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Article(id=1320881294813278794, tenantId=1146029695717560320, journalId=1260987677001138203, issueId=1320881182762447607, articleNumber=null, orderNo=null, doi=10.13386/j.issn1002-0306.2025070081, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=null, receivedDate=1751904000000, receivedDateStr=2025-07-08, revisedDate=null, revisedDateStr=null, acceptedDate=null, acceptedDateStr=null, onlineDate=1792852241470, onlineDateStr=2026-10-24, pubDate=1786723200000, pubDateStr=2026-08-15, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1792852241470, onlineIssueDateStr=2026-10-24, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1792852241470, creator=13701087609, updateTime=1792852241470, updator=13701087609, issue=Issue{id=1320881182762447607, tenantId=1146029695717560320, journalId=1260987677001138203, year='2026', volume='47', issue='16', pageStart='1', pageEnd='537', issueExtLink='null', onlineDate='null', pubDate='1786723200000', pubDateStr='2026-08-15', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=-1, specialIssue=null, createTime=1792852214756, creator='13701087609', updateTime=1792852214756, updator='13701087609', preIssue=null, nextIssue=null, articleTotal=null, ext=null, issueFiles=null, downloadFileDto=null}, startPage=302, endPage=312, ext={EN=ArticleExt(id=1320881294981050955, articleId=1320881294813278794, tenantId=1146029695717560320, journalId=1260987677001138203, language=EN, title=Beef Freshness Detection Using Artificial Olfaction–Vision and Deep Learning, columnId=1261343852057772164, journalTitle=Science and Technology of Food Industry, columnName=Packaging and Machinery, runingTitle=null, highlight=null, articleAbstract=

This study employed artificial olfaction–vision equipment combined with multidata fusion and deep learning technologies to accurately predict the freshness of chilled beef, providing a theoretical basis and methodological support for the rapid detection of meat product freshness. In this study, a self-developed electronic nose and machine vision system was used to collect odor and image information from beef samples. The semi-micro Kjeldahl method and an acid–base meter were used to determine the total volatile basic nitrogen content and pH of the samples, which served as the basis for evaluating the freshness of the beef samples. Firstly, kernel principal component analysis (KPCA) was used to reduce the dimensionality of the olfactory–visual feature information of beef. Subsequently, prediction models for the electronic nose, machine vision, and data fusion were established using the backpropagation neural network (BPNN), support vector machine (SVM), convolutional neural network (CNN), and slime mold algorithm–convolutional neural network–bidirectional gated recurrent unit with attention (SMA–CNN–BiGRU-Attention) methods, and the model results were compared. The results indicated that the prediction performance of the data fusion model was significantly better than that of a single electronic nose or machine vision model. Among them, the SMA–CNN–BiGRU-Attention data-fusion model exhibited optimal stability, 100% accuracy in classifying the three freshness levels of beef, and 96.4% accuracy in classifying beef stored under chilled conditions for 12 days. This study demonstrates the feasibility of artificial olfaction–vision technology for detecting the freshness of meat products. The SMA–CNN–BiGRU-Attention model, based on data fusion, achieves high accuracy, offering a potential solution for detecting beef freshness.

, authors=Bo ZHOU*, Zewei WU, Jun WU, Xuzhe ZHAO, authorsList=Bo ZHOU, Zewei WU, Jun WU, Xuzhe ZHAO, authorCompany=null, correspAuthors=Bo ZHOU, authorNote=null, correspAuthorsNote=null, copyrightStatement=Copyright © 2026 Science and Technology of Food Industry. All rights reserved., copyrightOwner=null, extLink=null, articleAbsUrl=null, sourceXml=null, magXml=null, pdfUrl=null, pdf=null, pdfFileSize=null, pdfExtLink=null, richHtmlUrl=null, mobilePdfUrl=null, reviewReport=null, pdfFirstPage=null, abstractGraph=null, abstractGraphContent=null, abstractVideo=null, citation=null, cebUrl=null, magXmlContent=null, mapNumber=null, fund=null), CN=ArticleExt(id=1320881302237196897, articleId=1320881294813278794, tenantId=1146029695717560320, journalId=1260987677001138203, language=CN, title=采用人工嗅视觉和深度学习法检测牛肉的新鲜度, columnId=1261343861805334715, journalTitle=食品工业科技, columnName=包装与机械, runingTitle=null, highlight=null, articleAbstract=

本研究采用人工嗅视觉设备结合多数据融合和深度学习技术实现冷藏牛肉新鲜度准确的预测，为肉制品新鲜度快速检测提供理论依据与方法支持。研究通过自制电子鼻和机器视觉系统采集牛肉样本气味和图像信息，利用半微量定氮法和酸度计测定样本中挥发性盐基氮（Total Volatile Basic Nitrogen，TVB-N）的含量和pH作为牛肉样本新鲜度的依据。首先对牛肉嗅视觉特征信息进行核主成分（Kernel Principal Component Analysis，KPCA）降维处理，然后采用反向传播神经网络（Back Propagation Neural Network，BPNN）、支持向量机（Support Vector Machine，SVM）、卷积神经网络（Convolutional Neural Networks，CNN）和黏菌算法-卷积神经网络-双向门控循环单元-注意力机制（SMA-CNN-BiGRU-Attention）方法分别建立电子鼻、机器视觉和数据融合的预测模型，并对比模型结果。结果表明，数据融合模型的预测性能显著优于单一电子鼻或机器视觉模型，其中SMA-CNN-BiGRU-Attention数据融合模型表现出最优的稳定性与准确性，其在牛肉的3种新鲜度分类中准确率达100%，在12 d冷藏分类中准确率为96.4%。研究证明了人工嗅视觉技术在肉制品新鲜度检测的可行性，采用数据融合的SMA-CNN-BiGRU-Attention模型具有较高的准确性，研究为牛肉新鲜度检测提供了极具潜力的解决方案。

, authors=周博*, 吴泽玮, 吴俊, 赵旭哲, authorsList=周博, 吴泽玮, 吴俊, 赵旭哲, authorCompany=null, correspAuthors=周博, authorNote=

周博（1971−），男，博士，副教授，研究方向：电子鼻技术，E-mail：

, correspAuthorsNote=
周博（1971−），男，博士，副教授，研究方向：电子鼻技术，E-mail：。
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周博（1971−），男，博士，副教授，研究方向：电子鼻技术，E-mail：

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周博（1971−），男，博士，副教授，研究方向：电子鼻技术，E-mail：

