Classification and quantification of adulterants in Alaska pollock based on fourier transform near-infrared spectroscopy with chemometrics

Abstract

Alaska pollock is one of the most economically valuable and consumed cod species, but the adulterants in the market affect its quality and value. This study used a combination of FT-NIR spectroscopy and chemometrics to construct a qualitative and quantitative analysis model. Before the establishment of detection models, different pre-processing techniques were used to eliminate background interference. Principal component analysis (PCA) and analysis of variance (ANOVA) showed the feasibility of spectral methods for the detection of Alaska pollock adulteration. The modeling results show that the RF classification model had the most suitable prediction results (accuracy: 92.18%, precision: 98.04%, recall: 92.59%, and F1-score: 95.29%), and SVR was the optimal prediction model for quantitative analysis, with an RC2 of 0.941, an RMSEC of 0.060, an RP2 of 0.946, and an RMSEP of 0.062. These results indicate that, based on the developed detection model, adulterants in Alaska pollock could be rapidly detected and accurately quantified. In addition, it provides a reliable basis for rapid and non-destructive detection of and has great potential for determining other seafood species.