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Shelf Life Testing:
Procedures and Prediction Methods for Frozen
Foods
Bin Fu
Kellogg's Battle Creek MI
Theodore P. Labuza
Dept. of Food Science & Nutrition, University of Minnesota
1334 Eckles Ave., St. Paul, MN 55108
2
19.1 Introduction
The shelf life of a food can be defined as the time period within which the food is safe
to consume and/or has an acceptable quality to consumers. Just like any other food,
frozen foods deteriorate during storage by different modes or mechanisms, as
summarized in Table 1. Microbes usually are not a problem since they cannot grow at
freezing temperatures unless subjected to extensive temperature abuse above the
freezing point. Enzymes are a big concern for frozen foods, which can cause flavor
change (lipoxygenase) in non-blanched fruits and vegetables and accelerated
deterioration reactions in meat and poultry (enzymes released from disrupted
membranes during precooking). Cell damage or protein and starch interactions during
freezing cause drip and mushiness upon thawing. Discoloration could occur by nonenzymatic
browning, bleaching, and freezer burn. Vitamin C loss is often a major
concern for frozen vegetables. Physical changes, such as package ice formation,
moisture loss, emulsion destabilization, recrystallization of sugars and ice of frozen
desserts are often accelerated by fluctuating temperatures.
For any specific frozen product, which mode determines its shelf life, depends
on the product characteristics (raw materials, ingredients, formulation), pre-freezing
treatment, freezing process, packaging film and processes, and of course storage
conditions. All of the quality deterioration and potential hazards are usually
exaggerated or complicated by a fluctuating time-temperature environment (e.g.
freeze/thaw cycle) during storage. On the other hand, the shelf life of a frozen food
can be extended through ingredient selection, process modification and change of
package or storage conditions, as discussed in Section 3 of this book.
This chapter will focus on shelf life testing of frozen foods for product
development and market practices. Shelf life testing consists basically of selecting the
quality characteristics which deteriorate most rapidly in time and the mathematical
modeling of the change. Table 19.1 can be used as a reference for the selection of
quality characteristics, which depends on the specific product and usually requires
professional judgment. Mathematical modeling of quality deterioration will be
discussed next.
3
Table 19.1 Deterioration modes of frozen foods
Frozen Foods Deterioration Modes
Frozen meats, poultry and seafood Rancidity
Toughening (protein denaturation)
Discoloration
Desiccation (freezer burn)
Frozen fruits and vegetables Loss of nutrients (vitamins)
Loss of texture (temperature abuse)
Loss of flavor (lipoxygenase, peroxidase)
Loss of tissue moisture (forming package ice)
Discoloration
Frozen concentrated juices Loss of nutrients (vitamins)
Loss of flavor
Loss of cloudiness
Discoloration
Yeast growth (upon temperature abuse)
Frozen dairy products
(ice cream, yogurt, etc.)
Iciness (recrystallization of ice crystals)
Sandiness (lactose crystallization)
Loss of flavor
Disruption of emulsion system
Frozen convenience foods Rancidity in meat portions
Weeping and curdling of sauces
Loss of flavor
Discoloration
Package ice
Frozen bakery products (raw dough,
bread, croissants)
Burst can (upon temperature abuse) (dough)
Loss of fermentation capability (dough)
Staling (becoming leathery)
Loss of fresh aroma
19.2 Modeling of quality deterioration
19.2.1 Basic equation
A frozen food starts to degrade once it is produced (Figure 19.1). The rate and
the degree of degradation depends on both the composition and the environmental
conditions during storage and distribution. In general, the loss of food quality or shelf
life is evaluated by measuring a characteristic quality index, "A". The change of quality
index A with time (dA/dt) can usually be represented by the following kinetic equation:
- dA/dt = k An (19.1)
where k is called a rate constant depending on temperature, product and packaging
characteristics; n is a power factor called reaction order which defines whether the rate
4
of change is dependent on the amount of A present. If environmental factors are held
constant, n also determines the shape of deterioration curve.
Ao
A a
b
c
t
d
e
Figure 19.1 Quality deterioration curves: a) linear; b) exponential;
c) hyperbolic; d) quadratic; e) complex.
19.2.2 Zero and first order kinetics
Equation 19.1 can also be written as:
f(A) = k t (19.2)
where f(A) is the quality function, k and t are the same as above. The form of f(A)
depends on the value of n. When n is equal to zero it is called zero order reaction
kinetics, which implies that the rate of loss of quality is constant under constant
environmental conditions (curve (a) in Fig. 19.1). If n is equal to one it is called first
order reaction kinetics, which results in an exponential decrease in rate of loss as
quality decreases (curve (b) in Fig. 19.1, which becomes a straight line if plotted on a
semi-log plot). These quality functions can be expressed as follows:
f(A) = Ao - A = kzt zero order (19.3a)
f(A) = ln Ao - ln A = kft first order (19.3b)
5
where Ao is the initial quality value. If Ae corresponds to the quality value at the end of
shelf life, the shelf life (q) of the food is inversely proportional to the rate
constant:
q = (Ao - Ae) / kz zero order (19.4a)
q = ln (Ao/Ae) / kf first order (19.4b)
It should be noted that most chemical reactions leading to quality loss in frozen
food systems are much more complex. However, the reaction kinetics can be
simplified into either pseudo-zero order or pseudo-first order kinetics. In the case of
complex reaction kinetics with respect to reactants, an intermediate or a final product
(e.g. peroxides or hexanal in lipid oxidation ) could be used as a quality index. There
are few cases where neither zero nor first order kinetics apply. Curve (c) in Fig. 19.1
shows the degradation curve for a 2nd order reaction (with single reactant), which also
shows a straight on a semi-log paper. A fractional order should be used to describe
the curve (d) in Fig. 19.1.
Sometimes, there is an induction period or lag time before the quality
deterioration begins (e.g. browning pigment formation in the Maillard reaction or a
microbial growth lag phase, as shown in curve (e) in Fig. 19.1. The length of the lag
depends on many factors, but temperature is a predominant factor. Given this,
modeling of both the induction or lag period and deterioration phase are necessary for
accurate prediction of quality loss or shelf life remaining. An example of such work has
been demonstrated by Fu et al. (1991) for the growth of bacteria in milk.
In certain circumstances (e.g. A represents a sensory hedonic score), a nonkinetic
approach, e.g. a statistical data fitting technique can also be used to describe
the deterioration curves. Varsanyi and Somogyi (1983) found that the change in
quality characteristics as a function of time could be approximately described with
linear, quadratic and hyperbolic functions and that storage temperature and packing
conditions affected the shape of the deterioration curves. However, the parameters
determined by data fitting are difficult to use for prediction under variable storage
conditions except for the linear curve.
