Machine Vision with CMOS based Hyperspectral Image Sensor Enables Meat Freshness Sensing

Imaging spectral information and analyzing its properties of materials have become intriguing for consumer electronics toward food inspection, beauty care and etc. Those sensory physical quantities are difficult to quantify. Hyperspectral cameras, which capture its figure and spectral information simultaneously, can be a good candidate for non-destructive remote sensing. In this study, 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 (FI). FI is defined from meat fluorescence, which has a strong correlation with bacterial density. Combined with ML techniques, hyperspectral data are processed more efficiently. By employing linear discriminant and quadratic component analyses, FI can be estimated from its decision boundary after hyperspectral data are obtained at an unknown freshness state. We demonstrate HIS grafted with ML performs as artificial eye and brain which is advanced machine vision for consumer electronics including refrigerators and smartphones. Advanced sensing versatility utilized by computational sensing systems allows hyper-personalization and hyper-customization of human life.