Potato chips, popcorn, puffed corn curls, and saltines were equilibrated to various water activities (a,) over saturated salt solutions. Sensory panels determined the crispness and textural hedonic quality of the humidified products by the magnitude estimation technique coupled to a verbal concept scale. Critical water activities (a,), where the products became organoleptically unacceptable, generally fell in the 0.35-0.50 a, range. Instron analyses showed that the force-deformation curve changed distinctly near the a, for saltines and puffed corn curls, while the curve changed more gradually with increasing a, for popcorn. Potato chips did not produce a consistently shaped force-deformation curve. The cohesiveness value of popcorn was found to be a good indicator of its sensory crispness.

A systematic approach that allows the correlation of the response of a Time-Temperature Indicator ('ITI) to the consumed and remaining shelf life of a food product exposed to the same variable temperature conditions was developed. The approach was based on kinetic principles and is applicable to a TTI-food system without necessitating side by side testing. The applicability of three major TII types to the developed scheme was studied. Experiments under several isothermal conditions were conducted and kinetic parameters for the three 'IT1 were determined. All TTI showed an Arrhenius temperature depcndence and their calculated activation energies ranged from 10 to 40 kcal/mol .

A major goal for the food scientist is the prediction of the change in quality of a particular food as a function of both time and environmental conditions. This has become the focus of many research and development projects because the information obtained is needed by those in the food industry so that they can In order to make useful predictions about shelf life, the research scientist needs information regarding 1) the potential major modes for loss of quality of the product, 2) the factors which control the initial quality or nutritional value during manufacture, 3) the environmental conditions the food will be exposed to including temperature, relative humidity and light, 4) whether it is packaged in a semi-permeable container, and, if so, the permeability of that film to oxygen, water vapor, and light, and 5) the kinetics of the reactions leading to loss of quality or nutritional value as a function of the reaction phase conditions in the food and the external environment.Since foods are very complex systems (food science is sometimes referred to as the study of "messy" chemistry), it is not always possible to isolate clear-cut chemical reaction mechanisms which lead to the observed changes in quality or to have elegant mathematical models that can describe the reaction rate under a variety of conditions. Thus compromises must be made. In addition, in the industrial research and development environment, it is not always possible to do extended studies to isolate the mechanisms and develop the data, so abuse conditions are chosen to get quick results. From the data obtained under these abuse conditions it is hoped to project what would happen under normal, long-term storage.This article will briefly review the possible major modes of deterioration that should be evaluated, then a general review of the application of simple kinetic methods will be made, and finally some mathematical examples will be given. For a more extensive review of shelf-life deterioration see Labuza U). A food system is very complex. Assuming that, for a given mode of deterioration, the above assumptions hold, one can simply write for the rate of gain of an undesirable quality factor [B] (7) and (8) [B]. Since a straight line can always be drawn between two points, as we add more data we increase our ability to predict both outside as well as inside the range of the data points.Thus, in Figure 1, a linear plot of quality versus time for a loss in quality of a food would allow easy extrapolation to the end of shelf life (ts) since the zero-order data fit a good straight line. However, the first-order data on the same coordinates would be more difficult to extrapolate because of the curvature of the graph. Plotting the first-order data as in Figure 2 solves this extrapolation problem since it gives us the desired straight line.Interestingly, in looking at the loss-of-quality data in Figure 1 for up to 50% loss (if this were as far as one collected data) one would find it hard to distinguish between zero-and first-order. The poor...

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