19.2.3 Temperature dependence of deterioration rate
19.2.3.1 Arrhenius kinetics
Once a frozen product is made and packaged and starts its journey from the
manufacturer's plant to warehouse, distribution center, retail store and finally
6
consumer's freezer, the rate of quality loss is primarily temperature dependent
(Zaritzky, 1982). The Arrhenius relationship is often used to describe the temperature
dependence of deterioration rate where for either zero or first order:
k = ko exp (-Ea/RT) (19.5a)
or ln k = ln ko - Ea/(RT) (19.5b)
where ko is a pre-exponential factor; Ea is an activation energy in cal/mol; R is the gas
constant in cal/mol K and equal to 1.986; T is an absolute temperature in K (273 + °C).
Thus, a plot of the rate constant on semi-log paper as a function of reciprocal absolute
temperature (1/T) gives a straight line as shown as Fig. 19.2. The activation energy is
determined from the slope of the line (divided by the gas constant R). A steeper slope
means the reaction is more temperature sensitive, i.e., a small change in T produces
are large change in rate.
Figure 19.2 Arrhenius plot
ln k
1/T
slope = -Ea/R
Thus, by studying a deterioration process and measuring the rate of loss at two
or three temperatures (higher than storage temperature), one could then extrapolate
on an Arrhenius plot with a straight line to predict the deterioration rate at the desired
storage temperature. This is the basis for accelerated shelf life testing (ASLT), which
will be discussed later. One should note however that in some cases a straight line
will not ensue for a variety of reasons, especially if a phase change occurs (Labuza
7
and Riboh, 1982). Thus for frozen foods, extrapolation from temperatures above 0¥C
are meaningless for shelf life prediction.
19.2.3.2 WLF kinetics
Besides the Arrhenius equation, another popular equation at least in the more recent
food literature, is the Williams Landau Ferry (WLF) model (Williams et al., 1955). Its
original form was based on the variation of the viscosity in the temperature range
above Tg as addressed in Chapter 3. When the rate constant at Tg' is substituted for Tg
(Tg' is the Tg of a maximally freeze-concentrated system), the WLF model can be
written as follows:
log (kT/kg) = C1(T-Tg')/[(C2+(T-Tg')] (19.6a)
or [log (kT/kg)]-1 = (C2/C1)/(T-Tg') + 1/C1 (19.6b)
where C1 and C2 are constants. Thus a plot of [log (kT/kg)]-1 vs. (T-Tg)-1 will be a
straight line with the slope equal to C2/C1 and the intercept equal to 1/C1. As can be
seen this is a two parameter temperature dependent model as is the Arrhenius
equation.
Frozen foods stored below Tg' are stable to ice recrystallization and other
physical changes. Levine and Slade (1988) postulated that stability is related to the
temperature difference between storage temperature and Tg'. This cryostabilization of
foods assumes stability below Tg' and rapid decrease of stability above Tg' according
to the WLF relationship, exhibiting an increase in reaction rate, much higher than
expected from the Arrhenius kinetics. However, this may not be true since the rate of
chemical reactions can be expected to be influenced by temperature increase in a
complex way: (i) an increase of the rate constant, resulting from both the viscosity
decrease and the increased molecular mobility (Fennema 1996); (ii) a decrease of the
reaction rate as a consequence of the increasing dilution of the reactants Roos et al.
(1996). For these reasons, it seems that the WLF model over predicts the temperature
effect of rate constant (Simatos et al., 1989). As noted by Nelson and Labuza (1994),
because of the small temperature range over which foods are stored, e.g., about D30°C
for dry foods and D20°C for frozen foods, both the Arrhenius and the WLF model give
good correlations as long as one does not use the universal coefficients suggested by
Slade and Levine (1991). In fact as shown by Nelson and Labuza (1994), their use of
the Lim and Reid (1991) data for enzymatic activity in the frozen state as shown in 19.3
is not proof that the Arrhenius relationship does not apply, WLF was assumed because
the rate was negligible below -10°C which was the measured Tg. But as seen in
8
Figure 19.3b if the data is plotted as Arrhenius plot an r2 of 0.999 ensues. The
challenge in applying the WLF model for stability or shelf life prediction is that (1) Tg is
not known; (2) Tg is difficult to determine; and (3) the universal coefficients of Levine
and Slade (1986) are not applicable.
0 50 100 150 200 250
0
1
2
3
4
5
-3.5
-5.5
-8.5
-13
-19
Time (hours)
Relative absorbance
Temperature (°C)
0.0037 0.0038 0.0039
-4
-3
-2
-1
0
1/T (K-1)
ln(k)
y = 79.497 - 2.1621E+4x R2 = 0.999
Figure 19.3 Hydrolysis of maltodextrin in the frozen state (Lim and Reid; 1991)
a. Rate as a function of temperature (Note Tg is -10 ¡C)
b. Arrhenius plot
19.2.3.4 Shelf life model
Most published data related to quality deterioration do not give rates or rate constants
but rather are in the form of an overall shelf life (end-point analysis) as a function of
storage temperature. Since the temperature range used is usually quite narrow, the
following exponential relationship exists between shelf life and storage temperature:
q = exp(-bT+c) (19.7a)
or ln q = -bT+c (19.7b)
where q is shelf life at temperature T in °C, b is the slope of the semilog plot of q vs T
and c is the intercept or reference temperature as shown as Fig. 19.4. Practically, this
is used frequently for shelf life determination and prediction due to its simplicity and
straightforwardness.
9
Figure 19.4 Shelf life plot
ln q
T
19.2.3.4 Q10 or q10
The Q10 approach is also often used for estimation of the temperature acceleration of
shelf life, which is defined as :
Q10 = rate @ T1+10 °C / rate @ T1 (19.8a)
Q10 = shelf life @T1 / shelf life @T1+10 °C (19.8b)
Q10 = (q10)1.8 (19.8c)
where T1 is temperature in °C. If the temperature unit is in °F, then the term q10 is
used, which in fact is more often used than Q10 in the frozen food literature.
The magnitude of Q10 depends on the food system, the temperature and the
absolute range. Q10 values from 2 up to 20 have been found for frozen foods (Labuza,
1982) Labuza and Schmidl, 1985. Q10 can be shown to be related to the Arrhenius
equation and the shelf life model through the following expression:
Q10 = exp [10 Ea/(R T (T+10)] (19.9a)
Q10 = exp (10 b) (19.9b)
Thus Q10 is not constant but depends on Ea and the absolute temperature T.
Some data gleaned from July (1989) and Labuza (1982) is shown in Table 19.2.
