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Automated detection of stale beef from electronic nose data

文献类型： 外文期刊

作者： Jia, Wenshen 1 ; Lv, Haolin 1 ; Liu, Yang 1 ; Zhou, Wei 7 ; Qin, Yingdong 1 ; Ma, Jie 6 ;

作者机构： 1.Beijing Acad Agr & Forestry Sci, Inst Qual Stand & Testing Technol, 11 Zhong Rd, Beijing 100097, Peoples R China

2.Minist Agr & Rural Affairs, Dept Risk Assessment Lab Agroprod Beijing, Beijing, Peoples R China

3.Minist Agr & Rural Affairs, Key Lab Urban Agr North China, Beijing, Peoples R China

4.Anhui Inst Innovat Ind Technol, Luan Branch, Luan, Peoples R China

5.China Three Gorges Univ, Coll Comp & Informat, Yichang, Peoples R China

6.Beijing Informat Sci & Technol Univ, Mech Elect Engn Sch, Beijing 100192, Peoples R China

7.Hebei Food Safety Key Lab, Food Inspection & Res Inst, Shijiazhuang, Peoples R China

8.Beijing Univ Agr, Coll Intelligent Sci & Engn, Beijing, Peoples R China

关键词： back propagation neural network; confusion matrix; electronic nose; k-nearest neighbor; linear discriminant analysis; machine learning; principal component analysis; stale beef; support vector machine

期刊名称：FOOD SCIENCE & NUTRITION （ 影响因子：3.8； 五年影响因子：4.5 ）

ISSN： 2048-7177

年卷期： 2024 年 12 卷 11 期

页码：

收录情况： SCI

摘要： Accurate detection of stale beef on the market is important for protecting the legitimate rights and interests of consumers. To this end, we combined electronic nose measurements with machine learning technology to classify beef samples. We used an electronic nose to collect information about the odor characteristics of different beef samples and used linear discriminant analysis to reduce data dimensionality. We then classified samples using the following algorithms: extreme gradient boosting, logistic regression, K-nearest neighbor, random forest, support vector machine, and neural networks for pattern recognition. We assessed model performance using a 10-fold cross-validation technique. All these methods reached an accuracy of 95% or above, with F1 scores and AUC values above 0.96. The support vector machine algorithm outperformed all other models, achieving perfect recognition with 100% accuracy and F1/AUC scores of 1.0. Our study demonstrates that electronic nose data combined with support vector machine can be used to successfully discriminate between stale and fresh beef, paving the way for novel research directions in the detection of stale beef.

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