Design of a Portable Electronic Nose for Identification of Minced Chicken Meat Adulterated With Soybean Protein Isolate

IF 1.9 4区 农林科学 Q4 BIOTECHNOLOGY & APPLIED MICROBIOLOGY
Min Zhou, Chunxia Dai, Joshua Harrington Aheto, Xiaorui Zhang
{"title":"Design of a Portable Electronic Nose for Identification of Minced Chicken Meat Adulterated With Soybean Protein Isolate","authors":"Min Zhou,&nbsp;Chunxia Dai,&nbsp;Joshua Harrington Aheto,&nbsp;Xiaorui Zhang","doi":"10.1111/jfs.13163","DOIUrl":null,"url":null,"abstract":"<div>\n \n <p>The study aimed to develop a portable electronic nose system for detecting adulteration with soybean protein isolate (SPI) in chicken meat. The system mainly consisted of three parts: the gas sensor array, the DSP28335 control board, and the upper computer. The DSP28335 control board, developed using C language, included analog to digital converter (ADC) module, digital output (DO) module, pulse width modulation (PWM) module, controller area network (CAN) module, power module, drive circuit, and so forth. The upper computer, developed using LabVIEW, facilitated user interaction with the user by primarily handling CAN configuration and monitoring, displaying and storing sensor data, temperature and flow data, and sending and monitoring electronic nose commands. The feasibility of the proposed electronic nose for characterizing adulterated chicken meat was tested on six classes of chicken meat that had been adulterated with varied quantities of SPI. The mass fractions of SPI were 0%, 5%, 10%, 15%, 20%, and 25%, respectively. On the basis of odor data from the electronic nose, K-nearest neighbor (KNN), linear discriminant analysis (LDA), and support vector machine (SVM) were applied to qualitatively distinguish minced chicken meat with different adulteration ratios. The results showed that the SVM model had the best recognition effect. When the best parameters (<i>c</i>, <i>g</i>) were <i>c</i> = 16 and <i>g</i> = 1, the accuracy of SVM model was 97.22% and 93.75% in the training and testing sets, respectively. These results demonstrated that the portable electronic nose designed in this paper effectively identifies minced chicken meat under various adulteration conditions, enabling rapid and nondestructive detection of chicken meat adulteration.</p>\n </div>","PeriodicalId":15814,"journal":{"name":"Journal of Food Safety","volume":"44 5","pages":""},"PeriodicalIF":1.9000,"publicationDate":"2024-09-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Food Safety","FirstCategoryId":"97","ListUrlMain":"https://onlinelibrary.wiley.com/doi/10.1111/jfs.13163","RegionNum":4,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q4","JCRName":"BIOTECHNOLOGY & APPLIED MICROBIOLOGY","Score":null,"Total":0}
引用次数: 0

Abstract

The study aimed to develop a portable electronic nose system for detecting adulteration with soybean protein isolate (SPI) in chicken meat. The system mainly consisted of three parts: the gas sensor array, the DSP28335 control board, and the upper computer. The DSP28335 control board, developed using C language, included analog to digital converter (ADC) module, digital output (DO) module, pulse width modulation (PWM) module, controller area network (CAN) module, power module, drive circuit, and so forth. The upper computer, developed using LabVIEW, facilitated user interaction with the user by primarily handling CAN configuration and monitoring, displaying and storing sensor data, temperature and flow data, and sending and monitoring electronic nose commands. The feasibility of the proposed electronic nose for characterizing adulterated chicken meat was tested on six classes of chicken meat that had been adulterated with varied quantities of SPI. The mass fractions of SPI were 0%, 5%, 10%, 15%, 20%, and 25%, respectively. On the basis of odor data from the electronic nose, K-nearest neighbor (KNN), linear discriminant analysis (LDA), and support vector machine (SVM) were applied to qualitatively distinguish minced chicken meat with different adulteration ratios. The results showed that the SVM model had the best recognition effect. When the best parameters (c, g) were c = 16 and g = 1, the accuracy of SVM model was 97.22% and 93.75% in the training and testing sets, respectively. These results demonstrated that the portable electronic nose designed in this paper effectively identifies minced chicken meat under various adulteration conditions, enabling rapid and nondestructive detection of chicken meat adulteration.

设计用于识别掺有大豆分离蛋白的鸡肉碎的便携式电子鼻
该研究旨在开发一种用于检测鸡肉中大豆分离蛋白(SPI)掺假的便携式电子鼻系统。该系统主要由三部分组成:气体传感器阵列、DSP28335 控制板和上位机。DSP28335 控制板采用 C 语言开发,包括模数转换(ADC)模块、数字输出(DO)模块、脉宽调制(PWM)模块、控制器局域网(CAN)模块、电源模块、驱动电路等。使用 LabVIEW 开发的上位机主要通过处理 CAN 配置和监控、显示和存储传感器数据、温度和流量数据以及发送和监控电子鼻指令来促进与用户的交互。对掺入了不同数量 SPI 的六种鸡肉进行了测试,以确定所建议的电子鼻用于鉴定掺假鸡肉的可行性。SPI 的质量分数分别为 0%、5%、10%、15%、20% 和 25%。在电子鼻气味数据的基础上,应用 K-nearest neighbor(KNN)、线性判别分析(LDA)和支持向量机(SVM)对不同掺假比例的碎鸡肉进行定性区分。结果表明,SVM 模型的识别效果最好。当最佳参数(c,g)为 c = 16 和 g = 1 时,SVM 模型在训练集和测试集的准确率分别为 97.22% 和 93.75%。这些结果表明,本文设计的便携式电子鼻能有效识别各种掺假条件下的碎鸡肉,实现了对鸡肉掺假的快速、无损检测。
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
Journal of Food Safety
Journal of Food Safety 工程技术-生物工程与应用微生物
CiteScore
5.30
自引率
0.00%
发文量
69
审稿时长
1 months
期刊介绍: The Journal of Food Safety emphasizes mechanistic studies involving inhibition, injury, and metabolism of food poisoning microorganisms, as well as the regulation of growth and toxin production in both model systems and complex food substrates. It also focuses on pathogens which cause food-borne illness, helping readers understand the factors affecting the initial detection of parasites, their development, transmission, and methods of control and destruction.
×
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