10
Table 19.2
Estimate of the Q
10
for shelf life of selected frozen foods
Days of HQL
I te m - 10°C - 20°C Q 1 0
pork sausage 20 120 4
pork 50 400 8
beef 60 200 3.3
ground hamburger 250 800 3.2
fried hamburger 35 250 7
raw poultry 200 700 3.5
fried poultry 25 700 3.2
fatty fish 7 60 9
19.2.3.5 Other models
The following models have also been proposed to describe the temperature
dependence of the rate constant (Kwolek and Bookwalter, 1971) for frozen systems:
kT = a + b T (19.10a)
kT = a Tb (19.10b)
kT = a / (b - T) (19.10c)
where a, and b are constants. In most cases, Equation 19.10c fits data better.
However, all these have very limited practical application.
19.2.4 Time-temperature tolerance
Frozen foods are often exposed to a variable temperature environment, e.g. during
distribution or due to freezing/defrosting cycle in retail or home freezers. In general, the
value of the quality function, f(A), at time t under changing environmental conditions
can be estimated from:
f(A) = ò k[T(t)] dt (19.11)
where T(t) is the temperature as a function of time. The form of f(A) depends on the
reaction order as discussed previously. If an effective temperature, Teff, is defined as
11
that constant temperature exposure which causes the same quality change as the
variable temperature condition, as proposed by Schwimmer et al. (1955), then
f(A) = keff t (19.12)
The rate constant at that defined temperature is termed the effective rate constant, i.e.
keff. To estimate the quality change under variable temperature conditions, one
needs to either solve for f(A) numerically or know the value of Teff or keff that
corresponds to the variable conditions.
The numerical approach for a randomly variable temperature history is
essentially the same as the Time/Temperature/Tolerance (TTT) approach initiated by
Van Arsdel et al. (1969) and derived empirically in the 1960's for the prediction of shelf
life of frozen foods (July, 1984). It is assumed that the temperature history of the
product is known. Thus the fraction of shelf life consumed, fcon, was calculated as the
sum of the times at each temperature interval, ti, divided by the shelf life at that
temperature, qi:
fcon = S (ti / qi) (19.13)
Thus the remaining shelf life at a reference temperature is equivalent to (1-fcon)*q.
Equation 19.13 assumes that the rule of additivity is valid for frozen foods (July,
1984), which means that the loss of remaining storage life or quality can be calculated
from knowledge of the prior time-temperature episodes the product has been exposed
to. This also implies that the prior sequence of the time-temperature episodes is of no
importance except to calculate the amount of quality remaining up to that time, i.e.
there is no history effect. If the rule of additivity is valid with reasonable accuracy, the
use of time-temperature integrators (TTI) should provide reliable results with respect to
prediction of shelf life remaining, which will be discussed later.
However, there are some cases where the total effect of various temperature
experiences may not be independent of the order in which they occur or of the nature
of temperature history. For example, widely fluctuating temperatures may cause
freezer burn or in-package desiccation, which is not additive (July, 1984). Where the
colloidal nature of a product is affected, the effect of time-temperature history may not
be additive either, especially with a freeze/thaw cycles. This is also true when growth
of microorganisms occurs (Fu et al., 1991). Certain chemical reactions, enzymatic as
well as nonenzymatic, could even proceed more rapidly at temperatures below
12
freezing. This is called a negative effect of temperature (Singh and Wang, 1977),
which could be caused by one or more of the following factors: (1) a freeze
concentration effect; (2) the catalytic effect of ice crystals; (3) a greater mobility of
protons in ice than in water; (4) a change in pH, up or down with freezing; (5) a
favorable orientation of reactants in the partially frozen state; (6) a salting in or out of
proteins; (7) decrease in dielectric constant; and (8) the development of antioxidants at
higher temperatures. As has been shown by Fennema (1975), the freeze
concentration effect can cause rates of chemical reactions to increase dramatically just
below the freezing point (Figure 19.5), e.g. ascorbic acid loss at -3°C can be faster
than at higher temperatures this one should not use data in the -4°C to 0°C range or
above as part of an accelerated shelf life test to predict rates at lower temperatures.
Fennema (1975), showed that the time to 50% loss of vitamin C in broccoli was 44
days at -5°C, 120 days at -2°C and 162 days at +2°C. This concentration effect is
evident in the shelf life plot of frozen strawberries as shown in Fig. 19.6 using the data
of Guadagni (1968). If the data collected only at 25 and 30°F (-3.9°C and -1.1°C) are
used, the predicted shelf life at 0°F (-17.8°C) is over 27 years, if data are collected at
only 20 and 25°F (-6.7 and 3.9°C), the shelf life predicted at 0°F is 40 days while data
below 20¥F extrapolated to the true expected shelf life is about 280 days.
Figure 19.5 Rate of chemical reaction as a function of temperature
above and below the freezing point of a food.
13
Figure 19.6. Shelf life plot of frozen strawberries showing the
influence of the freeze concentration effect just below the freezing
point on prediction of shelf life at 0¡F . Data from Guadagni (1968).
Each line represents a regression through a different selected set of
temperatures.
The response ratio of the food to changes in environmental temperature (RT) is
dependent on the fluctuating temperature conditions as well as the heat transfer
properties of the food as well as the package (Cairnes and Gordon, 1976; Dagerskog,
1974). In the analysis of food shelf life, an inherent assumption is made that the food
is responding instantaneously to the environmental temperature changes, i.e., RT = 1.
This may be acceptable if a surface deterioration process is the deterministic factor for
shelf life, e.g. mold growth in some foods. Freeze-defrost cycles generally can be
considered as sinusoidal oscillations. The amplitude of the effect is reduced inside the
package by some factor thus RT. < 1. It can be expected that the shorter the period of
the ambient variation the smaller the RT, and hence the smaller the amplitude of the
cyclic temperature variation in the package. Zuritz and Sastry (1986) also studied the
effect of packaging materials on temperature fluctuations for frozen ice cream and
found that packaging materials coupled with a layer of stagnant air were effective
barriers against thermal fluctuations.
19.2.5 Hazard function
14
After the product is produced, it may fail at any point in time in accordance with its life
distribution (Nelson, 1972). The hazard function h(t) of a distribution is defined for t ³ 0
by:
h(t) = f(t)/[1-F(t)] (19.14)
where f(t) is a probability density function and F(t) is a cumulative distribution function.
The h(t) is the conditional probability of failure at time t, given that failure has not
occurred before ..
The behavīor of a hazard function for studying the shelf life of food products can
be easily understood by examining the "bathtub" shaped curve in Fig. 19.7. Note that
at time to, a frozen food product begins its journey to many distribution outlets for
consumption. During the time between to and t1, early failures may occur owing to a
failure in the process itself, faulty packaging, extreme initial product abuse, and many
other environmental stresses to which the product is subjected. Early failure should not
be taken as a true failure relative to the shelf life of the product unless it represents the
normal condition. From t1 to t2 one can expect, barring chance major temperature
fluctuations, no failures. This interval represents the true period of the product's
stability. The failure rate is almost constant and small during this time. The hazard or
failure rate increases from time t2 to the termination point t3, owing to the true
deteriorative changes occurring within the product. The concept of hazard function is
important in the analysis and interpretation of the failure times of a product.
