Intelligent Evaluation and Dynamic Prediction of Oysters Freshness with Electronic Nose Non-Destructive Monitoring and Machine Learning.

IF 4.9 3区 工程技术 Q1 CHEMISTRY, ANALYTICAL
Baichuan Wang, Yueyue Li, Kang Liu, Guangfen Wei, Aixiang He, Weifu Kong, Xiaoshuan Zhang
{"title":"Intelligent Evaluation and Dynamic Prediction of Oysters Freshness with Electronic Nose Non-Destructive Monitoring and Machine Learning.","authors":"Baichuan Wang, Yueyue Li, Kang Liu, Guangfen Wei, Aixiang He, Weifu Kong, Xiaoshuan Zhang","doi":"10.3390/bios14100502","DOIUrl":null,"url":null,"abstract":"<p><p>Physiological and environmental fluctuations in the oyster cold chain can lead to quality deterioration, highlighting the importance of monitoring and evaluating oyster freshness. In this study, an electronic nose was developed using ten partially selective metal oxide-based gas sensors for rapid freshness assessment. Simultaneous analyses, including GC-MS, TVBN, microorganism, texture, and sensory evaluations, were conducted to assess the quality status of oysters. Real-time electronic nose measurements were taken at various storage temperatures (4 °C, 12 °C, 20 °C, 28 °C) to thoroughly investigate quality changes under different storage conditions. Principal component analysis was utilized to reduce the 10-dimensional vectors to 3-dimensional vectors, enabling the clustering of samples into fresh, sub-fresh, and decayed categories. A GA-BP neural network model based on these three classes achieved a test data accuracy rate exceeding 93%. Expert input was solicited for performance analysis and optimization suggestions enhanced the efficiency and applicability of the established prediction system. The results demonstrate that combining an electronic nose with quality indices is an effective approach for diagnosing oyster spoilage and mitigating quality and safety risks in the oyster industry.</p>","PeriodicalId":48608,"journal":{"name":"Biosensors-Basel","volume":"14 10","pages":""},"PeriodicalIF":4.9000,"publicationDate":"2024-10-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11506465/pdf/","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Biosensors-Basel","FirstCategoryId":"5","ListUrlMain":"https://doi.org/10.3390/bios14100502","RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"CHEMISTRY, ANALYTICAL","Score":null,"Total":0}
引用次数: 0

Abstract

Physiological and environmental fluctuations in the oyster cold chain can lead to quality deterioration, highlighting the importance of monitoring and evaluating oyster freshness. In this study, an electronic nose was developed using ten partially selective metal oxide-based gas sensors for rapid freshness assessment. Simultaneous analyses, including GC-MS, TVBN, microorganism, texture, and sensory evaluations, were conducted to assess the quality status of oysters. Real-time electronic nose measurements were taken at various storage temperatures (4 °C, 12 °C, 20 °C, 28 °C) to thoroughly investigate quality changes under different storage conditions. Principal component analysis was utilized to reduce the 10-dimensional vectors to 3-dimensional vectors, enabling the clustering of samples into fresh, sub-fresh, and decayed categories. A GA-BP neural network model based on these three classes achieved a test data accuracy rate exceeding 93%. Expert input was solicited for performance analysis and optimization suggestions enhanced the efficiency and applicability of the established prediction system. The results demonstrate that combining an electronic nose with quality indices is an effective approach for diagnosing oyster spoilage and mitigating quality and safety risks in the oyster industry.

利用电子鼻无损监测和机器学习对牡蛎新鲜度进行智能评估和动态预测。
牡蛎冷链中的生理和环境波动会导致牡蛎质量下降,这凸显了监测和评估牡蛎新鲜度的重要性。本研究开发了一种电子鼻,使用十个基于部分选择性金属氧化物的气体传感器进行快速新鲜度评估。同时进行的分析包括 GC-MS、TVBN、微生物、质地和感官评估,以评估牡蛎的质量状况。在不同的储存温度(4 °C、12 °C、20 °C、28 °C)下进行实时电子鼻测量,以深入研究不同储存条件下的质量变化。利用主成分分析法将 10 维向量还原为 3 维向量,从而将样品分为新鲜、次新鲜和腐烂三个类别。基于这三个类别的 GA-BP 神经网络模型的测试数据准确率超过 93%。在性能分析和优化建议方面征求了专家意见,从而提高了已建立的预测系统的效率和适用性。结果表明,将电子鼻与质量指数相结合是诊断牡蛎变质和降低牡蛎行业质量和安全风险的有效方法。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 求助全文
来源期刊
Biosensors-Basel
Biosensors-Basel Biochemistry, Genetics and Molecular Biology-Clinical Biochemistry
CiteScore
6.60
自引率
14.80%
发文量
983
审稿时长
11 weeks
期刊介绍: Biosensors (ISSN 2079-6374) provides an advanced forum for studies related to the science and technology of biosensors and biosensing. It publishes original research papers, comprehensive reviews and communications. Our aim is to encourage scientists to publish their experimental and theoretical results in as much detail as possible. There is no restriction on the length of the papers. The full experimental details must be provided so that the results can be reproduced. Electronic files and software regarding the full details of the calculation or experimental procedure, if unable to be published in a normal way, can be deposited as supplementary electronic material.
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信