Figure 18. Computer-generated plot of measured retort temperature and calculated center temperature and accomplished F0 for a given thermal process. Source: Ref. 3, reprinted with permission, copyright 1992 by Marcel Dekker, Inc., New York.

Process Optimization

The principle objective of thermal process optimization is to maximize product quality, minimize undesirable changes, minimize cost, and maximize profits. At all times, a minimal process must be maintained to exclude the danger from microorganisms of public health and spoilage concern. Five elements common to all optimization problems are performance or objective function (nutrients, texture, and sensory characteristics), decision variables (retort temperature and process time), constraints (practical limits for temperatures and required minimal lethality), mathematical model (analytical, finite differences, and finite element), and optimization technique (search, response surface, and linear or nonlinear programming).

Optimization theory makes use of the different temperature sensitivity of microbial and quality factor destruction rates. Microorganisms have lower decimal reduction time (less resistant to heat) and a lower Z-value (more sensitive to temperature) than most quality factors. Hence, a higher temperature will result in preferential destruction of microorganisms over the quality factor. Especially applied to liquid product either in a batch in-container mode or in continuous aseptic systems, the higher temperature with shorter time offers a great potential for quality optimization. However, for conduction heating foods, one of the major limitations is the slower heating. All higher temperatures do not necessarily favor the best quality retention because they also expose the product nearer the surface to more severe temperature than the product at the center, which might result in diminished overall quality. Using a finite differences computer simulation program, it has been demonstrated that the optimal process temperature is around 250°F (121°C) for maximized thiamin retention. In fact, processing at temperatures beyond 265°F was shown to result in poorer thiamine retention than processing at 240°F (9).

Product quality optimization can also be accomplished by promoting faster and more uniform heating in the product by other means, such as optimized container geometry in the form of appropriate height-to-diameter ratios, alternate packaging materials and shave such as the thin-profile retort pouch or semirigid container, or an agitated process for foods that are normally conduction heated but can flow under the influence of agitation (6).

On-Line Computer Control

Traditional control of thermal process operations has consisted of maintaining specified operating conditions that have been predetermined from product and process development research, such as the process calculations for the time and temperature of a batch cook. Sometimes unexpected changes can occur during the course of the process operation or at some point upstream in a processing sequence such that prespecified processing conditions are no longer valid or appropriate, and off-specification product that is produced must be either reprocessed or destroyed at appreciable economic loss. These types of situations can be of critical importance in food processing operations because the physical process variables that can be measured and controlled are often only indicators of complex biochemical reactions that are required to take place under the specified process conditions.

Because of the important emphasis placed on the public safety of canned foods, processors operate in strict compliance with the Food and Drug Administration's Low-Acid Canned Food Regulations. Among other things, these regulations require strict documentation and recordkeeping of all critical control points in the processing of each retort load or batch of canned product. Particular emphasis is placed on product batches that experience an unscheduled process deviation, such as when a drop in retort temperature occurs during the course of the process that may result from loss of steam pressure. In such a case, the product will not have received the established scheduled process and must be either destroyed, fully reprocessed, or set aside for evaluation by a competent processing authority. If the product is judged to be safe, then batch records must contain documentation showing how that judgement was reached. If judged unsafe, then the product must be fully reprocessed or destroyed. Such practices are costly.

In recent years, food engineers knowledgeable in the use of engineering mathematics and scientific principles of heat transfer have developed computer models (described earlier in this section) capable of simulating thermal processing of conduction-heated canned foods. These models make use of numerical solutions to mathematical heat transfer equations capable of predicting accurately the internal product cold spot temperature in response to any dynamic temperature experienced by the retort during the process. The accomplished lethality (F0) for any thermal process is easily calculated by numerical integration of the predicted cold spot temperature over time, as explained previously. Thus, if the cold spot temperature can be accurately predicted over time, so can accumulated process lethality.

Computer-based intelligent on-line control systems make use of these models as part of the decision-making software in a computer-based on-line control system. Instead of specifying the retort temperature as a constant boundary condition, the actual retort temperature is read directly from sensors located in the retort and is continually updated with each iteration of the numerical solution. Using only the measured retort temperature as input to the control system, the model operates as a subroutine calculating the internal product cold spot temperature at small time intervals for computer iteration in carrying out the numerical solution to the heat conduction equation by finite differences. At the same time, the model also calculates the accomplishing process lethality associated with cold spot temperature in real time as the process is under way. At each time step, the subroutine simulates the additional lethality that will be contributed by the cooling phase if cooling were to begin at that time. In this way, the control system decision of when to end heating and begin cooling is withheld until the model has determined that final target process lethality will be reached at the end of cooling (10).

By programming the control logic to continue heating until the accumulated lethality has reached some designated target value, the process will always end with the desired level of sterilization (Fj regardless of an unscheduled process temperature deviation. At the end of the process, complete documentation of measured retort temperature history, calculated center temperature history, and accomplished sterilization (F0) can be generated in compliance with regulatory record-keeping requirements. Such documents are shown in Figure 19 for a normal process and for the same intended process with an unexpected deviation.

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