Thermal Processing of Foods, A Retrospective, Part I: Uncertainties In Thermal Processing and Statistical Analysis

Thermal Processing of Foods, A Retrospective, Part I: Uncertainties In Thermal Processing and Statistical Analysis M. N. RAMESH Food Engineering Department Central Food Technological Research Institute Mysore 570 013, India

S. G. PRAPULLA Fermentation Technology and Bioengineering Department Central Food Technological Research Institute Mysore 570 023, India

M. A. KUMAR Central Instruments Facility Central Food Technological Research Institute Mysore 570 013, India

M. MAHADEVAIAH Food Packaging Technology Department Central Food Technological Research Institute Mysore 570 023, India I. Introduction 11. Uncertainties In Thermal Processing A. Uncertainties During Thermal Death Time studies B. Uncertainties in Calculating the Value of D C. Uncertainties in Calculating the Value of z D. Uncertainties in Calculating the Value of F E. Uncertainties During Processing of Canned Products F. Inaccuracies in Evaluating Thermal Processing G. Uncertainties Due to pH H. Uncertainties Due to Temperature I. Uncertainties in the Safety Factor 111. Uncertainties in Aseptic Systems A. Uncertainties in the Aseptic Value of z B. Uncertainties in the Aseptic Value of F C. Uncertainties in Aseptic Processing IV. Statistical Analysis of Thermal Process Calculation V. Suggestions for Future Work VI. Conclusions References 287 ADVANCES IN APPLIED MICROBIOLOGY,VOLUME 44 Copyright 0 1997 by Academic Press, Inc. All rights of reproduction i n any form reserved. 0065-2164/97$25.00

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I. Introduction

The aim of thermal processing is to ensure adequate protection of a product by the destruction of potential spoilage organisms, based on reliable thermal death time (TDT) information and product heat-penetration data. Such various equipment as still retorts, rotary retorts, plate sterilizers, aseptic processing units, and ultrahigh-temperature (UHT) units have been developed for thermal processing. This was necessitated by the development of specialty and convenience foods, new container designs, new processing systems, quality considerations, energy savings, and processing regulations (Bee and Park, 1978). The basic procedure for calculating the time-temperature profile as the basis for ensuring commercial sterility as developed by Nicholas Appert has not changed much since the nineteenth century (Corcos, 1975). One of the major tools for calculating this time-temperature profile is Ball’s formula, introduced by Ball in 1923. Despite its limitations, the method is still widely used in the canning industry and stands as a classical tribute to the value of mathematics in food processing. However, improvements have been made in subsequent years (Merson eta]., 1978). A thermal process for foods should be very accurate, as it involves high temperatures and long durations, especially for low-acid foods. Underprocessing may result in spoilage of processed food products. Overprocessing can result in loss of valuable nutrients. The target in thermal processing is to ensure sterilization rather than nutrient retention, so as to avoid rejection and reprocessing of the processed foods. For this, a safety factor is needed, as the development of process schedules involves many uncertainties. Several publications have appeared on the ambiguity associated with parameters describing the temperature response of the inactivation of spores (Hicks, 1951,1961; Powers et al,, 1962; Herndon, 1971; Hurwicz and Tischer, 1956a;Perkins et al., 1975; Robertson and Miller, 1984; Bee and Park, 1978; Cleland and Robertson, 1985). Though a large number of publications have appeared on thermal process evaluation during the past 45 years, there has been little information on the magnitude of the uncertainty and error involved in thermal processing (Robertson and Miller, 1984). Thermal processing operations involve biological variability, which in turn affects commercial sterility. When some of the uncertainty related to the biological parameters cannot be avoided, the processors are forced to use safety factors. Instead of indiscriminately using the safety factors, several statistical techniques and computer methods that

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were developed for applying safety factors can be adopted. Though this will not overcome the uncertainty, it will ensure optimum sterilization. The objective of this review is to identify the uncertainties with regard to Clostridium botulinum as the target microorganism and to summarize the statistical techniques available for evolving safety factors for sterilization within containers and aseptic processing. II. Uncertainties In Thermal Processing A. UNCERTAINTIES DURINGTHERMAL DEATHTIMESTUDIES

Thermal resistance of bacterial spores is dependent on water activity, pH, and the level of fats, proteins, carbohydrates, and salts present in foods (Hansen et ~ l .1963). , The specific effect of pH on the value of F has been worked out by Ito and Chen (1978). Hence, considering the effect of these parameters, it is always necessary to conduct TDT studies for the particular food product under investigation. Though there are some reports on TDT studies in particular foods, classical values derived from neutral phosphate buffer are generally used (Saikia and Ranganna, 1992). In the case of TDT studies for determination of the values of D and z, small glass tubes called ampules of wide-core diameter (7-13 mm) are used. Since these ampules have low thermal conductivity, the heating-up and cooling-down times have been considerable with lag time varying from 20 sec to 5 min (Shannon et al., 1970). To reduce the estimation error, a correction factor for the construction of TDT curves is added. Hence, the TDT data themselves would lead to approximation of the time-temperature data evolved. Pouches meant for TDT studies have been developed to considerably reduce error (Erdtsleck and Becimer, 1976). These do not require any safety factor. However, TDT ampules are still being used to evolve TDT data in the food processing industry. B . UNCERTAINTIES IN CALCULATING THE VALUE OF D

The uncertainties in derivation of D values from TDT curves (as indicated above) are multiples of the F values, as is evident from the following equation: F = D (log No - log Nf).

