Improvement of pear slices drying by pretreatments and microwave-assisted convective drying method: drying characteristics, modeling of artificial neural network, principal component analysis of quality parameters

Abstract

The drying process has been widely used for the preservation of foodstuffs. Among the drying methods, convective drying is frequently preferred. Although frequently used, various techniques need to be developed to overcome the existing disadvantages. The study aimed to enhance the drying and quality parameters of the pear slices by microwave-assisted convective drying and pretreatments (citric acid, ethanol, and ultrasound) and compare thin-layer and artificial neural network modeling (ANN). Microwave-assisted convective drying and pretreatments reduced drying time compared to convective-dried samples. The lowest drying time was obtained from the samples pretreated with 100% ethanol. On the other hand, ANN modeling gave the best prediction results for drying curves. Additionally, L* values decreased, whereas a* and b* values increased after the drying process. The citric acid pretreatment provided the lowest color change. Moreover, chroma values increased; however, hue angle values of the samples decreased compared to the 90.08 value of the fresh samples. An increase in the color intensity and a decrease in the yellowness were observed after drying. Total phenolic content (TPC) and antioxidant activity (AA) were highly affected by drying processes and pretreatments compared to fresh samples. The highest losses of TPC and AA were determined after ethanol and ultrasound after pretreatments. According to PCA results in terms of total phenolic content, antioxidant activity, and color values, ethanol and ultrasound-pretreated samples share a similar plane, while control and citric acid-pretreated samples exhibit a similar arrangement. Additionally, fresh and microwave-assisted convective-dried samples stand apart from each other and the rest of the examples, showcasing a unique positioning. In conclusion, microwave-assisted convective drying and pretreatments had a positive effect on drying time. However, these methods need to be enhanced in terms of quality parameters. Besides, ANN may be suggested for the prediction of the drying process.