.79

 

different months

Table D 2. Average net weight values (grams) for air freezer sub-treatment for

 

 

 

 

 

 

 

 

 

 

 

A-NPE 450.55 462.08 455.38 459.00 464.25 460.93
V-NPE 458.43 454.05 449.38 464.00 465.08 463.30
LDPE 447.45 449.63 446.00 447.65 450.13 461.75
P-LDPE 449.33 462.25 451 .38 435.70 450.70 426.48

 

freezer sub-treatment for different months

Table D 3. Average calculated initial water amounts in carrots (grams) for air

 

 

 

 

 

 

 

 

 

 

 

A-NPE 394.23 404.32 398.45 401 .63 406.22 403.31
V-NPE 401.12 397.29 393.20 406.00 406.94 405.39
LDPE 391 .52 393.42 390.25 391.69 393.86 404.03
P-LDPE 393.16 404.47 394.95 381.24 394.36 373.17

 

83

 

 

 

freezer sub-treatment for different months

Table D 4. Average calculated ﬁnal water amounts in carrots (grams) for air

 

 

 

 

 

 

 

 

 

 

 

-NPE 383.88 392.36 386.28 387.57 392.40 389.27
V-NPE 386.36 378.01 367.85 383.28 386.72 388.39
LDPE 382.62 383.33 379.07 379.43 382.43 393.29
P-LDPE 383.06 392.45 382.74 368.57 381 .85 362.38

 

sub-treatment for different months

Table D 5. Average calculated dry matter weight in carrots (grams) for air freezer

 

 

 

 

 

-NPE 56.32 57.76 56.92 57.38 58.03 57.62
V-NPE 57.30 56.76 56.17 58.00 58.13 57.91
LDPE 55.93 56.20 55.75 55.96 56.27 57.72
P-LDPE 56.17 57.78 56.42 54.46 56.34 53.31

 

 

 

 

 

 

 

 

 

Table D 6. Average calculated final moisture content in carrots (in %) for air

freezer sub-treatment for different months

 

 

 

 

 

 

 

 

 

 

 

A-NPE 87.21 87.17 87.16 87.11 87.12 87.11
V-NPE 87.08 86.95 86.75 86.86 86.93 87.02
LDPE 87.25 87.21 87.18 87.15 87.17 87.20
P-LDPE 87.21 87.17 87.15 87.13 87.14 87.18

 

84

 

freezer sub-treatment for different months

Table D 7. Average experimental ﬁnal moisture content in carrots (in %) for air

 

 

 

 

 

 

 

 

 

 

 

#1 #2 #3 #4 #5 #6
A-NPE 85.91 84.23 85.70 85.45 85.73 84.93
V-NPE 86.37 85.20 85.41 85.00 85.63 84.51
LDPE 85.67 84.98 86.32 85.50 85.67 85.48
P-LDPE 85.87 85.28 85.50 85.44 85.76 85.13

 

 

Table D 8. Average frost weight values (grams) for air freezer/retail sub-

treatment for different months

 

 

 

 

 

 

 

 

 

 

 

#1 #2 #3 #4 #5 #6
A—NPE 9.99 11.23 12.85 12.50 12.75 14.36
-NPE 16.22 15.86 24.57 27.21 27.01 21.14
LDPE 8.83 12.02 10.97 11.69 11.34 12.44
P—LDPE 8.97 9.27 11.53 10.95 12.53 10.93

 

 

 

for different months

Table D 9. Average net weight values (grams) for air freezer/retail sub—treatment

 

 

 

 

 

 

 

 

 

 

A—NPE 449.73 458.65 460.60 452.85 463.10 474.10

-NPE 446.43 456.03 453.23 460.58 458.80 448.53
LDPE 443.68 447.93 441.98 450.98 448.73 443.75
P-LDPE 430.00 434.50 453.60 450.43 455.53 420.78

 

 

 

85

 

freezer/retail sub-treatment for different months

Table D 10. Average calculated initial water amounts in carrots (grams) for air

 

 

 

 

 

 

 

 

 

 

 

