Bayesian Fusion Model Enhanced Codfish Classification Using Near Infrared and Raman Spectrum

Author:

Xu Yi,Koidis AnastasiosORCID,Tian Xingguo,Xu Sai,Xu Xiaoyan,Wei Xiaoqun,Jiang Aimin,Lei HongtaoORCID

Abstract

In this study, a Bayesian-based decision fusion technique was developed for the first time to quickly and non-destructively identify codfish using near infrared (NIRS) and Raman spectroscopy (RS). NIRS and RS spectra from 320 codfish samples were collected, and separate partial least squares discriminant analysis (PLS-DA) models were developed to establish the relationship between the raw data and cod identity for each spectral technique. Three decision fusion methods: decision fusion, data layer or feature layer, were tested and compared. The decision fusion model based on the Bayesian algorithm (NIRS-RS-B) was developed on the optimal discrimination features of NIRS and RS data (NIRS-RS) extracted by the PLS-DA method whereas the other fusion models followed conventional, non-Bayesian approaches. The Bayesian model showed enhanced classification metrics (92% sensitivity, 98% specificity, 98% accuracy) that were significantly superior to those demonstrated by any of other two spectroscopic methods (NIRS, RS) and the two data fusion methods (data layer fused, NIRS-RS-D, or feature layer fused, NIRS-RS-F). This novel proposed approach can provide an alternative classification for codfish and potentially other food speciation cases.

Funder

the National Scientific Foundation of China

HeYuan Planned Program in Science and Technology

Generic Technique Innovation Team Construction of Modern Agriculture of Guangdong Province

National Key Research and Development Program of Thirteenth Five-Year Plan

Publisher

MDPI AG

Subject

Plant Science,Health Professions (miscellaneous),Health (social science),Microbiology,Food Science

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