{"title":"Supervised learning-based artificial senses for non-destructive fish quality classification","authors":"","doi":"10.1016/j.bios.2024.116770","DOIUrl":null,"url":null,"abstract":"<div><p>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.</p></div>","PeriodicalId":259,"journal":{"name":"Biosensors and Bioelectronics","volume":null,"pages":null},"PeriodicalIF":10.7000,"publicationDate":"2024-09-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Biosensors and Bioelectronics","FirstCategoryId":"1","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0956566324007760","RegionNum":1,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"BIOPHYSICS","Score":null,"Total":0}
引用次数: 0
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.
期刊介绍:
Biosensors & Bioelectronics, along with its open access companion journal Biosensors & Bioelectronics: X, is the leading international publication in the field of biosensors and bioelectronics. It covers research, design, development, and application of biosensors, which are analytical devices incorporating biological materials with physicochemical transducers. These devices, including sensors, DNA chips, electronic noses, and lab-on-a-chip, produce digital signals proportional to specific analytes. Examples include immunosensors and enzyme-based biosensors, applied in various fields such as medicine, environmental monitoring, and food industry. The journal also focuses on molecular and supramolecular structures for enhancing device performance.