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Trends in Food Science & Technology, 2021, 118: 36−44., articleTitle=null, refAbstract=null)], funds=null, companyList=[AuthorCompany(id=1320881664704762434, tenantId=1146029695717560320, journalId=1260987677001138203, articleId=1320881294813278794, xref=null, ext=[AuthorCompanyExt(id=1320881664717345347, tenantId=1146029695717560320, journalId=1260987677001138203, articleId=1320881294813278794, companyId=1320881664704762434, language=EN, country=null, province=null, city=null, postcode=null, companyName=null, departmentName=null, remark=College of Mechanical Engineering, Yancheng Institute of Technology, Yancheng 224051, China), AuthorCompanyExt(id=1320881664725733956, tenantId=1146029695717560320, journalId=1260987677001138203, articleId=1320881294813278794, companyId=1320881664704762434, language=CN, country=null, province=null, city=null, postcode=null, companyName=null, departmentName=null, remark=盐城工学院机械工程学院，江苏盐城 224051)])], figs=[ArticleFig(id=1320881666726416997, tenantId=1146029695717560320, journalId=1260987677001138203, articleId=1320881294813278794, language=EN, label=Fig.1, caption=Flow chart of the research, figureFileSmall=jT9Hwtxaepl18QTpnxWt1w==, figureFileBig=8N4NYaUEZONLStRDmE6WPg==, tableContent=null), ArticleFig(id=1320881666801914470, tenantId=1146029695717560320, journalId=1260987677001138203, articleId=1320881294813278794, language=CN, label=图1, caption=实验设计流程图, figureFileSmall=jT9Hwtxaepl18QTpnxWt1w==, figureFileBig=8N4NYaUEZONLStRDmE6WPg==, tableContent=null), ArticleFig(id=1320881666910966375, tenantId=1146029695717560320, journalId=1260987677001138203, articleId=1320881294813278794, language=EN, label=Fig.2, caption=Processing of beef sample images, figureFileSmall=xU963dcG50PGYjY8xPeH5Q==, figureFileBig=jME2JIBgjeODCQZ8Hzk9eQ==, tableContent=null), ArticleFig(id=1320881666986463848, tenantId=1146029695717560320, journalId=1260987677001138203, articleId=1320881294813278794, language=CN, label=图2, caption=牛肉样本图片处理, figureFileSmall=xU963dcG50PGYjY8xPeH5Q==, figureFileBig=jME2JIBgjeODCQZ8Hzk9eQ==, tableContent=null), ArticleFig(id=1320881667061961321, tenantId=1146029695717560320, journalId=1260987677001138203, articleId=1320881294813278794, language=EN, label=Fig.3, caption=Changes of TVB-N and pH of chilled beef over time, figureFileSmall=RIcf93RsUrFLRE2qTFldWQ==, figureFileBig=4vcS0U5JoFgvv6KdAICkmQ==, tableContent=null), ArticleFig(id=1320881667162624618, tenantId=1146029695717560320, journalId=1260987677001138203, articleId=1320881294813278794, language=CN, label=图3, caption=冰鲜牛肉TVB-N和pH随时间的变化, figureFileSmall=RIcf93RsUrFLRE2qTFldWQ==, figureFileBig=4vcS0U5JoFgvv6KdAICkmQ==, tableContent=null), ArticleFig(id=1320881667250705003, tenantId=1146029695717560320, journalId=1260987677001138203, articleId=1320881294813278794, language=EN, label=Fig.4, caption=Radar chart of electronic nose sensors, figureFileSmall=SVed4Y2TwovFUJUxE241dg==, figureFileBig=EsBH38BaYk2jZ9jF/xUjUg==, tableContent=null), ArticleFig(id=1320881667322008172, tenantId=1146029695717560320, journalId=1260987677001138203, articleId=1320881294813278794, language=CN, label=图4, caption=电子鼻传感器的雷达图, figureFileSmall=SVed4Y2TwovFUJUxE241dg==, figureFileBig=EsBH38BaYk2jZ9jF/xUjUg==, tableContent=null), ArticleFig(id=1320881667389117037, tenantId=1146029695717560320, journalId=1260987677001138203, articleId=1320881294813278794, language=EN, label=Fig.5, caption=Histogram of the color space of beef images with different freshness levels, figureFileSmall=Qy/5qQOUS5vkS7agPc9nYQ==, figureFileBig=6aR5cHlRS5Vm/cXtmbf1+Q==, tableContent=null), ArticleFig(id=1320881667464614510, tenantId=1146029695717560320, journalId=1260987677001138203, articleId=1320881294813278794, language=CN, label=图5, caption=不同新鲜度牛肉图像色彩空间的直方图

注：（a1）~（a3）：RGB直方图；（b1）~（b3）：HSV直方图； （c1）~（c3）：Lab直方图。

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Electronic nose sensing performance parameters

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编号型号灵敏度特性检测范围（mL/m3）
S1MQ-136硫化氢、一氧化碳1~200
S2MQ-137三甲胺、乙醇胺5~500
S3MQ-138甲苯、丙酮、乙醇、氢气5~500
S4TGS2600氢气、异丁烷1~100
S5TGS2603甲基硫醇、三甲胺0.1~30
S6TGS813甲烷、丙烷、丁烷500~1000
S7TGS822酒精、苯、异丁烷、丙酮50~5000
S8MQ-3B酒精25~500
), ArticleFig(id=1320881668148286072, tenantId=1146029695717560320, journalId=1260987677001138203, articleId=1320881294813278794, language=CN, label=表1, caption=

电子鼻传感器性能参数

, figureFileSmall=null, figureFileBig=null, tableContent=
编号型号灵敏度特性检测范围（mL/m3）
S1MQ-136硫化氢、一氧化碳1~200
S2MQ-137三甲胺、乙醇胺5~500
S3MQ-138甲苯、丙酮、乙醇、氢气5~500
S4TGS2600氢气、异丁烷1~100
S5TGS2603甲基硫醇、三甲胺0.1~30
S6TGS813甲烷、丙烷、丁烷500~1000
S7TGS822酒精、苯、异丁烷、丙酮50~5000
S8MQ-3B酒精25~500
), ArticleFig(id=1320881668207006329, tenantId=1146029695717560320, journalId=1260987677001138203, articleId=1320881294813278794, language=EN, label=Table 2, caption=

Division of sample data into training set and test set for beef with different freshness

