Near Infrared Reflectance Spectroscopy Coupled to Chemometrics as a Cost-Effective, Rapid, and Non-Destructive Tool for Fish Fraud Control: Monitoring Source, Condition, and Nutritional Value of Five Common Whitefish Species

Abstract

Fish fraud is a problematic issue for the industry. For it to be properly addressed will require the use of accurate, rapid, and cost-effective tools. In this work, near infrared reflectance spectroscopy (NIRS) was used to predict nutritional values (protein, lipids, and moisture) as well as to discriminate between sources (farmed vs. wild fish) and conditions (fresh or defrosted fish). Samples of five whitefish species-Alaskan pollock (Gadus chalcogrammu), Atlantic cod (G. morhua), European plaice (Pleuronectes platessa), common sole (Solea solea), and turbot (Psetta maxima)-including farmed, wild, fresh, and frozen ones, were scanned by a low-cost handheld near infrared reflectance spectrometer with a spectral range between 900 and 1700 nm. Several machine learning algorithms were explored for both regression and classification tasks, achieving precisions and coefficients of determination higher than 88% and 0.78, respectively. Principal component analysis (PCA) was used to cluster samples according to classes where good linear discriminations were denoted. Loadings from PCA revealed bands at 1150, 1200, and 1400 nm as the most discriminative spectral regions regarding classification of both source and condition, suggesting the absorbance of OH, CH, CH2, and CH3 groups as the most important ones. This study shows the use of NIRS and both linear and non-linear learners as a suitable strategy to address fish fraud and fish QC.