The value of D for a mesophilic spoilage organism is about five times greater than that for C. botulinum. The processor must decide what the

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target organism is and the acceptable level of spoilage and then calculate the required process Fo value (Stumbo et d.,1983). In practice, a process goal that provides reasonable assurance against spoilage is more important than that for commercial safety. The extent of safety margin varies for Fo values of 2 to 3 min (Alstrand et d.,1952) over and above that derived from the scientific studies. Xezones and Hutchings (1965) have studied the thermal resistance of C. botulinum spores as affected by certain food constituents and indicated that the D values of C. botulinum increase as the pH value increases. These increases in D values are statistically significant, as they are reflected in the calculation of F values and hence also in the duration of process. It appears that there are no conclusive data to establish the number of adequate trial runs and the number of containers to be used for determining a safe thermal process schedule (Robertson and Miller, 1984). The thermal death of bacteria is not truly logarithmic (Saikia and Ranganna, 1992). Hence, the D values determined from survivor curves are of limited validity, so that extrapolation of D values is inaccurate. Inoculated pack studies are carried out to confirm whether the calculated thermal process is adequate. Hayakawa (1982) and Hayakawa et ~ l(1981) . have developed procedures for mathematical analysis of logarithmic and nonlogarithmic survivor curves. The idea that thermal death of bacteria is exponential has motivated bacteriologists to determine the basis of thermal process calculation used for food processing (Ball and Olson, 1955; Schmidt, 1957; Stumbo, 1973). Vas and Proszt (1957) have postulated that 1. Populations of single strains of bacteria are homogeneous with re-

gard to heat resistance.

2. Thermal death of bacteria is unimolecular, that is to say, that death

occurs from inactivation of a single molecule. However, experimental evidence indicates that assumption 1 is seldom true and that assumption 2 is incompatible with evidence of sublethal injury (Ball and Olson, 1955; Moats et d.,1971). In view of this, there can be no theoretical basis for the common practice of assuming that logarithmic survivor curves and any deviation from linearity will lead to experimental errors. Still, the logarithmic curve forms the basis for all calculations used in thermal process evaluation, with generous safety factors added onto them. The impact of this assumption is reflected on the D values for thermal death of bacteria being

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not truly logarithmic, so that, with these uncertainties, decimal reduction times ( D values) become meaningless (Hayakawa, 1982). One suggestion may be direct measurement of process times required for a given probability of kill at a given temperature. The direct method known as the spore count reduction technique involves inoculation of a representative number of cans with spores of a suitable test organism. In this operation, hundreds and sometimes thousands of cans packed with foods under study are inoculated with spores and subjected to different process types. The cans are then placed in incubation at a temperature near that for optimum growth of the test organism, and carefully observed for evidence of spoilage. The minimum required process corresponds to that giving the lowest processed lot showing no spoilage. However, this should not be confused with inoculation pack studies (Pilcher, 1949; Yawger, 1978; Berry and Bradshaw, 1986). This would give valid comparisons without introducing unwanted assumptions as to the logarithmicity of death rate (Moats et al., 1971). It has been demonstrated that, with this approach, F values are more reproducible and accurate than those derived from D values (Ott et d., 1961). Moats et al. (1971), from survivor curves carried through four or five log cycles, have also demonstrated that higher probabilities of kill could be seriously misleading. for C. botulinum, which is generally The suggested value of compatible with the 12D concept, is considerably lower than the value of suggested by Stumbo et d. (1983). If the initial spore numbers in the product are lo3 per unit and the probability of a final spore number is the spore logarithmic reduction will be iO-9/iO-3, that is, a 12D process would be required (Pflug and Odlaug, 1978). As suggested by Stumbo et al. (1975), a 13 to 15D process depending on the initial C. botulinum spore load is more compatible. An ideal thermal resistance determination method should be able to take into account all influencing factors. Many methods such as direct methods, indirect methods, particle methods, and mixing methods have been reported for thermal death determinations. Although most of these have individual advantages over previous methods, they are still rather laborious and leave important problems unsolved. These methods are discussed in detail by Brown and Ayres (1982). Being complex, direct methods do not allow working at low temperatures and using homogenized foods (Burton et d.,1977; Daudin and Cerf, 1977). In some methods, pH cannot be measured during heat treatment. The particle methods used for thermal death determination, aside from not allowing monitoring of pH and temperature, do not

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permit determinations in pure or homogenized foods (Brown et d., 1981).

The main advantage of the mixing method is elimination of lag phases and direct measurement of temperature in media. The literature provides procedures for determining heat resistance using survival curves without using food as the medium (Cerny, 1980; Brown et al., 1981). Determination of D values at ultrahigh temperatures (UHTs) with continuous pH measurement is not possible using the mixing method. Hence, most heat-resistant determinations are carried out using capillary tubes or employing some instrumental methods, like thermoresistometers (Cerny, 1980; Reichart, 1983; Mikolajacik and Rajkowsky, 1990; Brown et a]., 1981). A major drawback of all indirect methods is the inherent heating and cooling lag. In addition, although foods can be used as the medium, determinations at temperatures in the UHT range cannot be performed with high precision. The use of a capillary has drastically reduced lag time, but some still question whether the contents ever attain batch temperature (Brown and Ayres, 1982). C. UNCERTAINTIES IN CALCULATING THE VALUE OF Z The death rate kinetic factor z is assumed to be constant irrespective of a change in temperature. Actually, it follows a Arrhenius relationship and leads to the variable z. Gillepsey (1951) and Jonsson et d. (1977) suggested that straight calculations should be based on experimental results obtained at the actual temperature to be used during thermal processing and not by extrapolating data fiom other temperatures. D . UNCERTAINTIES IN CALCULATING THE VALUE OF F

In some cases, the 12D process adopted is inadequate when the initial spore count is high, as this prescribes an Fo value of 3 min assuming a defined initial and final number of spore counts. A better approach is to define an acceptable Nf value with a final spore reduction of and calculate the minimum safe process from the following equation (Cleland and Robertson, 1985): F = D[hg ( N f I N J ] .

Improperly cooled cans and faulty accounting of the cooling phase lead to erroneous Fo calculation. The steam can be shut off before total target F is reached, expecting that cooling phase lethality will increase the required overall Fo value. However, once the steam is off and cooling

