Supervised learning-based artificial senses for non-destructive fish quality classification

Abstract

Human sensory techniques are inadequate for automating fish quality monitoring and maintaining controlled storage conditions throughout the supply chain. The dynamic monitoring of a single quality index cannot anticipate explicit freshness losses, which remarkably drops consumer acceptability. For the first time, a complete artificial sensory system is designed for the early detection of fish quality prediction. At non-isothermal storages, the rainbow trout quality is monitored by the gas sensors, texturometer, pH meter, camera, and TVB-N analysis. After data preprocessing, correlation analysis identifies the key parameters such as trimethylamine, ammonia, carbon dioxide, hardness, and adhesiveness to input into a back-propagation neural network. Using gas and textural key parameters, around 99 % prediction accuracy is achieved, precisely classifying fresh and spoiled classes. The regression analysis identifies a few gaps due to fewer datasets for model training, which can be reduced using few-shot learning techniques in the future. However, the multiparametric fusion of texture with gases enables early freshness loss detection and shows the capacity to automate the food supply chain completely.