Automated detection of stale beef from electronic nose data

文献类型： 外文期刊

第一作者： Jia, Wenshen

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

作者机构：

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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