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The objective of this research was to explore the advantages of apple slices dehydration (cv. Granny Smith) using a Refractance Window (RW) with and without osmotic dehydration as pretreatment and compared with conventional hot‐air drying considering the quality attributes such as color and texture. For pretreatment, the apple slices were osmotically dehydrated and coupled to a moderate electric field (9.3 V/cm) using a sucrose solution (45°Brix) at 40°C until an Aw = 0.95 was achieved. Then, apple slices (with and without pretreatment) were dehydrated using RW at 55, 75, and 95°C and compared with conventional hot‐air drying at 55°C as a control. The results showed that with dehydration using RW, both with and without pretreatment at 95°C, it was possible to achieve a similar quality of apple slices when compared with conventional hot‐air drying at 55°C, but with a significant reduction in processing time. Additionally, the dehydration process using RW presented anomalous diffusion behavior on apple slices with an α > 1. PRACTICAL APPLICATIONS: Refractance Window (RW) is a technology used for dehydration of fruit and vegetables slices and pulp based on the refractance properties of wet materials. RW has reported promising results in processing time reduction as compared with conventional dehydration methods. In addition, the quality of the end products by RW is higher as compared with conventional drying methods. In this research, we have showed that the final product (dehydrated apple slices) using RW attained quality values (color and texture) similar to those obtained with osmotic dehydration (OD) coupled with moderate electric field (MEF)/hot‐air drying. However, the time spent by RW (50 min) was much lower than OD coupled with MEF/hot‐air drying (240 min). Then, the main advantage of RW is the capacity to dehydrate fruit and vegetables materials using temperatures around 100°C, which allow both, reduce processing time and also attain high‐quality retention.

The processing of squid generates nearly 50% of raw material as a waste by-product rich in protein. Use of this by-product as a raw material in the manufacture of a squid protein hydrolysate would add considerable product value to the industry. Enzyme hydrolysis of squid waste protein is a complex process because of a number of inherent simultaneous inhibition and enzyme inactivation reactions which occur during hydrolysis. Artificial neural networks (ANNs) are very effective in developing predictive models for processes involving complex reaction kinetics that would otherwise be difficult to develop by more traditional deterministic approaches. The objective of this work was to develop a kinetic model to describe the kinetics of enzyme hydrolysis of squid waste protein using ANNs. A series of enzyme hydrolysis experiments were carried out on samples of squid waste under specified conditions of temperature, pH, and initial enzyme and substrate concentrations. Experimental data in the form of substrate concentration over time were taken as real time course data. These data were fitted with a cubic spline procedure for use in an ANNs training process to estimate reaction rates for the model. The effect of a number of hidden processing elements on the error in prediction was studied, and the ANN showing necessary prediction performance was constructed. Maximum differences between experimental and predicted values of substrate concentration at any point in time ranged from 0.098 to 0.29 g/L (3–4%). Correlation coefficients between predicted and experimental results were also, respectively, high, ranging from 0.988 to 0.992. PRACTICAL APPLICATIONS Results from this study would suggest that artificial neural networks (ANNs) can be very effective in developing predictive models for processes involving complex reaction kinetics. Enzyme hydrolysis of squid waste protein is an example of such a process because of a number of inherent simultaneous inhibition and enzyme inactivation reactions occurring during hydrolysis. This work resulted in the successful development of a kinetic model to describe the kinetics of enzyme hydrolysis of squid waste protein using ANNs.

The uncertainty of shelf-life estimations for refrigerated foods exposed to changing temperature was quantified by considering the contribution of the experimental variability of the model parameters. Assuming the real distribution of the parameters can be replaced by an unknown empirical distribution, this uncertainty was analyzed by a bootstrap methodology. Independent sets of heat transfer and microbial growth parameters were chosen randomly from experimental values. Shelf-life values were estimated for each set, with the variability evaluated using an SD value. The procedure was repeated 10 times to obtain 10 shelf-life SDs. The variation coefficient of these SDs is an absolute measurement of shelf-life variability. At the recommended variation coefficient of 0.1, a sample size of 25 was found acceptable. The shelf-life variability estimated was similar (±1 day) for surimi in cardboard and expanded polystyrene containers with different shelf life and temperature profiles. The method is applicable to other predictions of food stability. PRACTICAL APPLICATIONS The effect of the experimental variability of model parameters on shelf-life estimations confirmed the need to assess the uncertainty of model predictions. Another beneficial aspect of the approach presented is that it encourages an integrated approach to food safety and shelf life. Decisions on raw materials and modifications to processing operations affect the initial microbial load of a product. Improvement of storage, distribution and retail facilities reduces temperature abuse. Product size, geometry and packaging choice are also aspects that should be considered. The impact on shelf life of all these factors can be examined by the integrated microbial and heat-transfer models of the type here presented. The increasing availability of microbial growth parameters and electronic recorders to monitor temperature during processing, storage and distribution facilitate their implementation. The impact of the experimental variability of model parameters is important. For example, this study suggested the need for microbial determinations with lower experimental variability.

This paper describes the development of a mathematical model to predict consequences of temperature abuse on shelf-life of Pacific Hake packed in modified atmosphere. The model includes simultaneous heat and mass transfer phenomena coupled with a predictive shelf-life model for packed fish. The model was solved using an explicit finite difference scheme. The simulated results are in good agreement with the experimental results. The model predicted temperature, gas concentration and relative humidity within < 0.6 [°C], < 1.43% and 3%, respectively. Shelf-life model predicts that, even if the fraction of the total storage time at an abusive temperature is small (4.3%), the reduction in shelf-life can be significant (25%). The predicted shelf-life without temperature abuse is consistent with literature results. Optimum MAP systems must reach pseudo-equilibrium headspace gas composition which maximize the shelf-life of food considering possible environmental temperature abuses.

Optimization of thermal processing in the commercial sterilization of canned foods is of great interest because the canning industry plays an important role within the economy of the food processing sector. Many food canning plants operate in a batch mode with a battery of individual batch retorts. The aim of this study was to propose and analyze several criteria and methodologies for optimum design and operation of such retort systems. Two criteria were proposed in the case of choosing the optimum number of retorts to be installed when designing a new batch-operated canning line. The third criterion dealt with seeking optimum process conditions for maximizing output from a fixed number of retorts when processing small batches of different products and container sizes. In the case of new plant design optimization, one objective was to determine the optimum number of retorts that would minimize on-going processing costs related to labor and energy. Retort scheduling (programming) was studied from which a simple mathematical expression was derived for this purpose. A second objective was to determine the optimum number of retorts that would maximize the net present value of initial investment. Approaches based upon engineering economics were studied from which to develop a mathematical procedure for this purpose. In the case of maximizing output from a fixed number of retorts for different products and container sizes, isolethal processes were identified for various product/containers from which a common set of process conditions could be chosen for simultaneous processing of different product lots in the same retort.