Effects of storage conditions and packaging materials on the postharvest quality of fresh Chinese tomatoes and the optimization of the tomatoes' physiochemical properties using machine learning techniques

Abstract

This study evaluated the impacts of varying storage temperatures and packaging materials on the colour, enzymatic activity, phytochemical content, and antioxidant properties of Chinese tomatoes during storage. More so, machine learning (ML) and optimization models were employed to predict and optimize the effects of storage period, storage temperatures, and packaging materials on the tomatoes' physicochemical properties. According to two-way ANOVA analysis, storage temperature impacted all parameters except L and anthocyanin. Furthermore, packaging demonstrated a substantial effect on all factors. The combined effect of storage temperature and packaging also impacted all measurements except for L and ΔE. It was possible to obtain the optimized conditions for storing the tomatoes using four constructed models and two different optimization algorithms. According to the optimization findings from all four ML models, storing the product at 4 °C with 85 % relative humidity (RH) results in a higher-quality end product than at 25 °C. Additionally, the majority of the models predict that using NPHDP packing material will typically produce tomatoes that are of higher quality. The optimization of storage conditions and packaging materials is vital for maintaining the quality and nutritional value of tomatoes throughout their postharvest.