Comprehensive Analysis and Optimization of Peach (Prunus persica) Vacuum Drying: Employing Principal Component Analysis, Artificial Neural Networks and the Standard Score Approach

Abstract

Dried peaches are widely consumed as a snack food product and used as an ingredient in cereals as well in chocolate and energy bars. Accordingly, the main objective of this investigation was to optimize the vacuum-drying process for peaches using a combination of three different statistical methods: principal component analysis, the standard score method and an artificial neural network approach. Applied input drying parameters were temperature (50–70 °C), pressure (20–120 mbar) and time (6–10 h), while the investigated output parameters were moisture content, water activity, total color change, phenolic and flavonoid contents and antioxidant activity. It was noted that all investigated output parameters constantly decreased (moisture content, water activity) and increased (total color change, total phenolic and flavonoid contents and antioxidant activity (FRAP, DPPH and ABTS assays)) in accordance with the applied drying temperature. The key variables accounted for 86.33% of data variance based on the PCA results, while the SS and ANN method resulted in the same optimal drying conditions: 60 °C, 70 mbar and 6 h, which indicated the effectiveness of the applied statistical methods.