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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.