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3种新鲜度12 d冷藏
类型训练集测试集时间训练集测试集
新鲜122531 d2510
2 d2510
3 d2510
4 d2510
5 d2510
次新鲜72316 d2211
7 d2510
8 d2510
腐败92389 d2510
10 d2510
11 d199
12 d2210
), ArticleFig(id=1320881668437693050, tenantId=1146029695717560320, journalId=1260987677001138203, articleId=1320881294813278794, language=CN, label=表2, caption=

不同新鲜度牛肉的训练集与测试集样本数据划分

, figureFileSmall=null, figureFileBig=null, tableContent=
3种新鲜度12 d冷藏
类型训练集测试集时间训练集测试集
新鲜122531 d2510
2 d2510
3 d2510
4 d2510
5 d2510
次新鲜72316 d2211
7 d2510
8 d2510
腐败92389 d2510
10 d2510
11 d199
12 d2210
), ArticleFig(id=1320881668513190523, tenantId=1146029695717560320, journalId=1260987677001138203, articleId=1320881294813278794, language=EN, label=Table 3, caption=

Classification results of three freshness levels for beef samples

, figureFileSmall=null, figureFileBig=null, tableContent=
方法BPNNSVMCNNSMA-CNN-BiGRU-Attention
训练集测试集训练集测试集训练集测试集训练集测试集
电子鼻原始数据92.3%90.16%90.9%88.52%93%91.8%97.9%95.9%
KPCA降维后95.8%91.8%94.4%90.16%96.5%93.4%100%98.36%
机器视觉原始数据90.2%86.07%88.11%86.07%90.9%89.34%97.2%92.62%
KPCA降维后93.36%88.52%92.66%86.88%94.76%91.8%99.3%94.26%
数据融合原始数据96.15%93.44%97.55%94.26%100%98.36%100%100%
KPCA降维后97.9%95.9%98.6%96.72%100%98.36%100100%
), ArticleFig(id=1320881668622242428, tenantId=1146029695717560320, journalId=1260987677001138203, articleId=1320881294813278794, language=CN, label=表3, caption=

牛肉样本的3种新鲜度分类结果

, figureFileSmall=null, figureFileBig=null, tableContent=
方法BPNNSVMCNNSMA-CNN-BiGRU-Attention
训练集测试集训练集测试集训练集测试集训练集测试集
电子鼻原始数据92.3%90.16%90.9%88.52%93%91.8%97.9%95.9%
KPCA降维后95.8%91.8%94.4%90.16%96.5%93.4%100%98.36%
机器视觉原始数据90.2%86.07%88.11%86.07%90.9%89.34%97.2%92.62%
KPCA降维后93.36%88.52%92.66%86.88%94.76%91.8%99.3%94.26%
数据融合原始数据96.15%93.44%97.55%94.26%100%98.36%100%100%
KPCA降维后97.9%95.9%98.6%96.72%100%98.36%100100%
), ArticleFig(id=1320881668718711421, tenantId=1146029695717560320, journalId=1260987677001138203, articleId=1320881294813278794, language=EN, label=Table 4, caption=

Classification results of beef samples under 12 d refrigeration

, figureFileSmall=null, figureFileBig=null, tableContent=
方法BPNNSVMCNNSMA-CNN-BiGRU-Attention
训练集测试集训练集测试集训练集测试集训练集测试集
电子鼻原始数据85.66%83.61%86.71%81.97%96.85%87.7%100%91.8%
KPCA降维后88.11%85.25%87.4%83.61%97.55%89.3%100%94.26%
机器视觉原始数据83.21%73.77%82.17%72.13%93.36%81.15%97.2%86.89%
KPCA降维后84.62%76.23%83.92%77.87%96.15%84.43%97.9%88.52%
数据融合原始数据90.9%86.07%91.61%84.43%97.9%91.8%100%96.72%
KPCA降维后92.66%90.16%93.71%88.52%100%94.26%100%98.36%
), ArticleFig(id=1320881668806791806, tenantId=1146029695717560320, journalId=1260987677001138203, articleId=1320881294813278794, language=CN, label=表4, caption=

牛肉样本的12 d冷藏分类结果

, figureFileSmall=null, figureFileBig=null, tableContent=
方法BPNNSVMCNNSMA-CNN-BiGRU-Attention
训练集测试集训练集测试集训练集测试集训练集测试集
电子鼻原始数据85.66%83.61%86.71%81.97%96.85%87.7%100%91.8%
KPCA降维后88.11%85.25%87.4%83.61%97.55%89.3%100%94.26%
机器视觉原始数据83.21%73.77%82.17%72.13%93.36%81.15%97.2%86.89%
KPCA降维后84.62%76.23%83.92%77.87%96.15%84.43%97.9%88.52%
数据融合原始数据90.9%86.07%91.61%84.43%97.9%91.8%100%96.72%
KPCA降维后92.66%90.16%93.71%88.52%100%94.26%100%98.36%
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采用人工嗅视觉和深度学习法检测牛肉的新鲜度
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周博 * , 吴泽玮 , 吴俊 , 赵旭哲
食品工业科技 | 包装与机械 2026,47(16): 302-312
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食品工业科技 |包装与机械 2026 , 47 (16) : 302 -312
采用人工嗅视觉和深度学习法检测牛肉的新鲜度
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周博* , 吴泽玮, 吴俊, 赵旭哲
作者信息
  • 盐城工学院机械工程学院，江苏盐城 224051
通讯作者:
周博（1971−），男，博士，副教授，研究方向：电子鼻技术，E-mail：。
作者简介:

周博（1971−），男，博士，副教授，研究方向：电子鼻技术，E-mail：

Beef Freshness Detection Using Artificial Olfaction–Vision and Deep Learning
Bo ZHOU* , Zewei WU, Jun WU, Xuzhe ZHAO
Affiliations
  • College of Mechanical Engineering, Yancheng Institute of Technology, Yancheng 224051, China
出版时间: 2026-08-15 doi: 10.13386/j.issn1002-0306.2025070081
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摘要
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本研究采用人工嗅视觉设备结合多数据融合和深度学习技术实现冷藏牛肉新鲜度准确的预测，为肉制品新鲜度快速检测提供理论依据与方法支持。研究通过自制电子鼻和机器视觉系统采集牛肉样本气味和图像信息，利用半微量定氮法和酸度计测定样本中挥发性盐基氮（Total Volatile Basic Nitrogen，TVB-N）的含量和pH作为牛肉样本新鲜度的依据。首先对牛肉嗅视觉特征信息进行核主成分（Kernel Principal Component Analysis，KPCA）降维处理，然后采用反向传播神经网络（Back Propagation Neural Network，BPNN）、支持向量机（Support Vector Machine，SVM）、卷积神经网络（Convolutional Neural Networks，CNN）和黏菌算法-卷积神经网络-双向门控循环单元-注意力机制（SMA-CNN-BiGRU-Attention）方法分别建立电子鼻、机器视觉和数据融合的预测模型，并对比模型结果。结果表明，数据融合模型的预测性能显著优于单一电子鼻或机器视觉模型，其中SMA-CNN-BiGRU-Attention数据融合模型表现出最优的稳定性与准确性，其在牛肉的3种新鲜度分类中准确率达100%，在12 d冷藏分类中准确率为96.4%。研究证明了人工嗅视觉技术在肉制品新鲜度检测的可行性，采用数据融合的SMA-CNN-BiGRU-Attention模型具有较高的准确性，研究为牛肉新鲜度检测提供了极具潜力的解决方案。