-NPE 393.51 401.32 403.03 396.24 405.21 414.84
V-NPE 390.62 399.03 396.57 403.00 401 .45 392.46
LDPE 388.22 391 .93 386.73 394.60 392.63 388.28
P-LDPE 376.25 380.19 396.90 394.12 398.58 368.18

 

freezer/retail sub-treatment at different months

Table D 11. Average calculated ﬁnal water amounts in carrots (grams) for air

 

 

 

 

 

 

 

 

 

 

 

-NPE 383.52 390.09 390.18 383.75 392.47 400.48
V-NPE 374.40 383.17 372.00 375.79 374.44 371 .32
LDPE 379.39 379.91 375.76 382.91 381.29 375.84
P-LDPE 367.28 370.91 385.37 383.17 386.06 357.24

 

freezer/retail sub-treatment for different months

Table D 12. Average calculated dry matter weight in carrots (grams) for air

 

 

 

 

 

 

 

 

 

 

 

 

A-NPE 56.22 57.33 57.58 56.61 57.89 59.26
V-NPE 55.80 57.00 56.65 57.57 57.35 56.07
LDPE 55.46 55.99 55.25 56.37 56.09 55.47
P-LDPE 53.75 54.31 56.70 56.30 56.94 52.60

 

86

 

 

 

freezer/retail sub-treatment for different months

Table D 13. Average calculated ﬁnal moisture content in carrots (in %) for air

 

 

 

 

 

 

 

 

 

 

 

A-NPE 87.22 87.19 87.14 87.15 87.15 87.11
V-NPE 87.03 87.05 86.78 86.72 86.72 86.88
LDPE 87.25 87.16 87.18 87.17 87.18 87.14
P-LDPE 87.23 87.23 87.17 87.19 87.15 87.17

 

freezer/retail sub-treatment for different months

Table D 14. Average experimental final moisture content in carrots (in %) for air

 

 

 

 

 

 

 

 

 

 

 

 

#1 #2 #3 #4 #5 #6
A—NPE 86.44 85.80 85.70 85.45 85.84 85.64
V-NPE 85.79 85.20 85.41 85.00 85.33 85.21
LDPE 86.50 85.20 86.32 85.50 86.20 85.81
P-LDPE 86.03 84.89 85.50 85.44 85.80 84.60

 

87

 

 

APPENDIX E
MODELS FOR FROST FORMATION

a)ModeI:y=mt+w

Table E 1. Model parameters for frost formation for packed carrots under air

freezer and air freezer/retail sub-treatments (straight line)

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Sub-
treatment Air freezer/Retail Air freezer
Treatment P-LDPE LDPE A-NPE P-LDPE LDPE A-NPE
1.99 2.14 2.18 2.41 2.11 2.24
w, % (0.14) (0.21) (0.12) (0.21) (0.22) (0.12)
m, % month' 0.12 0.11 0.14 0.06 0.08 0.15
‘ (0.03) (0.05) (0.03) (0.05) (0.06) (0.03)
R7 0.75 0.50 0.85 0.25 0.33 0.85
9 standard deviation
3.50 - 7
3.30 - T T
310 . y=0.15x+2.24 l, I/A:
2.90 - t poex + 2.41
o\°2.70 t
17,250 1
0
“22.30 4 <-
2.10 1 E
= 0.0 + .
1.90J y R2_8);3:11
1.70 - T '
1.50 T i P r . T
0 1 2 3 4 5 6 7

Time, months
0 LDPE bags w/ pinholes o LDPE bags w/o pinholes e Nylon/PE bags

Figure E 1. Straight-line model for samples exposed to air freezer/retail sub-

treatment

88

4.00

 

 

 

 

3.50 , y=0.21:lx+2.18
3.00 l T R ".085 lflyeonx + 2.14
‘ *MJ’TT’TTT 2=050
3‘2 2.50 7 ,,,,, jgﬂi’ﬂ‘ -
4.: ﬂ-M :2:
3 200: f T y=0.12x+1.99
it 1.50 R2: 0.76
1.00 l
0.50 l
0.00 T T T TT T T 1
0 1 2 3 4 5 6 7

