Sensory predictive analysis of freshness of food products under different lighting conditions

Recently, the efforts to use machine vision and artificial intelligence to evaluate the characteristics of food products has increased significantly. This is largely because, these technologies put up considerable advances in areas where the humans fail. We develop a sensory panel to study the effects of lighting conditions viz., light temperature and lighting power on the freshness of a food product. Panelists evaluated the product in terms of purchase intent (line scale from 0 to 100), overall liking (line scale from 0 to 100), and freshness (line scale from 0 to 100). Later, using machine learning models, predictive analytics is conducted to analyze the correlation among the light conditions and panliests’ gradings.