牛肉  /  新鲜度  /  电子鼻  /  机器视觉  /  深度学习  /  数据融合

This study employed artificial olfaction–vision equipment combined with multidata fusion and deep learning technologies to accurately predict the freshness of chilled beef, providing a theoretical basis and methodological support for the rapid detection of meat product freshness. In this study, a self-developed electronic nose and machine vision system was used to collect odor and image information from beef samples. The semi-micro Kjeldahl method and an acid–base meter were used to determine the total volatile basic nitrogen content and pH of the samples, which served as the basis for evaluating the freshness of the beef samples. Firstly, kernel principal component analysis (KPCA) was used to reduce the dimensionality of the olfactory–visual feature information of beef. Subsequently, prediction models for the electronic nose, machine vision, and data fusion were established using the backpropagation neural network (BPNN), support vector machine (SVM), convolutional neural network (CNN), and slime mold algorithm–convolutional neural network–bidirectional gated recurrent unit with attention (SMA–CNN–BiGRU-Attention) methods, and the model results were compared. The results indicated that the prediction performance of the data fusion model was significantly better than that of a single electronic nose or machine vision model. Among them, the SMA–CNN–BiGRU-Attention data-fusion model exhibited optimal stability, 100% accuracy in classifying the three freshness levels of beef, and 96.4% accuracy in classifying beef stored under chilled conditions for 12 days. This study demonstrates the feasibility of artificial olfaction–vision technology for detecting the freshness of meat products. The SMA–CNN–BiGRU-Attention model, based on data fusion, achieves high accuracy, offering a potential solution for detecting beef freshness.

