Rapid identification of foodborne pathogenic bacteria using hyperspectral imaging combined with convolutional neural networks(高光谱结合卷积神经网络对食源性致病菌的快速识别)

This paper presents a new classification model combined with hyperspectral imaging for rapid identification of foodborne pathogens. It adopts hyperspectral analysis to detect Shigella, Salmonella, Clostridium perfringens, and Streptococcus suis, and collects hyperspectral data. The region of interes...

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Main Authors:	周贯旭(ZHOU Guanxu), 姜红(JIANG Hong), 徐雪芳(XU Xuefang)
Format:	Article
Language:	zho
Published:	Zhejiang University Press 2025-07-01
Series:	Zhejiang Daxue xuebao. Lixue ban
Online Access:	https://doi.org/10.3785/j.issn.1008-9497.2025.04.008
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author	周贯旭(ZHOU Guanxu) 姜红(JIANG Hong) 徐雪芳(XU Xuefang)
author_facet	周贯旭(ZHOU Guanxu) 姜红(JIANG Hong) 徐雪芳(XU Xuefang)
author_sort	周贯旭(ZHOU Guanxu)
collection	DOAJ
description	This paper presents a new classification model combined with hyperspectral imaging for rapid identification of foodborne pathogens. It adopts hyperspectral analysis to detect Shigella, Salmonella, Clostridium perfringens, and Streptococcus suis, and collects hyperspectral data. The region of interest in the hyperspectral image using MATLAB software is selected and the average reflectance within that region is calculated. PCA dimensionality reduction on one-dimensional spectral data and hyperspectral images are then preformed, a 1D-CNN classification model for spectral data using Python software, and a 2D-CNN classification model for hyperspectral images are established. We build a feature fusion convolutional neural network based on spectra and images. At the same time, establish random forest (RF), K-nearest neighbor (KNN), and support vector machine (SVM) classification models for the reduced spectral data. The performance of the model using Precision, Recall, and F1-score metrics are evaluated. Through the analysis of the 1D-CNN, 2D-CNN, and feature fusion neural network classification models established on one-dimensional spectral data and hyperspectral images, it is show that the accuracy of the three models was 89.0%, 71.6%, and 93.3%, respectively. The feature fusion CNN model based on spectra and images can achieve good classification of the four strains, which outperforms the other three traditional machine learning algorithm models. In summarg, the combination of hyperspectral and convolutional neural networks is simple and easy to operate, and the established feature fusion neural network classification model can achieve rapid classification of foodborne pathogens.利用高光谱成像技术，采集了志贺氏菌、沙门氏菌、产气荚膜梭菌以及猪链球菌的高光谱图像和光谱数据。用MATLAB软件选取高光谱图像中的感兴趣区域（ROI），计算了该区域的平均反射率。通过Python软件采用主成分分析（PCA）方法分别对一维光谱数据和高光谱图像进行降维，构建了一维卷积神经网络（one dimensional convolutional neural network，1D-CNN）模型和二维卷积神经网络（2D-CNN）模型，将二者相结合，提出了一种快速识别食源性致病菌的特征融合CNN模型，同时对降维后的光谱数据建立随机森林（RF）、K-最近邻（KNN）及支持向量机（SVM）模型，用Precision，Recall及F1-score指标对模型性能进行评价。结果表明，1D-CNN，2D-CNN和特征融合CNN模型的分类准确率分别为89.0%，71.6%和93.3%，且特征融合CNN模型优于其他3种传统机器学习算法模型。将高光谱与CNN相结合的特征融合CNN模型可对食源性致病菌进行快速分类。
format	Article
id	doaj-art-51f997cfe82441e2b50d045f6b05fa4f
institution	Kabale University
issn	1008-9497
language	zho
publishDate	2025-07-01
publisher	Zhejiang University Press
record_format	Article
series	Zhejiang Daxue xuebao. Lixue ban
spelling	doaj-art-51f997cfe82441e2b50d045f6b05fa4f2025-08-20T04:02:27ZzhoZhejiang University PressZhejiang Daxue xuebao. Lixue ban1008-94972025-07-0152448949510.3785/j.issn.1008-9497.2025.04.008Rapid identification of foodborne pathogenic bacteria using hyperspectral imaging combined with convolutional neural networks(高光谱结合卷积神经网络对食源性致病菌的快速识别)周贯旭(ZHOU Guanxu)0姜红(JIANG Hong)1徐雪芳(XU Xuefang)21College of Investigation， People's Public Security University of China， Beijing 100038， China(1中国人民公安大学侦查学院，北京 100038)2Center of Forensic Science Beijing Hui Zheng Zhuo Yue Technology Co.， Ltd.