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commences, the lethal effect of the process is virtually fixed, and the contribution of the cooling phase is not properly evaluated and accounted for in terms of overall lethality. Hence, the standard practice is to attain the total a targeted F value in the heating phase and treat the cooling phase as a safety factor (Board et al., 1960). The weaknesses of this approach are that the process is longer, making it more expensive, and that destruction of heat-sensitive nutrients can be substantially greater than would occur if the minimum safe process were to be used (Cleland and Gesterkamp, 1983). F values determined in a small pilot plant retort are not directly applicable for the same process parameters in a commercial plant. Containers of different size processed for a particular F value do not have the same degree of commercial sterility. This is true because sterility is defined as the probability of survival of a single spore in the entire container at the cold point. Obviously, as the size and hence the volume increase, the containers should have a higher Fvalue to achieve the required final spore count (Cleland and Robertson, 1985). The F values are deduced by assuming pure conduction and pure convection heat transfer models. In practice, combined models usually exist. Particularly, the cooling phase time-temperature profile is highly irregular due to boiling, condensation, and agitation within the container (Cleland and Gesterkamp, 1983; Board et al., 1960). Therefore, there is inherent danger in expecting F to be identical for all cans unless the headspace within the container, the retort pressure during cooling, and the product temperature at the end of heating are closely monitored. Because of this uncertainty and complexity, the required lethality will be accomplished during the heating cycle, and that accomplished during cooling cycle will be a safety factor (Board et al., 1960). This may be safe, but will lead to overprocessing. Because of variations between individual containers, the rates of heat penetration may vary appreciably for the same product. Nevertheless, the same F, value is used though there are formulas to evolve the altered process schedules from one container size to another. Only rules of thumb are applied to get a revised process schedule, or the containers are simply processed for another 4-5 min. Therefore, it is always good practice to take at least three replicate containers for the experiment and perform sufficient experiments at each stage of product development to be sure that adequate processing is achieved under the most adverse conditions likely to be encountered (Shapton and Shapton, 1993). Food and Drug Administration (FDA) regulations demand that heat penetration data be taken from the regular production run. Despite this,

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a procedure commonly used in the canning industry to determine F values relies on data from a small number of cans processed in a pilot plant-scale retort. The implicit assumption is that the F value calculated for a few cans processed in a small retort will be the same as for production-run cans in a fully loaded commercial-scale retort (Robertson and Miller, 1984). The number of surviving spores is not easily determined because different parts of the container receive different thermal treatment, especially in the case of particulate foods. Thus, the assumption that the initial and final spore concentrations across a container are uniform will not be true. On the contrary, they will be position-dependent. With this uncertainty, calculation of the value of F using F = D (log No - log Nf) is also uncertain (Cleland and Robertson, 1985). This problem is mini-

mized in pure convection because of mixing due to convection. This uncertainty can be considerably reduced by adopting numerical techniques with smaller increments. From time to time it is necessary to reestablish a process because changes in recipe, manufacturing method, filling method, or location can adversely alter the effectiveness of the sterilization process. Under these circumstances, heat penetration of other tests must be made to reestablish a satisfactory sterilization process. Process establishment using outside data is usually employed. When a new product is proposed, existing data are most often used in order to reduce the experimental work. In these circumstances, it is extremely important to ensure that the new product is essentially the same as the one for which the process was originally developed. Even a simple product, like a vegetable in brine, will have different process requirements depending on the size, shape, and amount of vegetable in the container (Shapton and Shapton, 1993). E. UNCERTAINTIES DURINGPROCESSING OF CANNED PRODUCTS

The most common cause of C. botulinum outbreak is the failure of the operator to follow the specified process schedule (Pflug and Odlaug, 1978),which arises due to incorrect process calculation. All approaches applied for evaluating a thermal process schedule and for developing process lethality require the use of data that can only be determined experimentally. The usefulness of any calculated result is further limited by data uncertainties (Cleland and Robertson, 1985).

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The National Canners’ Association (1968)has opined that one test run of 6 to 8 cans will provide sufficient data for homogeneous products. However, composition varies according to the processor used, the season, the preparation method, and many other factors. Hence, it is always safe to have data from several runs and then conclude that sufficient parameters, after ensuring the condition of slower heating, have been included in the study. With all of this, no reference has been made to the level of uncertainty associated with the calculated F value. According to Cleland and Robertson (1985), the reasons for underprocessing are: 1. Calculation of process parameters may not be sufficiently accurate, since the assumptions made to derive these parameters used are not valid for the circumstances under which they were derived. 2. The data used in calculations may not be precise. These data might be kinetic data for spore inactivation ( D and z values) or data used to determine temperature-time profiles. 3. The safety margin allowed is inadequate. Such process variations as initial product temperature, differences in steam supply to different parts of the retort, differences in retort venting and come-up time, and changes in cooling water temperature result in some of cans being processed improperly, thus yielding a lower F value. The scheduled thermal process has large and ill-defined unscientific safety margins (Board, 1977). In carrying out heat penetration studies, the process schedules have been standardized to decide on the number of test cans to be used for each run and the number of test runs to be adopted. The results reveal that the safety factor is quite considerable (Robertson and Miller, 1984). Though this important safety factor ensures a safe commercial process, it can lead to considerable nutritional loss. Product heating is dependent on both intrinsic factors (e.g.,the nature of product, filling weight, headspace, can size) and extrinsic factors (e.g., equipment, method of heating media, ambient conditions). Therefore, both of these factors must be taken into consideration when establishing a process because any factor that alters process lethality is a “critical factor” that must be specified in the schedule process. Extrinsic factors are related to the performance of filling and sterilization equipment. Filling is a factor critical not only for obtaining the correct solid-to-liquid ratio, but also in increasing the rate of heat transfer depending on headspace. An intrinsic factor like the cube size of diced vegetables can alter the rate of heat penetration and also the

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density of packing within the container, thereby altering the product heating data (Shapton and Shapton, 1993). Sterilizers of many different types (e.g., still retorts, agitating retorts, continuous retorts, plate sterilizers, aseptic units, and UHT units) are used in thermal processing of foods and use various heating media. The types and sizes of sterilization equipment have their own particular characteristics that must be known for both product heating rates and accuracy of sterilizing times and temperature. The standard practice in the canning industry is to follow the following assumptions, given by Stumbo et al. (1975): 1. Maximum 121.1”Cresistance of C. botulinum spores would be based on D = 0.20 min. 2. Prior to heat processing, all low-acid foods should be assumed to have a final population of the most resistant C. botulinum spores of one spore per gram (1cm3) of product. 3. The volume of any given container will be computed using the inside dimensions, with no correction for headspace. 4. The value of z, when characterizing the relative resistance of the most heat-resistant C. botulinum spores, should be taken as 14°C. 5. The probability of a C. botulinum spore surviving in any one heatprocessed container of low-acid food should be greater than With adoption of all of these assumptions, uncertainties in evaluating thermal process schedules still exist. F. INACCURACIES IN EVALUATING THERMAL PROCESSING

The formula method was introduced when many workers in the food industry were unable to appreciate its mathematical basis. Consequently, a “cookbook” approach to its use was developed. This approach persists, even when most food scientists have a better background in mathematics and physics. Ball’s formula method would be useful for food technologists and would lead to more intelligent use of process calculations. However, the formula method has its deficiencies, especially since it tends to yield inaccurate results. This may be due to: I. Calculations involving the value of fh and g found in published tables and graphs for these functions. fh is usually based on the eye-fit line or by linear regression.