Time, months
9 LDPE bags w/ pinholes o LDPE bags w/o pinholes a Nylon/PE bags

Figure E 2. Straight-line model for samples exposed to air freezer sub-treatment

b) Model: y = b + (a-b) exp (—ct)

Table E 2. Model parameters for frost formation for packed carrots under air

freezer and air freezer/retail sub-treatments

 

 

 

 

 

 

Sub—
treatment Air freezer/Retail Air freezer
Treatment P-LDPE LDPE A-NPE P-LDPE LDPE A-NPE
1.75 1E-12 1.82 0.52 0.90 1.86
b, % (0.42)° (7.93) (0.37) (3.80) (1.90) (0.33)
2.84 2.636 3.06 2.73 2.54 3.21
a, % (0.54) (0.11) (0.29) (0.09) (0.13) (0.28)
0.32 1.78 0.39 1.51 1.06 0.38
c, % month" (0.43) (3.02) (0.31) (1.71) (1.12) (0.26)
R2 0.81 0.70 0.90 0.71 0.70 0.93

 

 

 

 

 

 

 

 

 

9 standard deviation

89

 

 

 

 

 

 

 

3. - - ~ - - - - —-— ..
5 1 F - 1;
3 ‘ l t g .
« / :_,—:.—:_’ , i -- :_ —'—‘1§
25 M’ , # St E I .L
.:_ 2 /../7*§””'T 3
(I) ’ /
e 15 /
LL
1 ._
0.5 1
0 7 T T T T T T
0 1 2 3 4 5 6

Time, months

. P—LDPE . LDPE . A-NPE

Figure E 3. Models for frost formation for bags in air freezer/retail sub-treatment

 

 

 

 

 

 

 

 

T T'
o " a
. ;_ T £__ ;
g r: 9 i:
r _ i l

l

l

0 T T T T T T ﬂ
0 1 2 3 4 5 6

Time, months

0 P-LDPE o LDPE o A-NPE

Figure E 4. Models for frost formation for bags in air freezer sub-treatment

90

APPENDIX F

Temperature proﬁles of the different sub-treatments are shown next. Figure F1

contains all the 6-month data for the air freezer sub-treatment. Figures F2 to F7

show the data for every month for the air freezer/retail freezer sub-treatment.

91

 

0 1 2 3 4 5 6
Time, months

Figure F1 . Temperature proﬁle of Air freezer wb-treahnent - All 6 Months

92

Temperature, F

30.0
25.0
20.0
15.0
10.0
5.0
0.0
-5.0
-10.0
-15.0

 

—- Air freezer — Retail freezer]

 

 

3
Time, months

Figure F2. Temperature profile of Air freezerIRetalI freezer sit-treatment -
Morilh

93

30.0
25.0
20.0
15.0
10.0

5.0

Temperature, F

0.0
-5.0
-10.0
-15.0

 

— Air freezer — Retail freezer

 

 

3
Time, months

Figure F3. Temperature proﬁle of Air freezer/Retail freezer sub-treatment -
Month 2

Temperature, F

30.0
25.0
20.0
15.0
10.0

.09-
CO

-5.0
-10.0
-15.0

 

 

L—Air freezer -— Retail frag]

 

3
Time, months

Figure F4. Temperature profile of Air freezer/Retail freezer sub-treatment -

95

Temperature, F

30.0
25.0
20.0
15.0
10.0
5.0
0.0
-5.0
-10.0
-15.0

 

—— Air freezer — Retail freezer

 

3
Time, months

Figure F5. Temperature proﬁle of Alr freezer/Retail freezer sit—treatment -

Temperature, F

30.0
25.0
20.0
15.0
10.0

9.01
CO

-5.0
-10.0
-15.0

 

l:—Air freezer -— Retail freezer

 

3
Time, months

Figure F6. Temperature profile of Air freezer/Retail freezer sub-treatment -
Month 5

97

 

—Air freezer — Retail freezer

 

30.0
25.0
20.0
15.0
10.0

5.0

Temperature, F

0.0
-5.0
-10.0

 

-15.0

3
Time, months

Figure F7. Temperature profle of Air freezer/Retal freezer atb-treatment -
Month

98

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