Time
to t1 t2 t3
Early
failure
Period of product stability
Failure due to
product
deterioration
Figure 19.7 Failure rate as a function of time
15
A fundamental assumption underlying statistical analysis of shelf life testing is
that the shelf life distribution of a food product belongs to a family of probability
distributions and that observations are statistically independent. Parameters of a shelf
life distribution are estimated by use of shelf life testing experimental data. Once the
parameters of a shelf life model have been estimated, it can be used to predict the
probabilities of various events, such as future failures (Nelson, 1972). Five statistical
models, normal, log normal, exponential, Weibull and extreme-value distributions
were tested for a few food products (Gacula and Kubala, 1975; Labuza and Schmidl,
1988) and it was found that the Weibull distribution fits best, which will be
demonstrated later.
19.3 Shelf life testing — overall aspects
19.3.1 Purpose
In the development of any new food product including reformulating, change of
packaging or storage/distribution condition (to penetrate into a new market), one
important aspect is the knowledge of shelf life. The shelf life of a food product is vital to
its success in the marketplace. This life must at least exceed the minimum distribution
time required from the processor to the consumer. Shelf life testing can assess
problems that the product has in the development stage, following a "fail small fail
early" philosophy, thereby eliminating large disasters later. Marketing/brand managers
also need reliable shelf life data to position the products and to establish the brand.
Periodic determination of shelf life help to provide assurance that the product remains
consistent over time with respect to quality.
Different shelf life testing strategies are necessary at different stages, as
illustrated in Fig. 19.8. If the objective is to identify whether pathogens and spoilage
microbes will grow in the case of temperature abuse, then a challenge study is
necessary. If the objective is to quickly estimate the approximate shelf life of the
product then an ASLT can be used, as long as the proper temperature range is
chosen. A confirmatory shelf life test may be conducted at the last stage with
simulated distribution chain conditions, although in today’s R & D environment, this
may be skipped.
16
Product concept
Prototype development
Pilot line testing
Scale-up line trial
Full line production
Marketplace
General stability information
Challenge Study
Accelerated shelf life testing
Confirmatory storage study
On-going shelf life monitoring
Figure 19.8 Shelf life testing strategy at different product development stages
19.3.2 Shelf life criteria
The criterion for the end of shelf life may be variable depending on the definition of
product quality grade, so the shelf life of a product may also be variable. The shelf life
of most perishable and semiperishable foods is almost solely based on sensory
quality. For example, fresh meat degrades mainly by bacterial activity and rapid
chemical oxidations that cause an off-flavor development and loss of color. This is
readily recognizable by consumers. In contrast, many longer shelf-life foods including
most frozen foods degrade mainly by slow chemical reactions such as loss of
nutritional value. For example, the vitamin C content of some frozen fruits and
vegetables, may fall below the required standard as listed on the label before sensory
quality becomes inadequate.
The criteria for shelf life may also vary depending on the sensitivity of the
consumer. For consumers, taste, odor, and appearance are the most obvious criteria;
in academia and in the industry, sensory evaluation correlated with instrumental
measurements of a given quality index (e.g., vitamin C level) are usually conducted. In
general, the criteria level corresponding to the end of shelf life of a product depends
17
on: (i) any legal requirement, e.g. zero tolerance for botulinum toxin; (ii) consumer
preferences or marketing requirements; and (iii) cost. In essence, the end of shelf life
depends on the percentage of consumers a company is willing to displease. If 100%
acceptance is required then high cost ingredients and absolute control of distribution
up to point of consumption is necessary, otherwise there will always be some people
who will get foods beyond shelf life. The aim is to keep this as small as possible.
19.3.2.1 Just noticeable difference (JND)
Sensory (organoleptic) examination of foods was a general procedure used by the
human race to evaluate wholesomeness of foods long before the discovery of
microorganisms. Sensory evaluation of foods by scientific methods can be used to
evaluate such attributes as taste, odor, body, texture, color and appearance. Changes
in these attributes may be brought out by microbial or non-microbial actions, usually
the latter for frozen foods.
The methods used to evaluate sensory shelf life data include difference testing
and hedonic scoring. Difference testing can involve paired comparisons, duo-trio
tests, or triangle tests. The paired comparison procedure determines the time when a
measurable difference in quality occurs between two test samples at a certain level of
probability. When applied to frozen foods, this method is often referred to as the Just
Noticeable Difference (JND) test or High Quality Life (HQL) test (July, 1984), which is
usually based on flavor changes. Duo-trio testing compares two unknowns to an
unabused control sample and asks the question of whether either of the unknowns are
the same as or different from the identified control. Triangle testing determines the one
different product among three test samples presented randomly to a set of judges (at
least 10). Probability plots are used to predict shelf life at a given probability level.
The difference method can result in finding a difference when none really exists (Type
I error), or not finding one when indeed there is a true difference (Type II error).
Labuza and Schmidl (1988) have discussed this topic more thoroughly in relationship
to shelf life testing, which is not commonly found in sensory textbooks. Table 19.3
shows some data from Guadagni (1968) for HQL of frozen foods.
18
Table 19.3
Days of High Quality Life for fruits and vegetable (from Guadagni 1968)
P roduct T yp e 0 °F 1 0°F 2 0°F
apples pie filling 360 250 60
blueberries pie filling 175 77 18
cherries pie filling 490 260 60
peaches retail syrup 360 45 6
blackberries bulk, no sugar 630 280 50
raspberriesbulk, no sugar 720 315 70
retail, syrup 720 110 18
strawberries bulk, sugar 630 90 18
retail 360 60 10
green beans retail 296 94 30
cauliflower retail 291 61 13
peas retail 305 90 27
spinach retail 187 57 23
corn retail 720 360
corn on cob retail 275 150
19.3.2.2 Hedonic scoring
Hedonic scoring — which indicates acceptance on a numerical scale, e.g. a 1-9 point
scale labeled from "dislike extremely" to "like extremely", is typically used for shelf-life
evaluation. The test can be designed to not only evaluate the overall acceptance of the
product, but that of specific characteristics such as flavor, texture, appearance,
aftertaste, etc. Trained panels can also use this technique on a line scale, which can
be converted to numerical equivalents.