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2. Deviation from the assumed conditions in the original derivative

with respect to initial product temperature and retort temperature. In addition, g is calculated assuming uniform heating and cooling rates. 3. The difficulty in obtaining accurate values of required experimental parameters (e.g., f h , z). Despite these lacunae, Ball’s formula method has served the canning industry well since the 1930s. However, the method has undergone careful scrutiny, and a few errors have been found and possible solutions proposed. As more accurate computer methods based on numerical analysis become available for routine work, it is likely that the formula method will lose its prominence. Nonetheless, the development and application of this method will continue to stand as a milestone in food technology history (Merson et al., 1978). G. UNCERTAINTIES DUE TO pH

It is well known that the heat resistance of microorganisms is mostly determined by heat treatment parameters (time and temperature). However, many other microenvironmental factors, such as pH, water activity (aw),and medium composition, also exert influence. As the heat resistance of a microorganisms changes logarithmically with temperature, it is very important that the exact treatment temperature (with no undue fluctuations) be known. The effect of media pH on the heat resistance of microorganisms is very important. Some foods ( e g , macaroni, spaghetti, Spanish rice) have been reported to undergo changes in pH during heating (Brown and Thorpe, 1979; Reed et al., 1951, Xezones and Hutchings, 1965, Cerny, 1980; Montville and Sapers, 1981). Thus, ignorance of medium pH or of a change in pH during heat treatment can lead to misinterpretation of results (Brown and Thorpe, 1979; Reed et al., 1951). The composition of media used for TDT studies also exerts a significant effect on heat resistance (Pflug and Odlaug, 1978).Therefore, heat resistance should be determined in the foods themselves whenever possible (Stumbo, 1973; Brown and Ayres, 1982). The data in the literature on microbial resistance vary greatly. In addition to the natural variability in resistance of different spore crops of the same organism and in the age of spore suspensions, factors that contribute to data variability include the diversity of methods employed for TDT studies and differences in actual medium conditions that are

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not monitored during treatment (Schmidt, 1957; Brown and Ayres, 1982). Moreover, continuous monitoring of actual pH and temperature during heat treatment is not possible in TDT studies, and spores cannot be heated in highly viscous homogenized foods. Reichart (1983) has indicated that D values under 0.06 min for high-acid foods and at high temperatures cannot be determined by this method. Nevertheless, the capillary method has been used as the reference method for such applications (Davis, 1975; Burton et al., 1977; Mikolajacik and Rajkowsky, 1980).

It is a general assumption that the cutoff pH for low-acid foods is 4.6. However, the ability of pH 4.6 to inhibit growth of C. botulinum might be in doubt (Townsend et al., 1954; Thompson and Tanner, 1925; Baird-Parker and Freame, 1967; Odlaug and Pflug, 1979; Seeger, 1983). Rowley and Feeherry (1970) found that germination occurred at pH 6.5-7.5 and that no germination occurred below pH 4.8. Huhtanen et al. (1976) found that the minimum pH for outgrowth of spores with only a C. botulinum inoculum was pH 5.24. However, upon inoculation with mold and C. botulinum spores in tomato juice with an initial pH as low as 4.2, the medium became toxic. Upon taking the pH of the inoculated medium, they found a pH gradient ranging from the starting pH of 3.5 to as high as 8.2. This indicates that competitive microorganisms not normally present in properly prepared acid products could initially grow and change the environment to enhance conditions that support the growth of C. botulinum spores. Thus, growth in these acid foods is not due to the ability of C. botulinum to grow under the acidic conditions of the food (pH 4.6 or less), but rather to the pH rising above the inhibition level as a result of, for example, the action of other microorganisms. The above theory thus indicates that, although a pH of 4.6 will inhibit the growth of C. botulinum, the actual minimum pH at which growth is inhibited in a given food will be specific for that food and may be higher than pH 4.6 (Ito and Chen, 1978). However, no investigator appears to have critically controlled and quantitated anaerobiosis. Uncompromising exclusion of oxygen is known to encourage C. botulinum spore outgrowth under otherwise suboptimal conditions (Smith, 1975; Lund and Wyatt, 1984). C. botulinum is inhibited by oxygen because of its evident inability to synthesize catalase or superoxide dismutase. In the absence of these enzymes, which confer aerotolerance by dissimilating toxic oxides, oxygen and its products drain the anaerobe of cell reducing power. The essential reductants, for example, NAD(P) and NAD(H), are thus un-

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available for yielding energy for cell metabolism. The ability of a microorganism to survive in an acidic medium may be related to the rate of proton migration into the cell relative to the proton-ejecting capacity of the cell (Corlett and Brown, 1980). In an oxygen-free substrate, cell energy that would otherwise be expended in scavenging oxygen and lowering the redox potential of the immediate environment, would be used to motivate expulsion of lethal hydrogen ions from the organism’s interior. Enhanced acid tolerance by C. botulinum in a relatively reduced system was first reported by Raatjees and Smelt (1979) and Smelt et al. (1982). C. botulinum was found to germinate, grow, and produce toxin in sterilized suspensions of soya protein (5.5%) acidified to pH 4 . 3 with hydrochloric acid and incubated in anaerobic jars. However, the results were not always reproducible, possibly because of varying oxygen tensions. The media were not prepared anaerobically, and the concentration of atmospheric oxygen in the jars was not measured. Nevertheless, these results suggest that a potential health hazard may exist in certain commercially produced foods that rely on acid to prevent C. botulinum growth. C. botulin um outgrowth and toxin formation in high-acid protein-rich environments is incompletely understood. The degree of anaerobiosis appears to be important, as does the extent of buffering. The protein may play several roles, such as a reducing agent, allowing the cell to overcome a limiting redox potential, a source of essential metabolites for growth and toxin production, and a buffer retarding acidification of the cell interior. Young-Perkins and Merson (1987) in their research have studied the effect of soya protein concentration on C. botulinurn spore germination, outgrowth, and toxin production in the presence of varying levels of an inorganic (hydrochloric) or organic (citric) acid under strict anaerobic conditions. They also investigated the interactions between pH, total acidity, and buffering capacity during the transition of C. botulinum from spore to active vegetative state at pH values less than 4.6; unequivocal evidence of C. botulinum spore germination, proliferation, mortality, and elaboration of neurotoxin under strict anaerobic conditions was firmly established. Although the pH of the medium and exposure temperature are known to influence the inactivation rates of bacteria, incorporation of these important environmental factors is not being followed in practical situations. Davey (1993) developed an extension of a generalized sterilization chart that combines temperature and pH. The procedure for its application is illustrated using inactivation of C. botulinum in a range of foods. This practical method should provide a convenient tool for

