Rapid identification of healthy Tegillarca granosa using laser-induced breakdown spectroscopy and fusion model

作     者：Xiaojing Chen Yanan Chen Xi Chen Leiming Yuan Chengxi Jiang Guangzao Huang Wen Shi Xiaojing Chen;Yanan Chen;Xi Chen;Leiming Yuan;Chengxi Jiang;Guangzao Huang;Wen Shi

作者机构：College of Electrical and Electronic EngineeringWenzhou UniversityWenzhouChina College of Computer and Artificial IntelligenceWenzhou UniversityWenzhouChina 

出 版 物：《Food Quality and Safety》 (食品品质与安全研究（英文版）)

年 卷 期：2023年第7卷第3期

页      面：474-482页

核心收录：

学科分类：09[农学] 0903[农学-农业资源与环境] 

基　　金：The authors would like to acknowledge the financial support provided by the Natural Science Foundation of Zhejiang(No.LY21C200001) China,the National Natural Science Foundation of China(Nos.62105245 and 61805180) the Wenzhou Science and Technology Bureau General Project(Nos.S2020011 and G20200044),China 

主　　题：Laser-induced breakdown spectrum(LIBS) one-class classification(OCC) sum of ranking differences(SRD) fusion model heavy metal 

摘      要：Objectives:This study presents a method combining a one-class classifier and laser-induced breakdown spectrometry(LIBS)to quickly identify healthy Tegillarca granosa(***).Materials and Methods:The sum of ranking differences(SRD)was used to fuse multiple anomaly detection metrics to build the one-class classifier,which was only trained with healthy *** one-class classifier can identify healthy *** to exclude non-healthy *** proposed method calculated multiple anomaly detection metrics and standardized them to obtain a fusion *** on the fusion matrix,the samples were ranked by SRD and those ranked lowest and below the threshold were considered to be ***:Multiple anomaly detection metrics were fused by the SRD algorithm and tested on each band,and the final fusion model achieved an accuracy rate of 98.46%,a sensitivity of 100%,and a specificity of 80%.The remaining three single classification models obtained the following results:the SVDD model achieved an accuracy rate of 87.69%,a sensitivity of 90%,and a specificity of 60%;the OCSVM model achieved an accuracy rate of 80%,a sensitivity of 76.67%,and a specificity of 60%;and the DD-SIMCA model achieved an accuracy rate of 95.38%,a sensitivity of 98.33%,and a specificity of 60%.Conclusions:The experimental results showed that the proposed method achieved better results than the traditional one-class classification methods with a single ***,the fusion method effectively improves the performance of traditional one-class classifiers when using LIBS to quickly identify healthy substances(healthy ***).