If the hedonic method is used to evaluate shelf life, one can simply use the
score as quality index A and plot the score vs. storage time, run a linear regression,
and choose the end of shelf life as the time when the progressed value drops below a
pre-set level (Waltzeko and Labuza, 1976; Gacula, 1975). The shelf life determined in
this way is called the practical shelf life (PSL) for frozen foods (July, 1984), and is
longer than the HQL or JND. The use of hedonic rating scales may be of limited use in
shelf life testing, yet it is probably the most used method. Many food companies use a
loss in hedonic score equal to D=0.5 for HQL and D=1.5 for PSL as the end of shelf life
19
(Labuza, 1982). Objective measurements and professional judgment are often
required to determine the end point. Data in Table 19.4 from an report published by
the former Refrigerated and Frozen Foods Institute (1973) Unfortunately there were no
methods given, but the data suggests that the PSL is about 2 to 3 times longer than the
HQL value. This in itself suggests that the HQL methods can be used to shorten shelf
life testing times.
Table 19.4
Relationship between practical shelf life (PSL)
and High Quality Life for frozen foods.
F rozen Food P SL/HQL Rati o
lean meat 1.9 - 2
fatty meat 2.0-2.4
lean fish 1.9-2.2
fatty fish 2.4-2.7
precooked foods 2.8-3.0
fruit 2.8-3.1
vegetables 3.1-3.5
19.3.2.3 Instrumental analysis
Chemical or instrumental analysis, such as moisture, nutrient loss, free-fatty acids or
color measurement that closely correlate to sensory attributes, can supplement
sensory techniques. They are usually less expensive and less time-consuming than
sensory approaches. A correlation between a physical or chemical test can increase
the confidence level of the sensory results. For example, the following constituents or
properties can be considered for monitoring chemical changes of pizza quality during
frozen storage: total free fatty acids, specific volatile free fatty acids by HPLC,
peroxides, oxidative volatiles (e.g., hexanal) by GC, spice volatiles by GC, lysine, color
(decrease in red color or increase in brown), in addition to sensory evaluation of taste
and flavor (Labuza, 1986). Most sensory experts agree that analytical methods should
complement the sensory tests. Vice versa, the endpoint determined by objective
measurements should be confirmed by sensory techniques as well.
20
19.3.2.4 Weibull Hazard analysis
The Weibull Hazard procedure requires one to first make an estimation of the time to
the end of shelf life. This becomes the initial estimated time limit for the study. The time
limit is then divided into several segments at which points panelists grade the product.
Additional panelists are added at a constant number for each subsequent time period
to maximize the number of testers near the end of the test. The panelist is asked to
grade the food as good (acceptable) or bad (unacceptable), i.e. no ranking on a
hedonic score. When the product is identified as unacceptable by 50% of the
panelists, the number of testers for the next period is increased by the number of failed
samples plus the constant number. The interval between sample times is also
shortened as the end of shelf life gets closer. The test ends when no more samples or
panelists are available. The scores are ranked and the cumulative hazard calculated.
The critical probability of failure Pc, can then be calculated from the following equation:
Pc = 100 (1 - exp(-å(H/100))) (19.15)
where H is the hazard value equal to 100/Rank. Choosing Pc = 50%, corresponds to
an accumulated hazard value of 69.3%.
The relationship between the logarithm of storage time (log t) and the logarithm
of hazard value (log H) is linear:
log t = (1/b) log H + log a (19.16)
where b is the shape parameter and a is the scale parameter. The shelf life can then
be determined based on the desired probability level allowed for product failure. The
lower this probability, the shorter the shelf life. This plot then allows one to make a
management decision with respect to the probability of displeasing a certain fraction of
consumers. It is hoped that the distribution time is such that greater than 99 percent of
the product is consumed before the end of shelf life based on displeasing less than
X% of consumers where X is the economic value. An detailed example was given by
Labuza and Schmidl (1988). It should be noted that this process can also be used for
simple analytical tests such as plate counts or vitamin C. In these cases the number of
panelists are replaced with the number of samples tested. Some criterion such as 20%
vitamin C loss is used as the negative response. Figure 19.9 shows an example of
Weibull plot for a frozen food based on assumed data. A shelf life of 16 months is
21
found at Pc = 50% from the graph. From this graph then, if 95% of the food were
distributed and consumed in 3 weeks, only 1% of the consumers would be displeased
.01 .1 1 10 100 1000
1
10
100
Cumulative hazard (%)
Shelf life (wk)
Probability (%)
0.01 0.1 1 10 50 99.99
Figure 19.9 An example of Weibull plot for a frozen food.
A shelf life of 16 wk was determined at Pc = 50%.
(or 0.95% of the product is out of compliance). If the rest were held and consumed at
10.5 weeks, 50% of those eating it would have out of quality food or another 0.5 x 5%
= 2.5% of product. Thus in this distribution model about 3.5% of the product is
unacceptable. To improve on this, the product must either move faster or one must
distribute it at a lower temperature. Wittinger and Smith (1986) used this approach to
determine sensory shelf life of ice cream based on iciness and found a shelf life of 5
weeks at 0°F (-15.5) which fits the general data for iciness in ice cream as shown in
Figure 19.10 (Labuza, 1982). It should be noted that this gives a Q10 of about 12.
22
.1
1
10
100
Temperature °C
1
10
100
-30 -20 -10 0
0.1
weeks
Figure 19.10 Shelf life plot for ice cream based on icyness
perception from data of Labuza (1992)
19.3.3 General procedures
Shelf life testing experiments are designed to measure the average shelf-life of a
product under given conditions. General procedures for shelf life testing of foods were
proposed by Labuza and Schmidl (1985), which include:
Step 1: Develop testing protocol — The protocol should consist of: i) specific
objective; ii) detailed test design in terms of product, package, and storage condition;
iii) execution procedures in terms of time, space and resource availability; iv) cost
estimation.
Step 2: Identify key quality indicator — Any previous shelf life data and kinetic
parameters of food deterioration available in the literature (Labuza, 1982; Man and
Jones, 1994) or the distribution turnover time of a similar or a competitive product in
the market place, if any, would be very helpful in this preliminary identification or in
determining the shelf life requirement.
Step 3: Estimate product sample and control needs — The number of samples
and controls required should be based on the detailed experimental design. If
sufficient product is available, extra samples should be placed into each storage
23
condition. Now and then it may be necessary to recheck a sample, especially if a value
is not in line with other data. It would be disastrous to be out of sample before failure
has occurred or the predetermined termination of the test is reached. Extra controls
should also be prepared and stored. When the samples are placed into storage
rooms, they should be positioned so that the complete package is exposed to the
external atmosphere, unless otherwise specified. The specific location of the test
sample should be recorded. Temperature controllers should be checked for accuracy,
periodically. In addition, removal of all unused samples from the storage room to make
space for future studies is a must.
There are various thoughts when it comes to using a control product. Some
sensory experts prefer an actual physical control; others are satisfied to just use the
numbers obtained in the zero time evaluation. There are three alternatives when using
a physical example as a control: (i) making the control from scratch each time using
the same ingredients, procedures, etc.; (ii) deep-freezing the control (e.g. pizza held at
-70 °C) and accepting that it might have changed slightly, but minimally compared to
the product in shelf life; (iii) using a fresh batch of product which may not be identical.