beef  /  freshness  /  electronic nose  /  machine vision  /  deep learning  /  data fusion
周博, 吴泽玮, 吴俊, 赵旭哲. 采用人工嗅视觉和深度学习法检测牛肉的新鲜度. 食品工业科技, 2026 , 47 (16) : 302 -312 . DOI: 10.13386/j.issn1002-0306.2025070081
Bo ZHOU, Zewei WU, Jun WU, Xuzhe ZHAO. Beef Freshness Detection Using Artificial Olfaction–Vision and Deep Learning[J]. Science and Technology of Food Industry, 2026 , 47 (16) : 302 -312 . DOI: 10.13386/j.issn1002-0306.2025070081
正文
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牛肉富含优质蛋白质和人类必需的氨基酸，其氨基酸组成比猪肉更接近人体需要，能提高机体抗病能力[1]。随着居民收入水平稳步提升，大众对健康饮食的需求与日俱增，高品质的牛肉受到越来越多消费者的青睐。
近年来我国牛肉的需求量逐年增加，根据中国国家统计局2024年发布的《中华人民共和国2023年国民经济和社会发展统计公报》，2023年全国牛肉产量为753万吨，比上年增长4.8%[2]。然而，新鲜牛肉因其高蛋白、高水分的特性，在贮藏和运输过程中容易受到微生物及酶的作用，导致脂肪、蛋白质和碳水化合物发生分解，进而引发变质，影响其品质稳定性[3]。因此，建立牛肉新鲜度的检测，并针对不同新鲜度等级的牛肉采取差异化处理措施，具有重要的现实意义[4]。
牛肉新鲜度传统的检测方法包括感官评价、理化指标检测和微生物菌落检测等[5]。感官评估是判断牛肉新鲜状态及腐败程度的常用检测手段，但是其具有一定的主观性，很难得到统一标准。理化指标检测能够精确测量牛肉腐败成分的浓度和物理特性的变化程度，但检测过程比较繁琐、耗时较长并且需要破坏样本。微生物菌落检测可以直接反映导致牛肉腐败的微生物负荷，但其检测周期较长、无法提供即时结果并需要经过严格培训的微生物检测人员。所以传统检测方法已经不能满足牛肉产业的需求。
目前肉制品检测专业的设备包括气相色谱-质谱联用仪[6]、核酸提取仪[7]、PCR仪、质构分析仪[8]等。但是这些设备价格昂贵，并且检测周期长，需要专业人员进行操作。电子鼻是一种通过模拟生物嗅觉系统，利用传感器阵列和模式识别技术，对气体或挥发性物质进行定性和定量分析的智能检测仪器[9−11]。电子鼻具有体积小、不破坏样本、操作简单、成本低等优势可用于肉类新鲜度的检测。机器视觉是通过光学设备（如摄像头、激光器）采集图像，结合算法对图像进行处理、分析和理解，捕捉肉类表面的颜色特征与形态变化，实现对新鲜度的判别。Chotimah等[12]使用电子鼻检测技术判断了鸡肉在室温和冷藏下的新鲜度。Chen等[13]使用实验室自制电子鼻检测和预测猪、牛肉和羊肉的新鲜度。Song等[14]提出了一种基于计算机视觉技术的猪肉不同部位和新鲜度的自动检测方法。近年来，多数据融合作为肉类新鲜度分类领域的创新方法得到更多重视，Weng等[15]融合电子鼻、机器视觉和人工触觉技术对猪肉和鸡肉新鲜度进行了检测，结果表明融合技术较单一技术显著提升了各类生鲜肉制品的检测精度。李玉花等[16]采用电子鼻和机器视觉技术融合检测装置对不同新鲜度鸡肉样本进行检测，通过整合多元检测数据能够极大程度提高分类的准确性。电子鼻和机器视觉检测技术需要根据检测对象选择合适的模式识别算法来提高检测的准确性。目前，反向传播神经网络（Back Propagation Neural Network，BPNN）、支持向量机（Support Vector Machine，SVM）、卷积神经网络（Convolutional Neural Networks，CNN）和循环神经网络（Recursive Neural Network，RNN）等是肉制品检测中常用的模型分类方法，随着算法研究的深入和算法融合的不断发展，深度学习融合方法得到了更多的研究。陈远涛等[17]将CNN与电子鼻系统相结合，能实现猪肉、鸡肉与鲳鱼混合样本新鲜度的有效判断。Chang等[18]提出残余气体注意力网络（RGA-Net），并将其与电子鼻系统结合用于猪肉品质识别，与其他分类方法相比该网络具有最优的分类性能。
本研究以牛肉为研究对象，通过人工嗅视觉系统提取不同贮藏时间下牛肉挥发性气体与表面特征信息，并结合深度学习技术实现对其新鲜度准确地判别。研究为牛肉品质无损检测与新鲜度评估提供理论依据与方法支持。
1　材料与方法
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1.1　材料与仪器
牛肉样本　本实验所用牛肉取材于当日屠宰的黄牛后腿部位（15 kg左右），表面光滑呈鲜红色、色泽均匀，样本购自盐城市鲜肉市场，并于屠宰后2 h内冰鲜运送至实验室；碳酸氢钠溶液　福建安研有限公司；硼酸、溴甲酚绿甲基红指示溶液混合　天津市科密欧化学试剂有限公司；盐酸　莱阳市康德化有限公司；以上试剂均为分析纯。
JTS-LW-1.5K天平　昆山艾斯博格电子科技有限公司；PHS-3C数显酸度计　杭州齐威仪器有限公司；科保新52 L冷藏箱　浙江科保冷链科技有限公司；MQ-136、MQ-138、MQ-137、MQ-3B传感器　广州市睿博翊电子科技；TGS2600、TGS2603、TGS813、TGS822传感器　日本费加罗公司；数据采集卡：DAQ122　福州市凌睿智捷电子有限公司；机器视觉系统由摄像头、LED光源、暗箱及黑色亚克力板构成　采用海康工业相机MV-CS060-10GM600W像素作为图像采集设备，在暗箱顶部对称布置两颗LED光源，通过黑色亚克力板的漫反射作用实现均匀打光，确保图像采集区域光照条件的一致性。
1.2　实验方法
1.2.1　实验设计
本研究的流程见图1，牛肉样本先剔除杂质并修剪规整，切割为统一规格后进行无菌分装，再转移至4 ℃冰箱中避光贮藏。贮藏期间，每日固定时间记录冰箱实际温度，同时将牛肉样本与其他试剂分区域独立存放；于冷藏1~12 d取样检测。通过电子鼻系统采集牛肉样本的气体信息；机器视觉装置采集样本的图像，将RGB图像转换至HSV与Lab颜色空间；利用半微量定氮法测量样本的TVB-N含量；PHS-3C数显酸碱度计检测样本的pH；检测后的数据经特征提取和特征融合，然后经KPCA法降维和深度学习模型实现牛肉新鲜度的预测。电子鼻和机器视觉为无损检测方式，采用同一批35个重复样本，每个样本每天检测一次，样本检测完毕继续冷藏用于后期检测；半微量定氮法需要破坏牛肉样本，每天检测3个重复样本，共需36个样本；PHS-3C数显酸度计检测pH会污染样本，每天设置3个重复，共需36个样本。
1.2.2　样本制备