， Beijing 102446， China(2北京汇正卓越科技有限公司司法鉴定中心，北京 102446)3State Key Laboratory for Infectious Disease Prevention and Control and National Institute for Communicable Diseases Control and Prevention， Chinese Center for Disease Control and Prevention， Beijing 102206， China(3中国疾病预防控制中心传染病预防控制所，北京 102206)This paper presents a new classification model combined with hyperspectral imaging for rapid identification of foodborne pathogens. It adopts hyperspectral analysis to detect Shigella, Salmonella, Clostridium perfringens, and Streptococcus suis, and collects hyperspectral data. The region of interest in the hyperspectral image using MATLAB software is selected and the average reflectance within that region is calculated. PCA dimensionality reduction on one-dimensional spectral data and hyperspectral images are then preformed, a 1D-CNN classification model for spectral data using Python software, and a 2D-CNN classification model for hyperspectral images are established. We build a feature fusion convolutional neural network based on spectra and images. At the same time, establish random forest (RF), K-nearest neighbor (KNN), and support vector machine (SVM) classification models for the reduced spectral data. The performance of the model using Precision, Recall, and F1-score metrics are evaluated. Through the analysis of the 1D-CNN, 2D-CNN, and feature fusion neural network classification models established on one-dimensional spectral data and hyperspectral images, it is show that the accuracy of the three models was 89.0%, 71.6%, and 93.3%, respectively. The feature fusion CNN model based on spectra and images can achieve good classification of the four strains, which outperforms the other three traditional machine learning algorithm models. In summarg, the combination of hyperspectral and convolutional neural networks is simple and easy to operate, and the established feature fusion neural network classification model can achieve rapid classification of foodborne pathogens.利用高光谱成像技术，采集了志贺氏菌、沙门氏菌、产气荚膜梭菌以及猪链球菌的高光谱图像和光谱数据。用MATLAB软件选取高光谱图像中的感兴趣区域（ROI），计算了该区域的平均反射率。通过Python软件采用主成分分析（PCA）方法分别对一维光谱数据和高光谱图像进行降维，构建了一维卷积神经网络（one dimensional convolutional neural network，1D-CNN）模型和二维卷积神经网络（2D-CNN）模型，将二者相结合，提出了一种快速识别食源性致病菌的特征融合CNN模型，同时对降维后的光谱数据建立随机森林（RF）、K-最近邻（KNN）及支持向量机（SVM）模型，用Precision，Recall及F1-score指标对模型性能进行评价。结果表明，1D-CNN，2D-CNN和特征融合CNN模型的分类准确率分别为89.0%，71.6%和93.3%，且特征融合CNN模型优于其他3种传统机器学习算法模型。将高光谱与CNN相结合的特征融合CNN模型可对食源性致病菌进行快速分类。https://doi.org/10.3785/j.issn.1008-9497.2025.04.008
spellingShingle	周贯旭(ZHOU Guanxu) 姜红(JIANG Hong) 徐雪芳(XU Xuefang) Rapid identification of foodborne pathogenic bacteria using hyperspectral imaging combined with convolutional neural networks(高光谱结合卷积神经网络对食源性致病菌的快速识别) Zhejiang Daxue xuebao. Lixue ban
title	Rapid identification of foodborne pathogenic bacteria using hyperspectral imaging combined with convolutional neural networks(高光谱结合卷积神经网络对食源性致病菌的快速识别)
title_full	Rapid identification of foodborne pathogenic bacteria using hyperspectral imaging combined with convolutional neural networks(高光谱结合卷积神经网络对食源性致病菌的快速识别)
title_fullStr	Rapid identification of foodborne pathogenic bacteria using hyperspectral imaging combined with convolutional neural networks(高光谱结合卷积神经网络对食源性致病菌的快速识别)
title_full_unstemmed	Rapid identification of foodborne pathogenic bacteria using hyperspectral imaging combined with convolutional neural networks(高光谱结合卷积神经网络对食源性致病菌的快速识别)
title_short	Rapid identification of foodborne pathogenic bacteria using hyperspectral imaging combined with convolutional neural networks(高光谱结合卷积神经网络对食源性致病菌的快速识别)
title_sort	rapid identification of foodborne pathogenic bacteria using hyperspectral imaging combined with convolutional neural networks 高光谱结合卷积神经网络对食源性致病菌的快速识别
url	https://doi.org/10.3785/j.issn.1008-9497.2025.04.008
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Rapid identification of foodborne pathogenic bacteria using hyperspectral imaging combined with convolutional neural networks(高光谱结合卷积神经网络对食源性致病菌的快速识别)

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