Step 4: Select proper package materials and package size — This is largely
dependent on shelf life requirements, packaging costs and availability, and consumer
information. Factors such as vacuum packaging, nitrogen flushing, or use of
antioxidants are often considered in combination with packaging materials.
Step 5: Choose storage conditions — Storage conditions are chosen based
on the type of shelf life testing. For example, the intended commercial
storage/distribution temperature range should be used in confirmatory shelf life testing.
Elevated temperatures are often used in accelerated shelf life testing to obtain data for
prediction of shelf life at lower temperature or for prediction of shelf life under variable
time-temperature distributions. Humidity control and/or monitoring is less important for
frozen foods as compared to other foods (e.g., snacks, cakes, pies, and pastries).
Light in the room should be properly controlled depending on the package.
Step 6: Estimate sampling frequency and duration of testing — The sampling
frequency is generally an estimation based upon experience from prior studies with
similar foods. However, once one knows an interval at one temperature, then the
intervals at other temperatures can be estimated using a Q10 value i.e., if the Q10 is 3
then for a 10°C lower temperature the sampling times can be 3 times longer. If the
interval between sampling is too long, the risk of under- or over-estimating shelf life
increases. The more analyses that are completed, the more accurate will be the shelf
life determination.
24
The question as to when one should end the experiment must be based on
some pre-set criteria for failure. One criterion could be the minimum shelf life
requirement driven by product category, distribution chain, and the benchmark's
product stability. If there is an accompanying sensory test, the end time can be based
on some organoleptic inferior quality criteria from which one then can get a microbial
or chemical index limit. For frozen products, several weeks to months are usually
needed. If the shelf life can be estimated with any accuracy, the test intervals can be
lengthened and clustered around the expected failure period. Most of the experts only
require about six evaluations to provide reliable results.
Step 7: Schedule for execution — Before scheduling the starting date for a shelf
life test, one must check for the availability of ingredients, packaging materials, and
storage space, and the time and resource available in the pilot plant or in the
processing plant to prepare the samples. One should also check for the time and
resources available in the microbial lab, the analytical lab and/or the sensory support
staff throughout the test period. A copy of the test request and schedule should be
sent in advance to those who will be doing the work. The courtesy of providing those
involved with this advance information always pays dividends. Holidays should be
marked on the scheduling calendar, since scheduling too many evaluations near
major holidays or Friday afternoon is not recommended. However, once scheduled,
sample observations on weekends and holidays should not be skipped over, since
important data points could be missed.
Step 8: Take sample and evaluate quality — Samples should be taken and
evaluated following pre-determined schedules. Sampling plans should be
administratively and economically feasible, taking into account the heterogeneity of
the food. Maxcy and Wallen (1983) pointed out the problem of heterogeneity of
samples in shelf life prediction. Multiple subsamples (³ 3) should be done for nonhomogenous
samples. A single package is usually used as an experimental unit.
Replication of 3 or 4 units are desired for each measurement. For frozen foods, a
thawing process is often involved in the sampling procedure. Proper thawing or
microwave heating is critical to the product quality. All samples should be thawed or
microwaved in the same way to minimize any biases.
The intended analyses should be based on the specific mode of deterioration,
which was discussed earlier. Whatever the choice, the tests should be reasonable and
logical. The key is to make sure that one is measuring the right thing. If the wrong
quality factor is measured, the test starts out a failure. Unfortunately, in many cases this
cannot be established initially, so sensory evaluation is a must in almost all shelf life
25
tests. Key sensory evaluation techniques for frozen foods have been discussed
before.
At the time of each pull, one unit of the sample should be evaluated (informally
by a minimum of 2-3 people) for changes in flavor and texture. This should be done in
addition to the final tasting prior to a consumer sensory test. This is necessary since it
helps the developer know approximately how the product is doing during the progress
of the shelf-life, helping to avoid any surprises in the results. Control samples may
need to be prepared fresh.
Step 9: Analyze data — Shelf life is the predicted day at which the stored
product (test pull) is X% less than the control at day zero (Reference). The data should
be plotted and regressed to determine that point using the proper model (zero or first).
All too often the data are not analyzed until the experiment is over and then the
scientist finds that nothing can be concluded because of lack of points or a poor fit or
some surprises. Statistical curve fitting should be consistent with the chosen model
based on a theoretical mechanism. The amount of change and number of data points
are related to the coefficient of variation (CV) of the test. A weighting factor may be
used in estimating the rate constant and its statistical limits. When the data for an
attribute does not fit the regression model well (adjusted R2 of < 0.8), scientific
judgment should be used to decide whether the data are applicable.
When in doubt, a rerun on retention samples might help understand or clarify
the results. Error analysis could be performed before experiments are run by first
finding inherent errors in time, temperature, and quality index measurements, then
calculating an expected standard deviation for the plot being used to determine a rate
constant. If the experimental data have a standard deviation much higher than the
expected value, either the functional form of the rate expression is incorrect or the data
contain errors from unanticipated sources.
Step 10: Prepare shelf life report — Depending on the type of shelf life
determination, the results should either throw light on the technical viability of the
product or provide answers to the questions about the maximum safe shelf life as well
as the maximum quality shelf life of the product. Before a shelf life is finally set, factors
in the scale-up of shelf life data will need to be taken into consideration. Based on
results from ASLT, the provisional shelf life will be set for the product. There is no
government regulation which defines the product end point except for that related to
nutrient levels (vitamin C and vitamin A) in 21 CFR 101.9(g)(1)(ii) which states that for
the vitamins listed, the analysis level cannot be below 80% of the label value if it is a
natural food with no added nutrients or cannot be below 100% (21 CFR 101.9(g)(1)(i))
26
if the product has any added vitamin or nutrient whether or not it is the nutrient under
test. Thus one must base the label value on some predicted initial variability and
some predicted loss during distribution and storage. The FDA usually takes samples at
the supermarket level (where they can purchase them) for compliance testing, not from
the end of the process line so distribution losses must be factored in.
The end point of shelf life is thus dependent on your corporate objectives and
how much risk the company is willing to take with the brand. No shelf life test is
completed until a termination summary has been written. All termination summaries
should include the objective of the test, product descrīption, package descrīption,
conditions and length of storage, methods of evaluation, results (in the form of graphs,
shelf life plots and Q10 values) and conclusions. Termination summaries should
become a permanent record in the company library for future reference and preferably
indexed well on a computer data base for later retrieval when needed. The final shelf
life should also be set to give a clear margin of safety. In any case, the shelf life of a
new product, particularly of the high risk category, should be set based on data that
relate to the worst case manufacturing and storage scenario. The shelf life can then
be reviewed and if necessary re-set in the light of further experience in manufacturing
and control after the product has been launched.