牛肉采购后迅速运送至实验室，于环境温度约25 ℃的无菌工作台上开展相关操作。将新鲜牛肉样本置于超净工作台上，使用无菌刀，将其切割为尺寸均一的块状样本，确保每块质量为（30±1）g。切割后的样本立即被分装，每个食品级保鲜盒中放置一块独立样本，共准备107个样本，其中35个样本按1~35的顺序编号用于电子鼻和机器视觉检测，其余72个样本用于TVB-N含量与pH的检测。将所有样本迅速转移至4 ℃医用冷藏箱中进行低温保存。本实验在电子鼻、机器视觉的样本提取过程中，严格遵循无菌操作规范。
1.2.3　电子鼻系统与气体采集
电子鼻硬件装置由四部分组成：气体采集模块、传感器检测模块、数据采集卡、上位机系统。气体采集模块通过待测环境与导管形成闭环通路，利用气泵抽取牛肉挥发出的气体。传感器检测模块采用阵列集成设计，内置4只TGS系列传感器与4只MQ系列传感器，两类传感器对醛类、胺类、醇类等挥发性有机物具备差异化响应特性。数据采集卡可将传感器输出的微弱电信号进行放大、滤波及模数转换。上位机系统通过USB接口与采集板通信，通过凌智数据采集系统V3.2.3实现信号实时显示。各类传感器相应特征如表1所示。
牛肉样本检测前先将电子鼻系统置于实验平台上预热30 min，以确保传感器阵列达到稳定工作状态。每日14:00点开始检测。从4 ℃冷藏环境中取出待测样本，迅速转移至温度恒定为（4±0.5）℃的冷藏箱内，通过导管连接气泵与电子鼻气室开始检测；传感器的采样频率为100 Hz，牛肉样本气体信号持续采集90 s，每个样本检测完毕后，气泵将洁净空气注入气室，持续60 s，确保清理残余气体。完成清洗后，将已测样本暂存于保鲜盒并放回4 ℃冰箱，按序进行下一样本检测。电子鼻传感器响应在40 s后逐渐稳定，提取各传感器40~90 s的平均值作为每个检测数据的特征用于后续研究，则牛肉样本12 d检测得到电子鼻的特征数据集为420×8的矩阵（420个检测数据，8个传感器特征值）。
1.2.4　图像采集
电子鼻检测完成后进行牛肉样本的图像采集，将牛肉放在黑色亚克力板上，通过定制支架固定于暗箱正上方（15±1 cm），确保镜头与待测样本平面垂直。暗箱顶部呈对称式安装两颗LED光源，选用相机作为图像采集终端，采集完成后将图片保存。
1.2.5　挥发性盐基氮（TVB-N）含量、pH测定
TVB-N含量测定采用半微量定氮法，每日18:00开始检测。取待测样本，将其切碎并充分搅拌均匀，称取10.00 g置于锥形瓶中，加入100 mL蒸馏水，采用机械振荡器振荡30 min，使样本充分浸渍。随后，过滤收集滤液备用。准确量取50 mL上述滤液转移至蒸馏瓶中，缓慢加入10 mL饱和碳酸氢钠溶液；将10 mL 2%硼酸溶液加入锥形瓶中作为吸收液，并滴加5~6滴溴甲酚绿-甲基红混合指示液，启动蒸馏装置，当蒸馏烧瓶内液体剩余约10 mL时，停止加热。取下接收锥形瓶，用盐酸滴定溶液进行滴定，溶液颜色由绿色逐渐变为微红色即为终点，记录消耗盐酸标准溶液的体积。计算公式如下：
$\rm TVB\text{-}N(mg/100\;g)=\frac{\left({V}_{1}\times C\right)}{V}\times \left(\frac{14}{1000}\right)\times 100 $
其中，V1为滴定体积，mL；C为标准溶液浓度，mol/L；V为样本体积，mL。
pH采用PHS-3C数显酸度计进行检测，每日20：00开始检测，样本制备与TVB-N含量测定相同，检测前先对PHS-3C数显酸度计进行校准，校准完成后将电极放入检测液中进行读数。
1.2.6　嗅视觉数据处理方法
1.2.6.1　图像处理和特征提取
研究围绕牛肉的颜色与形态两大维度展开特征提取工作，在颜色特征方面，重点分析R、G、B、H、S、V、L、a*、b*等颜色分量的均值[19]，在形态特征层面，分析面积、圆度及纵横比等指标。首先将采集到的牛肉RGB图像传输至计算机，随后利用Matlab软件进行处理与分析。图像处理过程首先将牛肉全景图像转换为灰度图像为图2（b），接着采用Otsu算法精准计算最优阈值，进而将灰度图像转化为二值图像为图2（c）。在特征提取环节，基于形态特征学原理，针对面积、圆度、纵横比关键形态参数开展定量分析[20−21]。将RGB图像分别转换为HSV色彩空间与La*b*色彩空间，对应得到图2（d）、图2（e）、图2（f）和图2（g）、图2（h）、图2（i），并计算各色彩分量相应的平均值[22]。
1.2.6.2　核主成分分析
核主成分分析（Kernel Principal Component Analysis，KPCA）是主成分分析（Principal Component Analysis，PCA）的非线性扩展方法，将线性不可分的高维数据映射到更高维的特征空间，使数据在新空间中具备线性可分性，再在该空间中执行传统PCA，最终实现对非线性数据的降维。在本研究中，针对电子鼻与机器视觉采集的牛肉新鲜度检测数据，KPCA通过核技巧有效捕捉两类数据中与新鲜度强相关的非线性特征，在保留核心判别信息的同时，大幅简化了高维数据结构。
1.2.6.3　数据融合
数据融合是指通过整合电子鼻和机器视觉两种技术的互补信息解决单一技术检测的局限性，最终实现更准确、鲁棒性强的新鲜度判定。数据融合方式共有3种，数据层融合、特征层融合和决策层融合，本研究将电子鼻采集的气味特征和机器视觉测量的图像特征作为原始数据，本研究选择特征层融合进行后续分析。电子鼻特征为8个传感器响应值的平均值；机器视觉特征包含R、G、B、H、S、V、L、a*、b*均值与高光区域占比，共计10个特征指标。
由于各提取特征存在量纲差异，在特征融合前先进行标准化处理，然后通过降维方法提取关键特征。
1.2.6.4　支持向量机（SVM）
SVM是一种有监督的机器学习算法，常用于分类和回归分析。SVM模型选择线性核函数构建分类器，惩罚参数C设为0.1。
1.2.6.5　反向传播神经网络（BPNN）
BPNN是一种基于误差反向传播算法的多层前馈神经网络，是使用最广泛算法之一。BPNN模型采用三层架构（输入层-隐含层-输出层）。对隐含层神经元数量（4~16个）进行参数调优，最终确定7个神经元为最优配置。模型采用Adam优化器，学习率设为0.01，激活函数选用Sigmoid以适配分类任务。
1.2.6.6　卷积神经网络（CNN）
CNN由卷积层、池化层、全连接层组成[23−24]，因其强大的特征提取能力，已广泛应用于多个领域。CNN模型的输入层接收多维时序传感器数据。两个卷积层，卷积层1：32个2×1卷积核，卷积层2：64个2×1卷积核。池化层大小为2×1，步长[2,1]。最后全连接层连接所有输入神经元与输出神经元的层，将前面各层提取的局部特征整合为全局特征，并映射到目标类别空间。训练过程中，采用Adam优化器（初始学习率0.01，L2正则化系数1e-4），设置最大训练轮次为500，每轮启用Shuffle机制增强数据随机性。
1.2.6.7　双向门控循环单元（BiGRU）
BiGRU由两个方向的GRU单元组成，一个处理正向序列数据，另一个处理反向序列数据[25]。正向GRU从序列的开始到结束处理数据，反向GRU从序列的结束到开始处理数据。BiGRU通过双向结构同时捕获序列的前后文信息，解决单向RNN的长期依赖局限。SMA-CNN-BiGRU-Attention模型，采用CNN对原始传感器数据进行特征挖掘，通过2层卷积-池化结构自动提取时序特征中的局部关联模式，形成高维特征表示。将CNN提取的特征序列输入BiGRU，同时捕获正向和反向时间依赖关系，构建CNN-BiGRU模型，引入SMA对CNN-BiGRU的关键超参数进行全局优化搜索，包括卷积核数量、GRU隐藏层维度、学习率等，通过模拟黏菌觅食行为寻找最优参数组合。最后引入Attention提升模型对微弱信号的敏感度。
