Intelligent Multisensor System For Analytical Control Of Sausages

IF 0.7 Q4 CHEMISTRY, ANALYTICAL
A. Kalinichenko, L. U. Arseniyeva
{"title":"Intelligent Multisensor System For Analytical Control Of Sausages","authors":"A. Kalinichenko, L. U. Arseniyeva","doi":"10.17721/moca.2019.57-72","DOIUrl":null,"url":null,"abstract":"The new technique of intelligent analysis of chemical aroma patterns of boiled sausages obtained by the electronic nose for authentication and microbiological safety assessment is developed. The informativeness of features extracted from steady-state responses of the multisensor system and robustness of chemometric algorithms for solving the objectives of qualitative and quantitative analysis of sausage volatile compounds are investigated. The classification model was built using maximum response values as input vectors of an optimized probabilistic neural network, which allows obtaining a 100 % accuracy of different sample grades identification and detection samples adulterated with soy protein. The method of partial least squares regression and area values as features were used for regression modelling and prediction of QMAFAnM with a relative error less than 12 % for a microbiological safety assessment of previously identified sausages. The use of the robust analytical technique to assess authentication, adulteration, total bacterial count for one measurement using the electronic nose in combination with machine learning algorithms will allow to significantly reduce the measurement time and the cost of analysis, and avoid subjective estimation of the results.","PeriodicalId":18626,"journal":{"name":"Methods and Objects of Chemical Analysis","volume":null,"pages":null},"PeriodicalIF":0.7000,"publicationDate":"2019-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Methods and Objects of Chemical Analysis","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.17721/moca.2019.57-72","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q4","JCRName":"CHEMISTRY, ANALYTICAL","Score":null,"Total":0}
引用次数: 0

Abstract

The new technique of intelligent analysis of chemical aroma patterns of boiled sausages obtained by the electronic nose for authentication and microbiological safety assessment is developed. The informativeness of features extracted from steady-state responses of the multisensor system and robustness of chemometric algorithms for solving the objectives of qualitative and quantitative analysis of sausage volatile compounds are investigated. The classification model was built using maximum response values as input vectors of an optimized probabilistic neural network, which allows obtaining a 100 % accuracy of different sample grades identification and detection samples adulterated with soy protein. The method of partial least squares regression and area values as features were used for regression modelling and prediction of QMAFAnM with a relative error less than 12 % for a microbiological safety assessment of previously identified sausages. The use of the robust analytical technique to assess authentication, adulteration, total bacterial count for one measurement using the electronic nose in combination with machine learning algorithms will allow to significantly reduce the measurement time and the cost of analysis, and avoid subjective estimation of the results.
香肠分析控制的智能多传感器系统
提出了利用电子鼻对煮香肠的化学香气特征进行智能分析的新技术,用于香肠的鉴定和微生物安全性评价。研究了多传感器系统稳态响应特征提取的信息量和化学计量学算法在香肠挥发性化合物定性和定量分析中的鲁棒性。以最大响应值作为优化后的概率神经网络的输入向量,建立分类模型,对不同等级的样品进行识别,并对掺假大豆蛋白的样品进行检测,准确率达到100%。采用偏最小二乘回归和面积值为特征的方法对QMAFAnM进行回归建模和预测,相对误差小于12%,用于对先前鉴定的香肠进行微生物安全性评估。使用强大的分析技术来评估认证,掺假,使用电子鼻与机器学习算法相结合的一次测量的细菌总数,将大大减少测量时间和分析成本,并避免对结果的主观估计。
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
CiteScore
1.00
自引率
14.30%
发文量
12
期刊介绍: The journal "Methods and objects of chemical analysis" is peer-review journal and publishes original articles of theoretical and experimental analysis on topical issues and bio-analytical chemistry, chemical and pharmaceutical analysis, as well as chemical metrology. Submitted works shall cover the results of completed studies and shall make scientific contributions to the relevant area of expertise. The journal publishes review articles, research articles and articles related to latest developments of analytical instrumentations.
×
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