Cheese provides essential nutrients for human nutrition and health, such as minerals and fatty acids (FA). Its composition varies according to milk origin (e.g., species and breed), rearing conditions (e.g., feeding and management), and cheese-making technology (e.g., coagulation process, addition of salt, ripening period). In recent years, cheese production has increased worldwide. Italy is one of the main producers and exporters of cheese. This study aimed to describe mineral, FA, and cholesterol content of 133 samples from 18 commercial cheeses from 4 dairy species (buffalo, cow, goat, and sheep) and from 3 classes of moisture content (hard, 45%). Mineral concentrations of cheese samples were determined by inductively coupled plasma optical emission spectrometry, and FA and cholesterol contents were determined by gas chromatography. Moisture and species had a significant effect on almost all traits: the highest levels of Na, Ca, and Fe were found in cheeses made from sheep milk; the greatest level of Cu was found in cow milk cheese, the lowest amount of K was found in buffalo milk cheese, and the lowest amount of Zn was found in goat cheeses. In all samples, Cr and Pb were not detected (below the level of detection). In general, total fat, protein, and minerals significantly increased when the moisture decreased. Buffalo and goat cheeses had the highest saturated FA content, and sheep cheeses showed the highest content of unsaturated and polyunsaturated FA, conjugated linoleic acid, and n-3 FA. Goat and sheep cheeses achieved higher proportions of minor FA than did cow and buffalo cheeses. Buffalo cheese exhibited the lowest cholesterol level. Our results confirm that cheese mineral content is mainly affected by the cheese-making process, whereas FA profile mainly reflects the FA composition of the source milk. This study allowed the characterization of mineral and FA composition and cholesterol content and revealed large variability among different commercial cheeses.

Fatty acid (FA) profile plays an important role on human health and on sensory quality of dairy products. There is few information about breed influence on milk FA profile of local goat breeds. This study aimed to characterize and compare the milk FA profile of 5 local endangered goat breeds (Garganica, Girgentana, Jonica, Maltese, and Mediterranean Red) and a cosmopolitan breed (Saanen) reared in the same farm during a complete lactation. A total of 252 milk samples were collected monthly from 42 goats (7 goats per breed) and analyzed for gross composition and FA profile. Individual FA was determined using gas-chromatography. Data were analyzed using a mixed model with repeated measures with breed and week of lactation as fixed effects. Results showed that the FA profile was significantly affected by week of lactation and only few FA by breed effect (p < 0.05). Overall, the main differences were found between Saanen and local breeds. This study contributed to the characterization of goat milk FA profile, and it may be of interest for the valorization of milk from local goat breeds which seem to have a healthier profile than milk of the cosmopolitan breed.

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.

Very little information about local breed goat milk is available, which is relevant for biodiversity preservation and local cheese production. This study aimed to evaluate the effect of breed and week of lactation on milk mineral profile of five Italian local breeds (Garganica, Girgentana, Jonica, Maltese and Mediterranean Red) and a cosmopolitan breed (Saanen). Sixty goats (10 per breed) from an experimental farm were enrolled in the study and sampled every 2 weeks for milk gross composition analysis. In addition, an individual milk sample was collected monthly from 42 goats (seven goats per breed) for mineral determination through inductively coupled plasma optical emission spectrometry. Data were analysed using a mixed linear model with repeated measures, including breed and week of lactation as fixed effects. Week of lactation affected mineral contents, except for B, being lower in early than late lactation, whereas, breed affected only P, Mg and Zn. Mediterranean Red and Jonica breeds’ milk was richer in P than Maltese, and in Zn than Maltese, Girgentana and Saanen breeds. For Mg, only Saanen differed from Maltese. Such information might be useful for the valorisation of goat milk produced by autochthonous breeds.

Determining cocoa bean quality is crucial for many players in the international supply chain. However, actual methods rely on a cut test protocol, which is limited by its subjective nature, or on time-consuming, expensive and destructive wet-chemistry laboratory procedures. In this context, the application of near infrared (NIR) spectroscopy, particularly with the recent developments of portable NIR spectrometers, may represent a valuable solution for providing a cocoa beans’ quality profile, in a rapid, non-destructive, and reliable way. Monitored parameters in this work were dry matter (DM), ash, shell, fat, protein, total polyphenols, fermentation index (FI), titratable acidity (TA) and pH. Different chemometric analyses were performed on the spectral data and calibration models were developed using modified partial least squares regression. Prediction equations were validated using a fivefold cross-validation and a comparison between the different prediction performances for the portable and benchtop NIR spectrometers was provided. The NIRS benchtop instrument provided better performance of quantification considering the whole than the portable device, showing excellent prediction capability in protein and DM quantification. On the other hand, the NIRS portable device, although showing lower but valuable performance of prediction, can represent an appealing alternative to benchtop instruments for food business operators, being applicable in the field.