1.3　数据处理
文中数据的显著性采用SPSS 26.0进行分析，P<0.05为差异显著；采用Orgin2021绘制雷达图和线形图；运用Matlab2023b对电子鼻与机器视觉的检测结果进行特征提取及降维处理，编程建立神经网络模型。
2　结果与分析
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2.1　牛肉在冷藏期理化指标变化
TVB-N（挥发性盐基氮）是衡量牛肉新鲜度的关键指标。依据GB 2707-2016《食品安全国家标准鲜（冻）畜、禽产品》[26]新鲜肉TVB-N含量的检测限为≤15（mg/100 g），根据陆钟超等[27]研究，将TVB-N含量小于15 mg/100 g时牛肉被认定为新鲜肉，TVB-N含量为15~25 mg/100 g为次新鲜肉，TVB-N含量大于25 mg/100 g为腐败肉。pH根据张亚芳等[28]的方法，新鲜肉：pH5.8~6.2；次新鲜肉：pH6.3~6.6；变质肉：pH6.7以上。
TVB-N含量测量结果如图3（a）所示，随着肉品贮藏时间延长，TVB-N含量呈现一直上升的趋势，当贮藏至第6 d，TVB-N含量达到约16 mg/100 g，随着贮藏时间进一步延长至第9 d，TVB-N含量攀升至约24.5 mg/100 g。牛肉TVB-N含量随冷藏时间的延长而上升主要是由于肌肉组织中的蛋白质在自身内源酶作用下，逐渐分解为多肽、氨基酸等小分子物质，这些含氮化合物进一步降解产生氨、胺类等碱性含氮物质[29−30]。
pH检测结果如图3（b）所示。牛肉pH随贮藏时间呈现规律性波动。在贮藏第2 d，pH降至最低，这可能是因为糖原开始通过无氧酵解转化为乳酸[31]。随着乳酸在肌肉中不断积累，肉的酸性环境逐渐增强。从贮藏第2 d开始，pH进入持续上升阶段，且贮藏时间越长，上升趋势越明显。
根据牛肉样本TVB-N含量和pH结果并参考GB 2707-2016《食品安全国家标准鲜（冻）畜、禽产品》以及陆钟超等[27]和张亚芳等[28]的研究，将牛肉样本按3种新鲜度和12 d冷藏两种分类形式划分，其中3种新鲜度分类将牛肉样本划分为新鲜（1~5 d样本）、次新鲜（6~8 d样本）和腐败（9~12 d样本）3个类别，12 d冷藏分类以牛肉冷藏天数作为分类依据，对12 d冷藏期内的样本数据进行划分，使不同贮藏天数的样本数据各自成为独立类别。
牛肉样本累计获得420个电子鼻检测数据，由于在采集过程受到环境影响，会产生一些异常数据，需要对数据进行预处理。马氏距离（Mahalanobis distance）用于衡量一个样本点与某一类样本集平均值之间的距离，可用于异常数据的筛选[32]。本研究采用马氏距离方法对电子鼻测试数据进行筛选，共剔除12个异常数据，其中10个来自腐败牛肉样本测试数据，2个属于次新鲜牛肉样本测试数据，保留408个有效测试数据。3种新鲜度和12 d冷藏分类样本均按照大致7:3的比例随机划分训练集与测试集用于后期的模式识别，牛肉样本测试数据的具体分布情况见表2。
2.2　电子鼻采集数据分析
图4是牛肉样本在不同时段的电子鼻传感器雷达图。图4表明，随着时间推移，大多数气体响应值呈现持续上升趋势，其中TG2603、MQ-3B响应值最大，说明当前检测环境中，它们的目标敏感物质含量相对更高。MQ-3B、TGS813两个传感器变化最为明显，分别提升了50.57%和156.33%，说明两个传感器所敏感的气体成分在样本状态改变时浓度波动更显著。这可能是因为牛肉在贮藏过程中，随着自身酶解、微生物代谢等作用的推进，不断生成含氮化合物、含硫化合物以及乙醇[33]。
2.3　机器视觉图像分析
2.3.1　牛肉样本颜色图分析
牛肉初始呈现鲜红色，源于脱氧肌红蛋白与氧气结合形成氧合肌红蛋白。此时新鲜牛肉表面光滑湿润，肉质富有弹性。随着贮存时间的延长，牛肉的红色会逐渐褪去、色泽变浅，其边缘部位也开始呈现灰色，新鲜度随之逐步降低。这一变化可能是由微生物繁殖及肌红蛋白过度氧化共同引发[34]，第6 d时牛肉表面会附着少量黏液，肉质松弛，并释放出少量具有刺激性气味的气体。当牛肉颜色完全转为褐色时，已进入腐败阶段，表面形成大量黏稠分泌物，质地黏腻，手指按压后凹陷无法恢复，同时释放出强烈刺激性气体。可能因为微生物大量繁殖与牛肉自身成分蛋白质、脂肪、色素的分解氧化共同作用的结果。
图5为3种新鲜度的牛肉图像在RGB、HSV、Lab颜色空间的特征直方图，从RGB颜色直方图分析（图5（a1）、图5（a2）、图5（a3））可见，新鲜牛肉的R通道呈现显著峰值特征，这是由于肌红蛋白的氧合状态使其呈现高亮红色；而当牛肉发生腐败时，直方图峰形出现紊乱，可能伴随异常峰值生成。观察HSV颜色直方图（图5（b1）、图5（b2）、图5（b3））可发现，新鲜牛肉的H通道峰值处于最左侧（对应红色区间），随着新鲜度下降，峰值逐步右移；S通道的分布范围逐渐变宽，表明水分流失导致色素分布离散化，颜色饱和度随之降低；V通道在新鲜状态下呈现均匀分布，而腐败过程中亮度分布出现明显不均。在Lab颜色直方图（图5（c1）、图5（c2）、图5（c3））中，新鲜牛肉的L通道分布高度集中，反映其表面光泽度均匀；腐败后L通道分布弥散，表明肉质光泽度显著下降。a通道的峰值在新鲜阶段达到最高，随贮藏时间延长持续降低，对应肉色从鲜红向褐变的转变；b通道值则呈上升趋势，显示黄色调逐渐加深。
图6是牛肉贮藏过程中颜色参数变化图，从图6 RGB均值可见，初始阶段肌红蛋白对红光吸收较弱、反射能力强，使牛肉呈现鲜红色，故R均值较高。但随着贮藏时间延长，肌红蛋白逐步分解氧化，颜色从暗红渐变为褐色，致使R均值持续下降，而G、B均值则不断上升。图6 HSV均值中，因牛肉色泽逐渐加深，H均值相应升高；新鲜牛肉因表面湿润且肌纤维结构完整，V均值较高，而随着贮藏中表面黏液增多，V均值持续降低；变质后由于色素分解且多色混合，颜色趋近灰色，故S均值不断下降，但变化不显著。Lab颜色空间模型可全面反映牛肉表面色泽及色差变化，从图6 Lab均值能看出，L和a的均值显著下降，这是因为贮藏期间细胞失水较多，加之蛋白酶分解肌原纤维形成不规则孔隙，导致反射能力减弱，进而使L值降低；同时肌红蛋白氧化导致红色逐渐褪去，促使a值快速下降。此外，贮藏过程中脂肪氧化不断加剧，黄色调愈发明显，使得b均值持续上升。
2.3.2　牛肉样本高光区域提取特征分析
高光区域特征分析是食品品质无损检测领域的重要研究方向，其核心在于建立光学特性与牛肉生理生化指标的关联性，实现新鲜度的快速精准判别。叠加高亮区域轮廓的新鲜、次新鲜和腐败样本如图7（a1）、图7（a2）和图7（a3），随着贮藏时间延长，牛肉表面高光区域的轮廓面积显著增大。这可能是因为贮藏时间延长导致的水分向表面流出并积累，进而扩大了反射范围。通过将RGB图像转换为灰度图像如图7（b1）、图7（b2）和图7（b3）并进一步二值化处理如图7（c1）、图7（c2）和图7（c3），可有效过滤非高光区域的细节信息，仅保留反光强度最高的区域，使高光特征的形态、尺寸及空间分布更易于分析。图7（d1）、图7（d2）和图7（d3）所示的高光区域量化分析表明：新鲜牛肉因表面纹理致密、尚未产生黏液及微生物菌落，高光区域占比仅为3.07%；次新鲜牛肉开始出现黏液分泌且微生物数量呈中等水平，高光区域占比提升至9.34%；而腐败牛肉因表面黏液分泌显著且微生物大量繁殖，高光区域占比达11.03%。
