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

Abstract

Abstract Objectives This study presents a method combining a one-class classifier and laser-induced breakdown spectrometry (LIBS) to quickly identify healthy Tegillarca granosa (T. 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 T. granosa. The one-class classifier can identify healthy T. granosa to exclude non-healthy T. granosa. The proposed method calculated multiple anomaly detection metrics and standardized them to obtain a fusion matrix. Based on the fusion matrix, the samples were ranked by SRD and those ranked lowest and below the threshold were considered to be unhealthy. Results 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 metric. Therefore, the fusion method effectively improves the performance of traditional one-class classifiers when using LIBS to quickly identify healthy substances (healthy T. granosa).