Step 11: Implementation — One should get top management’s approval of the
test results so that they can be implemented. Management must believe and support
those test results. It is important for production, sales, distribution, purchasing and
quality control to work together to be sure that the production is properly handled from
the time of manufacture until this product is consumed.
19.4 Challenge study
19.4.1 Basis
Freezing reduces the microbial population of foods but considerable numbers usually
survive even prolonged frozen storage. A challenge study is often used in the
laboratory to study the factors and factor interactions as they affect the shelf life of the
product. Such simulated experiments enable the researcher to better control the study.
A challenge study is necessary for frozen foods for two reasons: (i) to predict microbial
growth and potential risk of the product upon temperature abuse in a distribution
chain; and (ii) to assess the relative stability and the relative risk of different formula,
different processes or different packaging materials, which is a must in new product
development. A challenge study may also be considered as a preliminary shelf life
determination in terms of microbiological safety. It is often used in the early stage of
27
development since if microbial safety is a concern at this stage, then reformulating can
be done quickly.
19.4.2 Microbial abuse procedures
Step 1: Identify barriers — A composition/ingredient analysis should be done to
identify any barrier(s) against spoilage microbes and pathogens in case of
temperature abuse.
Step 2: Choose types of organisms/strains and inoculation level — One
principle is to use an organism or a strain that has been isolated previously from the
product or similar foods which is responsible for spoilage or risk. The more isolates in
the study, the greater is the confidence in the accuracy of the shelf life assessment. An
inoculation level must also be determined, which is generally much higher than the
normal contamination level in a product. If the average contamination level for a
particular product is known, then the inoculation level should be as close to that level
as possible. Sometimes several inoculation levels are used.
Step 3: Determine temperature abuse conditions — After inoculation, products
should be packaged using the desired commercial packaging conditions, and
subjected to temperature abuse. Factorial design and response surface methodology
are often used in designing a challenge study. A typical temperature abuse condition
used by some food companies is provided in Table 19.5. It starts out with five sets of
test packages placed at -18 °C to begin the cycle. At the end of the first 24 hr, one set
of packages is removed and tested for microbiological indicators to establish a zerotime
level. All the other packages are kept at -18 °C for the next 20 hr, then removed
and abused by placing them at 38 °C for 4 hr. Another set of packages is then
removed for microbiological testing, and the cycle is repeated for the remaining
packages, i.e. they are all returned to -18 °C for at least 20 hr, then abused at 38 °C
for 4 hr. This procedure is repeated so that one set goes through at least four freezethaw
cycles. If there is no significant increase in spoilage organisms or pathogenic
organisms after the fourth cycle, the food is deemed safe microbiologically.
28
Table 19.5 A typical temperature abuse test sequence for microbial challenge
studies
Day Abuse temperature cycle Number of package sets
remaining
1 24 hr at -18 °C 5
2 20 hr at -18 °C
4 hr at 38 °C
4
3 20 hr at -18 °C
4 hr at 38 °C
3
4 20 hr at -18 °C
4 hr at 38 °C
2
5 20 hr at -18 °C
4 hr at 38 °C
1
Source: Labuza and Schmidl (1985)
Step 4: Do microbial survival analysis — This is to find out if there are any
microbial growth upon temperature abuse or if the inoculated microbes survived the
process. Appropriate detection and enumeration techniques should be used.
19.4.3 Applicability
The use of inoculated pack studies conducted by independent laboratories allows a
food processor to assess the relative risks that can occur under conditions of
temperature abuse of the food product in question. Taking frozen pizza as an
example, both the cheese and sausage, if naturally fermented, will have high total
counts of bacteria. Since the product is usually partially pre-baked and then frozen, the
numbers of vegetative microorganisms will decrease until thawing occurs.
Unfortunately, pathogens such as Staphylococcus aureus will not be totally
inactivated by these treatments. If the product is abused during distribution so
severally that the temperature near the surface reaches about 7 °C, pathogens may
grow. A challenge study with Staphylococcus aureus will verify the microbial safety
of the product.
It should be noted that inoculated pack studies with pathogens should not be
conducted in food industry laboratories that are located close to the food processing
facilities because of the possible transfer of pathogens to food products. No sensory
29
panel can be applied to evaluate the inoculated samples other than visual
observation.
19.5 Accelerated shelf life testing
19.5.1 Basis
During product development, preliminary shelf life knowledge is often needed in
addition to microbiological safety. Shelf life testing experiments at this stage are often
accelerated to evaluate the effects of various formulation and processing parameters
on shelf life stability of the product being developed periodically since one can not
afford the relatively long shelf life period for a frozen food stored under normal freezing
conditions. In addition, temperature fluctuations may occur in distribution and retail
holding for frozen storage. Thus kinetic studies at several temperatures within that
range are necessary to predict its shelf life. Accelerated shelf life testing conducted at
elevated isothermal temperatures and/or with freeze/thaw cycles for frozen products
have been used extensively for several decades by industry and government
agencies (Labuza and Schmidl, 1985). The Arrhenius relation and the Q10 approach
are used to extrapolate the results to the expected lower storage temperature.
Acceleration factors other than temperature have also been studied for some other
deterioration modes, such as moisture gain or loss and lipid oxidation (Labuza, 1984),
but rarely done for frozen foods.
19.5.2 Unique procedures
Step 1: Clarify test objectives — In general there are two occasions where
ASLT applies: i) estimate approximate shelf life quickly during development stage; ii)
collect kinetic parameters for actual shelf life prediction as in the marketplace, which is
conducted generally near the launch phase.
Step 2: Select accelerating temperature conditions — Suggested isothermal
accelerating conditions for frozen foods are -15, -10, and -5 °C with a control stored at
< -40 °C (Labuza and Schmidl, 1985). The inherent assumption is that the
deterioration mechanism is the same across the temperature range although as noted
earlier, there is concern about how close to freezing one can go.
Moisture migration from the food into the surrounding air with resulting
desiccation of the food and ice crystal formation in the package is a major mode of
deterioration of frozen foods under fluctuation temperature conditions. Cycling
temperature storage is used to test for this, i.e. from 0 °F or 10 °F up to 20 °F with one
day at each temperature and then repeated several times. A freeze-thaw cycling study
is also needed to determine its effect on sensory quality. Usually, the high temperature
30
can be much lower than that used in a microbial challenge study unless microbial
survival is still a concern. Typically, cycling temperature/time can be three to five 24
hour cycles between -18 °C and -7 °C, or between - 18 °C and 7 °C, depending on
the product.