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assessment of the necessary sterilization designs. It could be readily extended to a range of other foods and bacterial contaminants. Considering the various factors that affect thermal resistance, inoculum size, the number of strains, the medium, the incubation temperature, and the number of replicate samples used are very critical in determining the effect of pH of media during TDT studies (It0 and Chen, 1978). To optimize the probability of determining the minimum pH at which C. botulinum spores will grow in food, the following protocol may be considered (It0 and Chen, 1978): 1. The inoculum size that should present a natural contamination level of about lo4 spores per container would appear appropriate. 2. A reasonable number of types and strains should be utilized. Because of strain sensitivity to pH, different strains of each type should be utilized. 3. The product utilized should be the food to be tested rather than a bacteriological medium, or it should be food product supplemented with a bacteriological medium. 4. The product should be placed in 5-10 replicate tubes or other appropriate containers at a given pH level. 5 . The product should be incubated anaerobically at 30°C. If thioglycolate is utilized, care should be taken, as it may delay or prevent growth at the minimum pH level if used at too high a concentration 1966). (Segner et d., 6. The presence of toxin should be determined at the minimum pH level at which growth is observed. In addition, at least the next lower pH level should be examined for growth and presence of toxin. Raatjees and Smelt (1979) and Smelt et (11.(1982) have shown that the C. botulinum spore can grow at low pH values of 4.2. However, under specific conditions, the classified pH value for low-acid foods where C. botulinum is targeted is 4.6. Still, no attempt is made to redefine the low-acid food to include, foods with pH below 4.6. This leads to lower heat processing, as they are classified as high-acid foods. In spite of these reports, the knowledge available on the effects of reduced pH on bacterial spores is limited (Blocher and Busta, 1983). The most investigated species has been C. botulinum, and the evidence has thrown light on arriving at a pH range for this organism. However, there is a need for more data on the fundamental mechanisms of acid inhibition and studies on the practical implications of this information related to the safety of processed foods.

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H. UNCERTAINTIES DUETO TEMPERATURE The cold point is defined as that point in a container that receives the least heat. This is assumed to be the slowest heating point in the container. Flambert and Deltour (1972) have shown that this point varies in the container over the whole heating and cooling cycle. They concluded that the location of the least-processed point lies on the vertical axis through the container for small Hlr, values and in a ringshaped region for large Hlr, values, where H is the height of the container and r, is radius of the container. Thermal processes are normally designed using time-temperature data measured in the slowest heating zone (the cold point). But for natural convection heating products, this cold point location varies during heating and cooling phases (Zechman and Pflug, 1989). The location of the cold point during natural convection heat transfer is determined by the fluid flow patterns of the product within the container. Zechman and Pflug (1989)concluded that the slowest heating zone along the vertical axis of vertically positioned metal containers with natural convection-heated liquids may be located near the bottom of the container, but usually not farther than 25% of the container height from its base. The size of the slowest heating zone is smallest, and therefore the exact location is more critical in the design of processes, for liquids of high initial viscosity in small containers. Hence, the use of one-point location for a specific container size for all natural convection-heated products is not appropriate. Since the cold point moves during various stages of heating, the location of the slowest heating zone may be different for the same product and container size when the process time or temperature is significantly changed. In conduction heating of products at the steam-off point, large temperature gradients can exist from the outside surface to the cold point. This leads to a kind of temperature abuse called overshooting during the initial stages of cooling. The F value of this period is appreciable and is on the order of 13% of the Fo value (Robertson and Miller, 1984). Robertson and Miller (1984) worked on some of the uncertainties between can-to-can variations and run variations and deduced that the variations may be due to the position of the thermocouple point, variations in retort operation, heat conduction along the thermocouple wire affecting temperature readings, differences in headspace, and the accuracy of the thermocouples. According to Navankasattusas and Lund (1978), a thermocouple with an accuracy range of fO.l-l°C will lead to a corresponding error in accomplishing lethality of 2.3

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to 26%. Cowell et al. (1959) reported that the variations in Fo values are largest during the early stages of heating and cooling. The FDA specifies that at least one mercury glass thermometer with divisions that are easily readable to 1°F be used for heat penetration studies. However, Pflug and Odlaug (1978) calculated that, if the accuracy of can center temperature measurement is +1"C, then the potential error in the value of Fo due to this temperature variation is about +12%. Hence, they have suggested that, for low-acid foods, Fvalues of 2-6 may be listed in steps of 0.5 min, 6-10 by 1min, 10-20 by 2 min, and greater than 20 by 5 min [Merson et a]., 1978). The method first introduced by Ball (1923) of considering 42% of the come-up time to be lethal (with the retort at the processing temperature) and the remaining 58% to be nonlethal is still widely used. Research to verify this procedure is necessary. The basic heat penetration data required for evolving time-temperature profiles are collected by measuring the temperature at regular intervals of time at the cold point of containers. Usually, T-type plug-in thermocouples are used for this purpose. However, Bee and Park (1978) listed 25 cases of unreliable data used in conducting heat penetration studies. Hence, care should be taken to avoid all these problems when collecting heat penetration data. Processors commonly conduct heat penetration tests in the laboratory and apply the data in establishing thermal processing requirements to other conditions. This is particularly true for the conditions of retort temperature (RT) and the initial temperature (IT) of the product when processing times are predicted by the Ball formula method (Stumbo, 1973).

