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

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.

Spectral data fusion in nondestructive detection of food products: Strategies, recent applications, and future perspectives

“…The outcomes exhibited that the overall performance of statistics fusion was higher than that of single detection data, and the accuracy reached 96.6%. In another study, Xu et al (2022a) established a rapid nondestructive identification method for codfish using the NIR and Raman spectroscopy data fusion. The decision fusion method based on the Bayesian algorithm demonstrated notably superior discrimination accuracy when compared to the other two fusion methods (LLDF and MLDF), as well as the single spectral methods (Raman and NIR).…”

Challenges in the Use of AI-Driven Non-Destructive Spectroscopic Tools for Rapid Food Analysis

“…Therefore, combining NIR with other techniques, such as MIR or SERS, can lead to an encompassing evaluation of food by integrating multiple information sources. Data fusion from complementary sensors has been employed for authentication and quality assessment, significantly boosting the performance of individual instruments [ 102 , 103 ]. Regarding HSI data, modelling and wavelength selection methods for spectral data are not entirely effective at solving the issue of 3D data.…”