Quantification for Food Inspection enabled by Hyperspectral Imaging System and Machine Learning

Abstract Several important sensory physical quantities exist but are difficult to quantify, including food freshness. With the aid of a hyperspectral imaging system (HIS) and machine learning (ML), meat freshness is converted into a measurable physical quantity, i.e., freshness index (F. I.), in this study. F. I. is defined from meat fluorescence, which has a strong correlation with bacterial density, using a line-scan-type HIS stimulated at 365 nm. Combined with ML techniques, hyperspectral images are processed more efficiently. By employing linear discriminant and quadratic component analyses for hyperspectral images, F. I. can be estimated from its decision boundary after hyperspectral images are taken at an unknown freshness state. Advanced sensing versatility utilized by computational sensing systems allows hyper-personalization and hyper-customization of human life with the aid of home appliances and smartphones.