Fishy forensics: FT-NIR and machine learning based authentication of Mediterranean anchovies (Engraulis encrasicolus)

Seafood authentication and traceability is a challenging issue owing to its complex supply chain and fishing in international waters. NIR spectroscopy has been successfully used to authenticate food of animal and plant origin. In this study, FT-NIR was used to discriminate between Mediterranean anchovies ( Engraulis encrasicolus) fished from Adriatic, Balearic, and Tyrrhenian Sea. The spectra were prepared using the standard normal variate (SNV) and the Savitzky-Golay 1st derivative, 2nd order (SG), as well as both together. The model was built, after outlier removal, with linear-support vector machine (L-SVM), polynomial-SVM (P-SVM), k-nearest neighbor (k-NN) and Random Forest (RF). Spectral preprocessing improved model classification accuracy for all algorithms. The data could not be put into groups by linear algorithms like L-SVM and k-NN because the NIR spectra were not linear and had many columns. Non-linear algorithms, P-SVM and RF when coupled with SG+SNV, successfully produced models with maximum robustness. P-SVM and RF models had 100% accuracy in training set and 95.7% and 95.5% accuracy in testing set, respectively and 95.2 and 95.1 accuracy in cross-validation set. • FT-NIR was used to trace the origin of Mediterranean Anchovies fished. • Spectra was pre-processed with Standard Normal Variate (SVM) & 1 st derivate Savitzky-Golay (SG), individually & in combination. • Four different machine learning techniques were used for modelling- L-SVM, P-SVM, k-NN and RF. • P-SVM and RF produced robust models with >95% classification accuracy and >94% precision and recall. • SG+SVM pre-processing worked best with both these algorithms, than individual pre-processing.