Evaluation of Postharvest Senescence in Broccoli Via Hyperspectral Imaging

Abstract

Fresh fruits and vegetables are invaluable for human health, but their quality deteriorates before reaching consumers due to ongoing biochemical processes and compositional changes. The current lack of any objective indices for defining "freshness" of fruits or vegetables limits our capacity to control product quality leading to food loss and waste. It has been hypothesized that certain proteins and compounds such as glucosinolates can be used as an indicator to monitor the freshness of vegetables and fruits. However, it is challenging to "visualize" the proteins and bioactive compounds during the senescence processes. In this work, we propose machine learning hyperspectral image analysis approaches for estimating glucosinolates levels to detect postharvest senescence in broccoli. Therefore, we set out the research to quantify glucosinolates as "freshness-indicators" which aid in the development of an innovative and accessible tool to precisely estimate the freshness of produce. Such a tool would allow for significant advancement in postharvest logistics and supporting the availability for high-quality and nutritious fresh produce.