Step 3: Estimate testing time and sampling frequency— Testing times are
dependent on a desired shelf life at target storage conditions. For example, given that
a shelf life of 12 months at -18 °C is desired, a shelf life plot can be constructed. Figure
19.11 indicates the test time at -4 °C that equates to 12 months at -18 °C for various
Q10 values. Sampling times at -4 °C should thus be 1 wk, 2 wk, 1 month, 3 months, and
4.5 -5 months. Most published results suggest that Q10 values for vitamin C loss and
quality loss in frozen vegetables range from 2 to 20 and that the shelf life of vegetables
is only 6-8 months at -18 °C (Labuza, 1982). Considering these Q10 values, a product
that does not retain good quality for 4.5 months at -4 °C may not retain good quality for
12 months at -18 °C. This also suggests the sampling frequency shown in Table 19.6.
All simple tests should be conducted at each sampling time, while sensory testing
should be concentrated mainly toward the end of the test sequence with a few near the
beginning.
0 5 10 15 20 25 30
.1
1
10
100
T (°F)
Shelf life 12 mo at 0
ASLT at 25 °F
4.5 mo
1.2 mo
14 days
6 days
Q10=2
Q10=5
Q10=10
Q10=20
Figure 19.11 Shelf life testing times at 25 °F equivalent to 12 mo at 0 °F
for various Q10 values.
Table 19.6 Sampling frequency for frozen pizza ASLT
31
Temperature (°C) Sampling times (wk)
- 4 1, 2*, 3, 4, 5, 8, 12, 14, 16*, 20*
- 7 2, 4*, 10, 15*, 20*
- 10 4*, 10, 15*, 20*
* Sensory test times Source: Labuza (1986)
Step 4: Determine end point — Figure 19.12 shows a comparison of times to
various levels for the loss of vitamin C in frozen spinach as a function of temperature
(Kramer, 1974). The dotted line represents the 80/80 rule, i.e., from a legal standpoint,
for natural products, 80% of the tested sample must have no more than a loss of 20%
(i.e. 80% of the label value). Consumer sensory testing will not always give such a
clear shelf-life result since different shelf life times can result using different quality
attributes. Often professional judgment has to be made to decide what factor to use as
the base for the end of shelf-life of the product. When shelf life is unacceptably short,
adjustments should be made to the food, its environment, packaging, process and
hygienic conditions, until a suitable extension of shelf life can be achieved. For some
products, the test results may demonstrate that the target shelf life is not attainable. At
this point, the question of whether to launch the new product with a shorter shelf life or
to abandon the entire project becomes a marketing decision.
-20 -10 0 1 0
1
10
100
Storage Temperature (°F)
Shelf life (mo)
Figure 19.12
Shelf life of frozen spinach as a function of vitamin loss level
50% loss
25% loss
10% loss
Quality (80/80 rule)
32
Step 5: Estimate kinetic parameters — From each test storage condition,
estimation of k or q is needed to make the appropriate shelf life plot. From this one can
then estimate the potential shelf life and confidence interval for the storage condition.
Then parameters for the Arrhenius relation and the shelf life plot are determined by
linear regression, which are used for shelf life prediction.
Step 6: Extrapolate to normal freezing storage condition — The most useful
shelf life information is obtained for product kept at its intended storage temperature,
which is about -18°C for retail frozen products and -23°C for distribution of frozen
foods. Figure 19.13 demonstrates how the shelf life plot is used for extrapolation. It is
always a good practice to compare a model's prediction against actual experimental
results because of the potential for errors from using the higher temperature data as
noted earlier besides the other errors suggested by Labuza and Riboh (1982). In
addition, the existence of a glass transition at a temperature between the test
temperature and the prediction temperature would lead to error as shown by Nelson
and Labuza (1994). In the case of frozen foods, most likely the error would be an
under prediction of the shelf life.
ln Q
T
T1
T2
T3
Ts (commercial storage temperature)
Figure 19.13 Extrapolation from ASLT
Step 7: Predict quality loss for a fluctuating time-temperature distribution — The
prediction is based on two assumptions: (1) that there is no history effect from the
time-temperature variation and (2) that the key deterioration mode does not change as
a function of temperature. The frozen spinach data shown in Figure 19.12 is used in
the following example in Table 19.7 for a time-temperature distribution. The line
33
equivalent to 20% loss is set as the end of shelf life limit i.e., if Ao = 36 mg/100 g then A
at the end of shelf life is 0.8 x 36 or 28.8 mg or 7.2 mg of vitamin C can be lost. For
each temperature of exposure, the time on the 80/80 line is the time for 20% loss, thus
at -10°F, the 20% loss (equivalent to 100% shelf life) time is 16.5 months. Thus for 6
months storage at -10°F, there is 6/16.5 or 36.3% of the shelf life lost and the amount
left is 36 - 6.36 x 7.2 = 33.4 mg.
Table 19.7 Estimation of quality remaining of frozen spinach after exposed to a
variable time-temperature history with Ao = 36 mg/100g spinach.
Temperature
(°F)
Time t
(months)
q shelf life
(months)
fcon
(t/q )
Sfcon Aremaining
(mg/100g)
-10 6 16.5 0.363 0.363 33.4
+3 1 4.5 0.256 0.619 31.5
+12 0.25 1.6 0.156 0.775 30.4
Since as noted 80% of Ao is equal to 28.8 mg/100g at end of shelf life, this product is
still acceptable at the end of the set of three different time/temperature exposures. In
fact, the shelf life left @ 5 °F = (1-0.775) x 3.3 = 0.74 months = 22 days.
19.5.3 Applicability
Because of relatively long shelf life for frozen foods and the unique feature of freezing,
the degree of temperature elevation is largely limited. Prediction of actual shelf life
from ASLT may be severely limited except in very simple food systems. Frozen foods
such as frozen pizzas, may present problems with moisture migration. The moisture
may diffuse from the pizza sauce which has a higher aw into the crust containing a
lower aw, creating a pizza crust that is limp and soggy. Product development scientists
should only use the results as a guideline and must use as many storage conditions
as possible to minimize prediction errors.
34
ASLT is just a quick method, which can not replace the normal storage tests
discussed next. Once it is verified that the extrapolation may be wrong, i.e., too large
an error, then a careful look should be taken at the deterioration mode, the experiment
design and procedure, the data collected and the model developed. If the
extrapolation under predicts the true shelf life, then it becomes an economic concern, it
is over predicted, then reformulating may be necessary. If the shelf life prediction
indicates that the product meets the stability expectation, then the product has a
chance of performing satisfactorily in the marketplace.
19.6 Confirmatory storage study
19.6.1 Basis
The difference in potential shelf life should be considered when scaling up from
experimental test batches to pilot plant and then to full scale production. Experience
has shown that results of small-scale experiments in the laboratory may not be of
much use for large-scale production (Graf and Saguy, 1991). Scale-up not only affects
the processability and quality of a food product, but it often alters its shelf life.
Depending on the mode of failure and the food scientist's approach to inhibiting
microbial growth and chemical reactions leading to deterioration, scale-up may

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