In this study, a DNA mini-barcoding methodology was developed for the differentiation of species commonly found in canned tuna. Primers were designed to target a 236-base pair (bp) fragment of the mitochondrial control region (CR) and a 179-bp fragment of the first internal transcribed spacer region (ITS1). Phylogenetic analysis revealed the ability to differentiate 13 tuna species on the basis of the CR mini-barcode, except in a few cases of species introgression. Supplementary use of ITS1 allowed for differentiation of introgressed Atlantic bluefin tuna (Thunnus thynnus) and albacore tuna (Thunnus alalunga), while differentiation of introgressed Atlantic bluefin tuna and Pacific bluefin tuna (Thunnus orientalis) requires a longer stretch of the CR. After primer design, a market sample of 53 commercially canned tuna products was collected for testing. This mini-barcoding system was able to successfully identify species in 23 of the products, including albacore tuna, yellowfin tuna (Thunnus albacares), and skipjack tuna (Katsuwonus pelamis). One instance of mislabeling was detected, in which striped bonito (Sarda orientalis) was identified in a product labeled as tongol tuna (Thunnus tonggol). PCR amplification and sequencing was unsuccessful in a number of products, likely due to factors such as the presence of PCR inhibitors and DNA fragmentation during the canning process. Overall, CR and ITS1 show high potential for use in identification of canned tuna products; however, further optimization of the assay may be necessary in order to improve amplification and sequencing success rates.

This study is directed towards developing a fast, non-destructive, and easy-to-use handheld multimode spectroscopic system for fish quality assessment. We apply data fusion of visible near infra-red (VIS-NIR) and short wave infra-red (SWIR) reflectance and fluorescence (FL) spectroscopy data features to classify fish from fresh to spoiled condition. Farmed Atlantic and wild coho and chinook salmon and sablefish fillets were measured. Three hundred measurement points on each of four fillets were taken every two days over 14 days for a total of 8400 measurements for each spectral mode. Multiple machine learning techniques including principal component analysis, self-organized maps, linear and quadratic discriminant analyses, k-nearest neighbors, random forest, support vector machine, and linear regression, as well as ensemble and majority voting methods, were used to explore spectroscopy data measured on fillets and to train classification models to predict freshness. Our results show that multi-mode spectroscopy achieves 95% accuracy, improving the accuracies of the FL, VIS-NIR and SWIR single-mode spectroscopies by 26, 10 and 9%, respectively. We conclude that multi-mode spectroscopy and data fusion analysis has the potential to accurately assess freshness and predict shelf life for fish fillets and recommend this study be expanded to a larger number of species in the future.