Effect of Pulsed Electric Field on the Drying Kinetics of Apple Slices during Vacuum-Assisted Microwave Drying: Experimental, Mathematical and Computational Intelligence Approaches

Abstract

One of the challenges in the drying process is decreasing the drying time while preserving the product quality. This work aimed to assess the impact of pulsed electric field (PEF) treatment with varying specific energy levels (15.2–26.8 kJ/kg) in conjunction with a microwave vacuum dryer (operating at energy levels of 100, 200 and 300 W) on the kinetics of drying apple slices (cv. Gravenstein). The findings demonstrated a notable reduction in the moisture ratio with the application of pulsed electric field treatment. Based on the findings, implementing PEF reduced the drying time from 4.2 to 31.4% compared to the untreated sample. Moreover, two mathematical models (viz. Page and Weibull) and two machine learning techniques (viz. artificial neural network and support vector regression) were used to predict the moisture ratio of the dried samples. Page’s and Weibull’s models predicted the moisture ratios with R2 = 0.958 and 0.970, respectively. The optimal topology of machine learning to predict the moisture ratio was derived based on the influential parameters within the artificial neural network (i.e., training algorithm, transfer function and hidden layer neurons) and support vector regression (kernel function). The performance of the artificial neural network (R2 = 0.998, RMSE = 0.038 and MAE = 0.024) surpassed that of support vector regression (R2 = 0.994, RMSE = 0.012 and MAE = 0.009). Overall, the machine learning approach outperformed the mathematical models in terms of performance. Hence, machine learning can be used effectively for both predicting the moisture ratio and facilitating online monitoring and control of the drying processes. Lastly, the attributes of the dried apple slices, including color, mechanical properties and sensory analysis, were evaluated. Drying apple slices using PEF treatment and 100 W of microwave energy not only reduces drying time but also maintains the chemical properties such as the total phenolic content, total flavonoid content, antioxidant activity), vitamin C, color and sensory qualities of the product.

Keywords

• apple drying
• artificial neural networks
• machine learning
• modeling
• support vector regression
• vacuum-assisted microwave drying