The FDA reviews processes filed for low-acid canned foods for adequacy in protecting public health (Mulvaney eta].,1978). These reviews have revealed that the temperature difference at which the heat penetration data were taken and for which thermal processes were filed can range from a few degrees to as much as 50°C. Using products that exhibit broken-heating curves, Berry and Bush (1987) showed that the procedure of extrapolating heating data to other RTs and ITS can have a significant influence on predicting required processing times. Extrapolation of broken-heating heat penetration data to a higher value is a conservative practice. Predicted processing time determined from data taken at a lower RT will be greater than the processing time actually required at a higher RT. However, the converse is not true. Berry and Bush (1987) demonstrated that, by taking heat penetration data at higher RTs, higher than those intended for estab-

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lishment of the thermal process, resulted in a calculated processing time as much as 10 min shorter than that demonstrated to be necessary. The effect of IT was even greater than that of RT for products exhibiting broken-heating curves. Their conclusion was that heat penetration data should be taken at the highest IT intended to make the process more safe. Extrapolation of heat penetration data taken at other RTs and ITS for product heating with straight-line heating curves as a result of significant induced convection can have an influence on calculated processing times. In general, this influence is less than that of product heating with broken-heating curves in a still retort. Taking heat penetration data at one RT and calculating processes at different RTs will have no effect on processing time for either rapid-heating or conduction-heating products. However, the effect will be significant for intermediate-viscosity products or large can size. The influence of extrapolation to different ITS is more significant with regard to determining required processing time than extrapolating data taken at different RTs, but the results will not be as consistent as those obtained from extrapolation of RT. The effect of product IT on heating depends on the product, and no compromise can be made as to the conservative direction of extrapolating data taken at different ITS, as it is product-dependent. Thus, extrapolating heating data to other conditions (RT or IT) can affect the establishment of thermal processes. The significance of this effect depends on the product, and serious underprocessing can occur with certain products and/or processing conditions. The conservative nature of the Ball formula method should not be expected to overcome this phenomenon, particularly for broken-heating products, where calculated processing times taken at other ITS will be deficient by as much as 50% (Berry and Bush, 1987; 1989). I. UNCERTAINTIES IN THE SAFETY FACTOR

It is felt that it is not feasible to apply valid statistical techniques because uncertainties in the data used to derive the processing parameters cannot be adequately assessed (Hicks, 1961). It is also argued that the safety factor is required for its own sake because the quantity or process being calculated must conform to the FDA safety standard, which cannot be specified very precisely in terms of the quantities calculated (Hicks, 1961). Hence, in the food processing industry it is probably necessary to think in terms of safety factors rather than precise confidence limits.

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Safety factors in process calculations are not always explicitly recognized. They are often introduced implicitly based on calculations of process schedules assuming initial spore population with specific properties (like D and z values). This hypothetical initial population is probably much harder to destroy than that likely to occur in commercial processed foods (Hicks, 1961). There is no specific point in the process calculation procedure at which to introduce a safety factor. That being the case, where and how does one compensate for the unknown? The canning industry must deal with a biological product that contains microorganisms. Product and processing conditions vary widely. Allowances should be made for unknown conditions, and there must be a way to compensate for realistic extremes. In this context, one should recognize the availability of scientific computing methods for determining the safety factors discussed in a later section. In the determination of process schedules, safety factors can be introduced in the response parameter, fh, and/or the lag factor, j . In earlier practices, a safety factor has been incorporated in the value of Fo itself. When carrying out heat penetration tests, the sterilization process engineer may introduce safety factors by using the slowest heating can in the design and overfill or make other manipulations to compensate for the unknown conditions. It has been stated as a rule of thumb that the Fo value for a continuous processed product should be double the Fo value used in the design of a still cook (Perkins, 1969). Perkins (1969) incorporated a similar safety factor in recommendations for the sterilization of hospital supplies based on his wide experience with sterilizers and sterilization conditions in hospitals. Lenz and Lund (1977a) and Lund (1978) have suggested that safety factors for canned foods may be calculated using statistical methods. At the same time, they have pointed out that there are not enough available background data to pursue these methods. Cues for Safety Factor Selection. Since the public health sterilization value requirement must include adequate safety, not only for microbiological variation, but also for process delivery variation, these factors should be considered in selecting the values of both F and z. Process delivery variation and the potential accompanying hazards are functions of the product, the container, and the processing system. In general, a greater safety factor is required as we go from metal to glass and other exotic packaging systems. The overall safety factor must be increased as we go from a conduction-heating product or a pure convection-heating product to products heated by both conduction and convection that may exhibit broken-heating curves. The safety factor must

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increase as we go hom long-time processes at low temperatures to short-time processes at higher temperatures and as we go from still cooks to agitating cooks (Pflug and Odlaug, 1978). Ill. Uncertainties In Aseptic Systems

A. UNCERTAINTIES IN THE ASEPTIC VALUE OF z

The process assumes a constant z value irrespective of the applied temperature, as in TDT studies on a laboratory scale. Product sterilizers have confirmed that at temperatures above 120°C the value of z continuously increases, making extrapolation of TDT curves a suspect practice for high temperatures (Kaplan, 1984; Bockelmann, 1985).

B. UNCERTAINTIES IN THE ASEPTIC VALUE OF F Recommended Fo values have often been larger than necessary to destroy a given population of microorganism (Dignan et a]., 1989). This is to ensure that a variation in processing conditions will not impede achieving commercial sterility. It is a general practice to consider the lethality achieved during cooling as the safety factor. c.

