The genus Aeromonas has been described as comprising several species associated with the aquatic environment, which represents their principal reservoir. Aeromonas spp. are commonly isolated from diseased and healthy fish, but the involvement of such bacteria in human infection and gastroenteritis has frequently been reported. The primary challenge in establishing an unequivocal link between the Aeromonas genus and pathogenesis in humans is the extremely complicated taxonomy. With the aim of clarifying taxonomic relationships among the strains and phenotypes, a multilocus sequencing approach was developed and applied to characterize 23 type and reference strains of Aeromonas spp. and a collection of 77 field strains isolated from fish, crustaceans, and mollusks. All strains were also screened for putative determinants of virulence by PCR ( ast , ahh1 , act , asa1 , eno , ascV , and aexT ) and the production of acylated homoserine lactones (AHLs). In addition, the phenotypic fingerprinting obtained from 29 biochemical tests was submitted to the nonparametric combination (NPC) test methodology to define the statistical differences among the identified genetic clusters. Multilocus sequence typing (MLST) achieved precise strain genotyping, and the phylogenetic analysis of concatenated sequences delineated the relationship among the taxa belonging to the genus Aeromonas , providing a powerful tool for outbreak traceability, host range diffusion, and ecological studies. The NPC test showed the feasibility of phenotypic differentiation among the majority of the MLST clusters by using a selection of tests or the entire biochemical fingerprinting. A Web-based MLST sequence database ( http://pubmlst.org/aeromonas ) specific for the Aeromonas genus was developed and implemented with all the results.

An appropriate seafood origin identification is essential for labelling regulation but also economic and ecological issues. Near infrared (NIRS) reflectance spectroscopy was employed to assess the origins of cuttlefish caught from five fishing FAO areas (Adriatic Sea, northeastern and eastern central Atlantic Oceans, and eastern Indian and western central Pacific Oceans). A total of 727 cuttlefishes of the family Sepiidae (Sepia officinalis and Sepiella inermis) were collected with a portable spectrophotometer (902–1680 nm) in a wholesale fish plant. NIR spectra were treated with standard normal variate, detrending, smoothing, and second derivative before performing chemometric approaches. The random forest feature selection procedure was executed to select the most significative wavelengths. The geographical origin classification models were constructed on the most informative bands, applying support vector machine (SVM) and K nearest neighbors algorithms (KNN). The SVM showed the best performance of geographical classification through the hold-out validation according to the overall accuracy (0.92), balanced accuracy (from 0.83 to 1.00), sensitivity (from 0.67 to 1.00), and specificity (from 0.88 to 1.00). Thus, being one of the first studies on cuttlefish traceability using NIRS, the results suggest that this represents a rapid, green, and non-destructive method to support on-site, practical inspection to authenticate geographical origin and to contrast fraudulent activities of cuttlefish mislabeled as local.