Rapid identification of healthy <i>Tegillarca granosa</i> using laser-induced breakdown spectroscopy and fusion model
نویسندگان
چکیده
Abstract Objectives This study presents a method combining one-class classifier and laser-induced breakdown spectrometry (LIBS) to quickly identify healthy Tegillarca granosa (T. granosa). Materials Methods The sum of ranking differences (SRD) was used fuse multiple anomaly detection metrics build the classifier, which only trained with T. granosa. can exclude non-healthy proposed calculated standardized them obtain fusion matrix. Based on matrix, samples were ranked by SRD those lowest below threshold considered be unhealthy. Results Multiple fused algorithm tested each band, final model achieved an accuracy rate 98.46%, sensitivity 100%, specificity 80%. remaining three single classification models obtained following results: SVDD 87.69%, 90%, 60%; OCSVM 80%, 76.67%, DD-SIMCA 95.38%, 98.33%, 60%. Conclusions experimental results showed that better than traditional methods metric. Therefore, effectively improves performance classifiers when using LIBS substances (healthy
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ژورنال
عنوان ژورنال: Food Quality and Safety
سال: 2023
ISSN: ['2399-1402', '2399-1399']
DOI: https://doi.org/10.1093/fqsafe/fyad022