VIS-NIR hyperspectral imaging and multivariate analysis for direct characterization of pelagic fish species

The identification of fish species and their physical and chemical characterization play a crucial role in the fishing industry, fish-food research and the management of marine resources. Traditional methods for species identification, such as expert observation, DNA barcoding and meta-barcoding, though effective, require labor-intensive laboratory work. Consequently, there is a pressing need for more objective and efficient methodologies for accurate fish species identification and characterization. This study proposes the use of multivariate analysis and visible-near infrared hyperspectral imaging (HSI) for a rapid characterization of fish, including the evaluation of specific morphological regions of interest (ROIs) in fish images or intrasample spectral variability, species differentiation, and freshness assessment. The study involves three pelagic species: sardine (Strangomera bentincki), silverside (Odontesthes regia) and anchovy (Engraulis ringens). Principal component analysis (PCA), support vector machine regression (SVM-R), partial least squares regression (PLS-R), and partial least squares discriminant analysis (PLS-DA) were applied as multivariate techniques for these purposes. Comparative studies of morphological ROIs revealed significant differences between the spectral characteristics of various fish zones. A decrease in reflectance intensity due to freshness loss was detected, and the prediction of this freshness, quantified as "time after capture," was achievable using SVM-R, with a 9% relative error of prediction. Overall, VIS-NIR HSI, supported by multivariate analysis, enables differentiation between the studied species, highlighting its potential as a robust fish species identification and characterization tool.