Machine Vision with a CMOS-Based Hyperspectral Imaging\nSensor Enables Sensing Meat Freshness

Imaging\nspectral information of materials and analysis of its properties\nhave become an intriguing tool for consumer electronics used for food\ninspection, beauty care, etc. Those sensory physical quantities are\ndifficult to quantify. Hyperspectral imaging cameras, which capture\nthe figure and spectral information simultaneously, can be a good\ncandidate for nondestructive remote sensing. In this study, with the\naid of a hyperspectral imaging system (HIS) and machine learning (ML)\ntechniques, meat freshness is converted into a measurable physical\nquantity, i.e., the freshness index (FI). Herein, the FI is defined\nas meat fluorescence, which has a strong correlation with the bacterial\ndensity. Combined with ML techniques, hyperspectral data are processed\nmore efficiently. By employing linear discriminant and quadratic component\nanalyses, the FI can be estimated from its decision boundary after\nhyperspectral data are obtained in an unknown freshness state. We\ndemonstrate that the HIS integrated with ML performs as the artificial\neye and brain, which is advanced machine vision for consumer electronics,\nincluding refrigerators and smartphones. Advanced sensing versatility\nutilized by computational sensing systems allows hyper-personalization\nand hyper-customization of human life.