摘要
药品食品的安全问题一直是人们关注的重点。相比于传统的食源性致病菌光谱检测方法,拉曼光谱法具有检测范围广、检测灵活、光谱特征突出等特点。本文以常见的食源性致病菌为研究对象,利用拉曼光谱仪采集了11种食源性致病菌样品的132个拉曼光谱数据,提出了一种基于主成分分析和随机森林算法的分类模型。实验结果表明,主成分分析结合随机森林算法的分类模型可以将食源性致病菌区分开,且分类准确度可达到91.36%。
Objective Food and drug safety is of great concern to society.Food pathogenic bacteria are pathogenic bacteria that can cause food poisoning or bacteria that use food as the vector of transmission.Therefore,quick and effective detection of food-borne pathogenic bacteria in food is crucial to protect public health.The culture separation method,which is traditionally used to examine microorganisms,depends on the medium used for culturing,separation,and biochemical identification.Detection of food-borne pathogenic bacteria generally requires five to seven days and includes a series of detection procedures such as pre-enrichment,selective enrichment,microscopic examination and serological verification.Therefore,traditional detection methods are insufficient for preventing and controlling foodborne pathogenic bacteria.However,Raman spectroscopy is a nondestructive method that can be used to rapidly and accurately identify molecules existing in the functional groups.In this study,11 food-borne pathogenic bacteria samples were used to construct a recognition and classification model based on a random forest algorithm and Raman spectra.This model was then used to build a classification and recognition model to resolve the problems of low classification accuracy and long detection time required by traditional methods used to detect food-borne pathogenic bacteria.The results of this study will help to ensure public health safety by rapidly and effectively detecting pathogens in food and drugs.Methods All of the food-borne pathogenic bacteria in this study were purchased from China Center of Industrial Culture Collection.First,a sample of food-borne pathogenic bacteria was detected by Raman spectrometry in a shift range of 500--1600 cm-1.LabSpec 6.0 software was used for spectral collection,and each sample was collected 15 times.After screening,132 Raman spectral data were obtained.Min-max normalization was performed on the Raman spectral data in the spectral preprocessing stage,and the intensity was mapped to a range of [0
作者
王其
曾万聃
夏志平
李志萍
曲晗
Wang Qi;Zeng Wandan;Xia Zhiping;Li Zhiping;Qu Han(College of Computer Science and Information Engineering,Shanghai Institute of Technology,Shanghai 201418,China;Military Veterinary Institute,Changchun,Jilin 130062,China)
出处
《中国激光》
EI
CAS
CSCD
北大核心
2021年第3期130-138,共9页
Chinese Journal of Lasers
关键词
光谱学拉曼光谱
机器学习
食源性致病菌检测
主成分分析
随机森林
Raman spectroscopy
machine learning
food-borne pathogen detection
principle component analysis
random forest Spectroscopy