UNCERTAINTIES IN

ASEPTIC PROCESSING

Having made a decision as to the amount of lethal heat required, it is not certain how the high temperatures and short times can be measured (Shapton and Shapton, 1993). Accuracy of measurement is of prime importance because of the way in which small changes in these values have a large numerical effect on evaluation of the process. Static temperatures are used to measure the dynamic nature of the aseptically processed food product. Even for a simple liquid-phase product, process evaluation is not straightforward. As prescribed by the FDA, only exposure in the holding section is being used. The contributions of the heating and cooling sections are not taken into account. Such a process schedule would result in significantly greater nutrient destruction than may be acceptable to consumers (Chandrana and Gavin, 1989). Hence, the very objective of aseptic processing to retain nutrients by high-temperature shorttime (HTST) processing is defeated. The type of flow in the heating and holding sections is a critical factor, since the type of flow determines the relationship of the minimum

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residence time to the average or mean residence time. There is much to be learned about the dynamics of a food particle carried by a viscous fluid through an aseptic processing system. Each system is unique, as is each section within each system. The establishment of a thermal process must be specific for a food product and processing system (Dignan et al., 1989). In modern HTST sterilization, application of temperature ranges from 130 to 150°C for a few seconds results in retention of valuable vitamins. From the experiments of Srimani et al. (1980),it is evident that it is not possible to destroy all microorganisms when they are heated to a high temperature and cooled down immediately within an average retention time of 60 sec. Thus, heat-resistant spores have to be destroyed by chemical or other means to ensure absolute sterilization in the HTST process. Process schedule evaluation for particulates is even more complex since they are usually carried or suspended in a liquid phase. The size, composition, and integrity of particles are critically important, together with such factors as the solid-to-liquid ratio. However, none of the above methods are considered in practice, and only rules of thumb for extrapolation of the F values of still processing are followed. There do exist several mathematical approaches to define F values, but they remain theories that have not been transferred to processing industries. Burton et al. (1977) found considerable discrepancies between data obtained for B. stearothermophilus in milk from capillary tubes and UHT sterilizers, and they urged caution in process evaluation. Srimani et al. (1980) found that the D value for B. stearothermophilus (without heat activation) and B. subtilis remained constant above 135 and 12O"C, respectively. Some researchers, including Manji and Van de Voort (1985), favored the Arrhenius model over the thermal death time (TDT) model for the temperature dependence of bacterial spore death, although recognizing that the differences may not be practically significant. Rodriguez et al. (1987) have developed a sophisticated model for spore death kinetics based on considerations of population dynamics and systems analysis. These and other studies suggest that the process designer must use caution in extrapolation of TDT data to aseptic temperatures, and that a suitable margin of safety should be employed in each situation. Process evaluation for large food particle suspensions is considerably more difficult than for liquids. The principal difficulty is in measurement of cold zone temperatures of individual food particles during continuous flow. When a suspension is heated within heat exchangers, the liquid heats up relatively rapidly, but particle thermal response may

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be considerably slower. Further complications are introduced by the existence of residence time distributions in the process equipment (both heat exchangers and holding tubes) and an unknown value of the fluid-to-particle convective heat transfer coefficient. Consequently, process evaluation depends on development of accurate mathematical models for prediction of particle cold spot temperatures and reliable techniques for microbiological validation. Because of these limitations in aseptic processing, much effort is needed to establish the sterility of a newly installed plant. Examination after incubation of large numbers of samples taken from the filling line is necessary to ascertain whether or not the plant is operating in accordance with FDA standards (Hersom, 1985). A particular system used for filling one product is unique to that product, as the flow rate is dependent upon the viscosity and residence time of that particular food product. If any change in the product is envisaged, this calls for a major change in the equipment or the operating parameters. This is not economical for some food products. Different kinds of aseptic processing of such food forms as pulp, liquid, and particulate foods are in many ways distinctive. The sterilization methods used vary enormously depending on adaptation of direct, indirect, or electrical methods (Hersom, 1985). This indicates that it requires significantly more complex techniques than does the manufacture of canned foods in conventional retorts (Ahvenainen, 1988). Thermal death in the aseptic processing line is not the same as that with such small-scale laboratory methods as thermal shock, and the thermal death curve of a targeted microorganism cannot be efficiently simulated (Swartzel, 1985). This indicates that the best final result is obtained by testing sterilization effectiveness in the aseptic processing line itself, as there are no on-line methods for determining the sterility of a product. Only one final product evaluation for sterility (incubated packs) does not provide the necessary microbial safety. Other quality control measures (e.g., pH, pack integrity, seal efficiency) have to be used as complements (Brown and Ayres, 1982). Since aseptic processing is carried out at a high temperature of 12O-13O0C, even 1 min of overprocessing will lead to loss of product nutrients. Hence, the process schedule should be derived more accurately compared to canning (Lopez, 1987). Selection of the sterilization system that is most advantageous for a specific food product depends on many factors. The details of the system must be determined so that the process as a whole produces high-quality commercially sterile products. This particular setting is unique for that product. Since the product is first

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sterilized and then packed, utmost care should be taken to maintain system sterility. IV. Statistical Analysis of Thermal Process Calculations

When the uncertainties of thermal processing involving heat penetration studies and determination of thermal resistance data cannot be addressed, there are methods that can be used to evolve the safety factor so as to ensure the commercial sterility of a product with optimum sterilization. This also reduces losses of vitamins and other essential nutrients. Hicks (1961) was the first to provide data on statistical analysis of thermal process calculations. He studied the uncertainties due to physical measurement of temperature and those due to biological uncertainties in spore count. Different statistical distributions (e.g., normal, Poisson) have been applied to study the variation of thermal processing data. Massaguer et al. (1983) used a Poisson distribution to describe the error population of thermal process data, while Lund (1978) has used a normal distribution. As indicated by Hicks (1961), a sensible approach would be to define process schedules in terms of confidence levels or the coefficient of variations rather than adding safety factors implicitly. The coefficient of variation (CV) has been applied to define the probability of thermal process calculations in achieving commercial sterility. A CV can be defined for individual uncertain process parameters like the theoretical initial temperature (IT*), and g, D, f, and z values (Esselen et al., 1951; Tung and Britt, 1995). Herndon (1971) reported a CV of 25% for theoretical values in convection-heating foods. Hicks (1961) used a mean CV of 7%, a value obtained by considering the data of Hurwicz and Tischer (1956b) and Jackson and Olson (1940). Other statistical methods have been applied for evaluation of the D value of microorganisms. Lewis (1956) has extensively reviewed the application of Spellman-Karber and log-log methods for estimating the process schedule parameter and its standard deviations from experimental data. From his methods, the CV of the D value was approximately 10%. Stumbo et al. (1950) reported a CV in the z value of about 2%; whereas Kaplan et al. (1954) obtained a CV of 9% for PA3679, with a mean z value of 18.7"F. Hicks (1961) estimated that the effect of a CV of 2% for the z value would result in a CV of 5% in the calculated process time value; whereas a CV of 9% for the z value would result in

