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This study evaluated near-infrared (NIR) and visible-NIR (Vis-NIR) spectroscopy as a way to distinguish fresh (F) from frozen-thawed (T) swordfish cutlets (Xiphias gladius). A total of 90 F and 60 T samples were used. The T samples were stored at a high and low frozen temperature (HT: −10°C; LT: −18°C). Spectra were collected using a Vis-NIR portable spectrophotometer (380–1080 nm) and a NIR monochromator (1100–2500 nm). The percentage of correctly classified samples obtained with Vis-NIR spectroscopy was ≥ 96.7%, whereas that for NIR was ≥ 90.0%. The best classification was observed comparing F and HT samples using Vis-NIR (100 vs. 96.7%, respectively). The more descriptive principal component scores (PCS) of NIR and Vis-NIR were used with a multivariate binary logistic regression. The model with the PCS of the first two Vis-NIR principal components accounted for 81.1% of the classification. Vis-NIR could be a strategic tool to screen the cold treatment of swordfish. [ABSTRACT FROM PUBLISHER]

Traditional food plays a crucial role in showcasing the culture and heritage of its producers. The identification of the Traditional agro-Food Product (TFP) " Seppia bianca di Chioggia ", which is derived from Italian traditional cuttlefish, was performed through genetic analysis utilizing mitochondrial cytochrome c oxidase I subunit gene (COI) sequences and near-infrared spectroscopy (NIRS). The set samples were obtained from the shores of Chioggia (Adriatic Sea), and a comparison was made with cuttlefishes collected from Cesenatico (Adriatic Sea) and the coastal regions of France (Atlantic Ocean). The primary objective was to differentiate the TFP from samples sourced from nearby production areas and distant geographic locations that are commonly sold in Italy. A total of 368 cuttlefishes were analysed for NIRS data, whereas a subset of 75 samples were randomly selected and analysed for COI sequences to assess cuttlefish origin and TFP certification. Genetic and NIRS models were then tested on the same set of samples using a machine learning approach through the hold-out validation method. Additionally, an external validation was performed using a new set of cuttlefishes (labelled fished in shore of Chioggia or in Atlantic Ocean) collected from the daily fish market located in Chioggia (Venice, Italy). Both technologies showed excellent accuracy (100%) in discriminating fishing origin between the Adriatic Sea and Atlantic Ocean. However, the genetic information was not sufficient to differentiate cuttlefishes between the closest fishing areas within the Adriatic Sea (balanced accuracy = 50–83.5%) and did not provide information about the production system (TFP certification), while it showed the highest performance of genetic membership assignment (≥96%) among untreated cuttlefishes collected in the fish market. However, the NIRS model completely differentiated (accuracy = 100%) samples according to the specificity of the treatment nature (simple skinned products vs. TFP preparations), while it was unable to differentiate cuttlefish origin in untreated samples collected in the fish market (accuracy = 50%). The present results demonstrated that NIRS represents a rapid, fast, green and nondestructive approach to support on-site, practical inspection to authenticate and promote TFP protection that mirrors a niche product as a result of the synergy between natural resources and cultural heritage. • Preservation of Traditional Agro-Food fishery products to protect cultural heritage. • Counterfeiting and mislabelling undermining local productions and the farm-to-fork strategy. • Near Infrared Spectroscopy is a fast and feasible investigation for food authenticity. • Cytochrome c oxidase I to identify the geographical origin areas of fishing. [ABSTRACT FROM AUTHOR]

Abstract: This study investigated the possibility of using near-infrared spectroscopy (NIRS) for the authentication of Asiago d’allevo, a protected designation of origin cheese from northern Italy. Latent variable models applied on spectral data were developed and used to estimate several chemical properties and to classify the available dataset according to the location and management of the cheesemaking factory (lowland and alpine), the ripening age (6, 12, 18 and 36months), the altitude of milk production (low, medium, medium–high and high), and the period of the year of the cheese production (May, July and September). The variable importance in projection index was used to identify the most informative spectral regions for discrimination. Results showed that NIR spectra can be used both to accurately estimate several chemical properties and to classify the samples according to the different experimental conditions under investigation with the same discrimination capacity provided by traditional chemical analysis. [Copyright &y& Elsevier]