2.4　线性判别分析、主成分分析和核主成分分析结果
线性判别分析（linear discriminant analysis，LDA）、PCA及KPCA是当前广泛应用的降维算法。电子鼻数据经处理后的结果如图8所示，总体来看，同一类别的数据呈现出聚集分布的状态，不同类别之间界限基本清晰，可以区分。但在区分新鲜与次新鲜样本时，其界限表现出一定的模糊性，这种现象可能是由于组内样本存在个体差异性，进而使得不同组别的结果呈现出一定程度的相似性。LDA、PCA和KPCA贡献率分别为94.5%、91%和97.6%，其中KPCA的分析结果最佳。
对牛肉图像中提取了9个颜色特征R、G、B、H、S、V、L、a、b均值和高光区域占比，以此为变量构建模型。考虑到KPCA在电子鼻采集数据的信息贡献率方面表现最优，故在机器视觉分析中同样采用KPCA进行降维处理，其降维结果如图9所示贡献率为93.25%。
图9可以看出不同新鲜度组别之间存在较为明显的差异，但是存在一些样本交错的情况，这可能是由于存在某些样本之间存在一定的相似性，尽管各组间的界限不够清晰，但仍然反映了它们之间的差异性。由于机器视觉的图像信息维度多、独立性强，信息整合难度稍大，电子鼻KPCA贡献率（97.6%）略高于机器视觉KPCA贡献率（93.25%），但是二者都可以较好地区分不同牛肉新鲜度。上述结果也表明，KPCA可保留原始数据93%以上的关键信息，因此后续牛肉新鲜度的建模采用KPCA提取的前3个主成分作为模型的输入。
2.5　牛肉样本的预测模型分析
牛肉样本数据采用KPCA进行降维处理，利用SMA-CNN-BiGRU-Attention、BPNN、SVM和CNN分别建立电子鼻、机器视觉和数据融合的预测模型，其中3种新鲜度分类结果见表3，12 d冷藏分类结果见表4。
根据表3的统计，KPCA降维后数据的预测结果通常要优于原始数据预测结果，在新鲜、次新鲜和腐败3种新鲜度分类场景下，无论是基于原始数据还是降维后的数据，数据融合分析的性能均显著优于电子鼻、机器视觉单项检测方法，其中KPCA降维后电子鼻、机器视觉和数据融合的SMA-CNN-BiGRU-Attention模型预测的准确率分别为98.36%、94.26%和100%。相较于单一检测手段，数据融合技术通过整合多源信息，提升了检测结果的准确性与可靠性。
通过对表4的统计，12 d冷藏分类场景下数据表现出的规律与3种新鲜度分类时基本一致。但由于分类类别大幅增加，检测任务的复杂度显著提升，导致整体准确率出现明显下降。即便如此，数据融合技术的检测准确率仍高于电子鼻、机器视觉等单一检测方法，数据融合预测模型测试集多次预测的准确率高于96.4%，其中，KPCA降维后数据融合的SMA-CNN-BiGRU-Attention模型预测的准确率达到98.36%，高于电子鼻和机器视觉单一模型的94.26%和88.52%，充分展现出多源信息整合在复杂分类任务中的优势。
根据上述研究结果表明，SMA-CNN-BiGRU-Attention模型的检测性能显著优于其它对比模型，在牛肉新鲜度的区分任务中表现出更强的判别能力。
3　结论
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本研究采用人工嗅视觉手段对冷藏牛肉的新鲜度进行研究，自制电子鼻可准确地捕捉与牛肉新鲜度相关的挥发性气体变化，机器视觉装置则能有效识别牛肉新鲜度对应的外观特征演变规律，通过对牛肉样本的气体和外观特征进行多模态特征融合处理，结合深度学习SMA-CNN-BiGRU-Attention方法构建了高精度的牛肉新鲜度预测模型。研究结果表明，该方法在3种新鲜度分类任务中实现了100%的准确率，在12 d冷藏分类中准确率超过95%。该研究克服了传统肉制品理化检测方法存在操作繁琐、检测时间长、成本高的缺点，同时相比于单一检测技术嗅视觉多信息融合显著提升了牛肉新鲜度检测的准确性和鲁棒性，研究证明了人工嗅视觉技术在肉类品质检测领域的应用潜力。但是，肉制品新鲜度快速检测仪器的实际应用还有很多问题需要解决，比如新鲜度信息快速准确地获取、检测仪器的小型化和可操作性、提高检测结果的准确率和可信度等，所以多传感装置的高度集成及小型化将是肉制品检测研究的重点方向。

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2026年第47卷第16期
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doi: 10.13386/j.issn1002-0306.2025070081
  • 接收时间：2025-07-08
  • 首发时间：2026-10-24
  • 出版时间：2026-08-15
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  • 收稿日期：2025-07-08
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    盐城工学院机械工程学院，江苏盐城 224051

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周博（1971−），男，博士，副教授，研究方向：电子鼻技术，E-mail：。
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2种不同金属材料的力学参数
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Family		 属数
Number of
genus		 种数
Number of
species		 占总种数比例
Percentage of
total species (%)		 属
Genus		 种数
Number of
species		 占总种数比例
Percentage of total
species (%)
鹅膏菌科Amanitaceae 2 11 5.26 鹅膏菌属 Amanita 10 4.78
小菇科 Mycenaceae 2 12 5.74 丝盖伞属 Inocybe 5 2.39
多孔菌科 Polyporaceae 8 14 6.70 蜡蘑属 Laccaria 5 2.39
红菇科 Russulaceae 3 23 11.00 小皮伞属 Marasmius 6 2.87
小菇属 Mycena 11 5.26
光柄菇属 Pluteus 5 2.39
红菇属 Russula 17 8.13
栓菌属 Trametes 5 2.39
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