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a CV of 23% in the calculated process time. Further, Hicks (1961) pointed out that the variances in D and z values are not independent. The dependent nature of the variances had not been studied until Lenz and Lund (1977b), who reported calculation results incorporating the dependent nature of the D and z values. Although the coefficient of variation is a valuable statistical parameter in that it provides information on the reliability or probability of having a given value of a parameter, its real significance is to incorporate uncertainty into the lethality calculation process. The usual method, as used by Hicks (1961), to incorporate statistical parameters into lethality calculations is to perform error analysis using the coefficient of variation. Although this is an entirely appropriate approach, there are other approaches that should be investigated. Recently, Lenz and Lund (1976,1977b) used a Monte Carlo procedure to determine the effect of parameters describing heating rate and microbial destruction on certainty of calculated lethality. In one study (Lenz and Lund, 1977b), lethality distributions were generated for conduction-heating foods, while in another study Lenz and Lund (1976) examined convection heating. Lund (1978) worked on a statistical analysis of thermal process calculations. In a Monte Carlo procedure, the confidence interval for calculated lethality is estimated by: 1. Establishing the population distribution of each variable under normal processing conditions by randomly choosing a value for each variable. 2. Calculating lethality using an appropriate calculation model. Lenz and Lund (197713) suggested that a knowledge of the standard deviation of lethality could be applied to establish a minimum safety factor that could be incorporated into the system. Finally, one should realize that the variability in calculated process schedules emanates from experimentally determined parameters that are subject to biological variations and from the accuracy range of measuring system parameters. The function of quantifying that with ability would be to use the information in assigning reasonable safety factors that would accomplish the objective of the process (inactivation of spores to ensure commercial sterility) while minimizing destruction of quality factors (Lund, 1978). Patino and Heil (1985) introduced a statistical approach to error analysis in thermal process calculations. Traditionally, D and z are felt to be separate parameters that arise from the two-stage procedure of first estimating the value of D and then estimating a z value from

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thermal resistance curves. But Patino and Heil(l985) showed that these two parameters are highly correlated. When the values of D and z are estimated simultaneously by using nonlinear least squares, the correlation coefficient was as high as 0.92-0.96. When these variables cannot be completely eliminated, one alternative is to develop scientific methods to estimate and accurately define the safety factors. Hayakawa et al. (1988)have developed a computerized procedure for estimating the variability of process lethality when there are variations in all independent thermal process parameters of conduction-heating food packages. They have applied a well-known statistical procedure of the Monte Car10 technique combined with a reliable mathematical method for thermal process evaluation. The coefficients of variation in sterilizing values (Fo) were estimated from heat penetration data collected by processing 2 1 1 x 300 and 307 x 409 cans of spaghetti in tomato sauce (60 cans each). Their results agreed well with those computed by the developed computerized procedure. V. Suggestions for Future Work

It is evident from this review that a great deal of attention has been focused on the effect of pH on sterility and the attempts to redefine lowand high-acid cutoff. Also, there is a need for more data on the fundamental mechanisms of acid inhibition and studies on the practical implications of this information related to the safety of processed foods. However, there have been few investigations on the effect of uncertainties in D and z values on process schedules. Studies on these issues would yield deeper insight into the concepts and would facilitate redefinition of this terminology. It is important that D and z values be evaluated simultaneously at the actual conditions in the food being studied. There is also a paucity of information about on-line methods for determining sterility during aseptic processing. The several limitations of aseptic processing that have been brought out in this review provide food for thought to direct future research. Studies on these topics would provide further insight into the roles of such alternative heating methods as ohmic heating and microwave heating, particularly in the case of particulate foods. It has been established that a loerror in measurement of the temperature of processed food will lead to a 13 to 15% difference in the sterility value. This is an important matter of concern that demands adoption of more accurate means of temperature measurement.

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Hence, the future course of research should be mainly directed toward eliminating these uncertainties. There is a need to develop computer-aided methods for evaluating safety factors based on heat penetration data, to develop on-line control systems for achieving commercial sterility, and to develop direct methods for evaluating thermal processing by detecting living microbes, instead of D and z values. Improvements in aseptic processing should be made by devising on-line sterility evaluation techniques. Concerted efforts must be focused by academia and industry to develop methods and alternatives to eliminate the above-discussed uncertainties. Focusing on the above areas will provide a holistic approach for managing food safety in this era of streamlined processing and deregulation. Attempts should be made to improve the hitherto employed thermal processing techniques of the nineteenth century to lead to better thermal processing during the twenty-first century. VI. Conclusions

Despite these uncertainties, the long and continuing successes of commercial food processing are in large part due to the wide-ranging safety factors inherent in practice. Hence, it is necessary to develop alternative methods to minimize safety margins and thereby reduce nutrient losses and processing costs. One alternative is to develop either on- or off-line methods to ensure commercial sterility. Rapid microbial methods are being developed to evaluate thermally processed products for their sterility. Another area is development of on-line control systems. Though these approaches may not eliminate all uncertainties, they will at least ensure the development of minimum time-temperature profiles, with particular reference to nutrient retention, which will finally facilitate optimum sterilization. Subsequent parts of the series will deal with some of these techniques for optimum sterilization. It is important to prevent microbial contamination right from the point of harvesting foods through the preparation of recipes at the customer end. The hazard analysis and critical control point (HACCP) method and the good manufacturing practices (GMP) method are being developed for different food processing environments. These will be discussed in subsequent parts of this series. The research on eliminating the above-discussed uncertainties should reach the food processor as results become available. One possibility is to involve the food processor at the R&D stage of any new research in this direction. At the same time, the active participation of food industries will facilitate immediate implementation of the research outcome. The final result

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will be healthy, nutritious, and safe foods for customers, which is the basic objective of thermal processing. ACKNOWLEDGMENTS

The authors thank V. Prakash, Director of the Central Food Technological Research Institute, and A. Ramesh, N. G. Karanth, and R. Venkatakuppaiah, for their encouragement, and M. Asha, for her help in preparation of the manuscript. REFERENCES

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