Hempseed oil (HSO) is extremely rich in unsaturated fatty acids, especially linoleic (18:2 n-6) and α-linolenic (18:3 n-3) acids, which determine its high sensitivity to oxidative and photo-oxidative degradations that can lead to rancidity despite the presence of antioxidant compounds. The aim of this work was to evaluate which material/temperature/light solutions better preserve HSO quality during its shelf life and to test NIR as a rapid, non-destructive technique for monitoring oxidation phenomena. Futura 75 hemp seeds were cold-pressed; the oil was packed into 20 mL vials of four different materials (polypropylene, clear glass, amber glass, and amber glass coated with aluminum foil) and stored for 270 days at 25 °C under diffused light and at 10 °C in dark conditions., Peroxides and conjugated dienes and trienes were evaluated at intervals to monitor oil stability. Moreover, NIR spectra were measured in transmission, and the sample dataset was analyzed using ASCA to test the significance of the experimental factors: the model showed the significance of all factors and of all the simple interactions. Our results demonstrate that oil stored in amber glass vials with aluminum foils at refrigerated temperatures receive the highest protection from environmental conditions, mitigating oxidative changes, and that the NIR technique could be used to rapidly monitor HSO oxidation parameters.

Proteomics and metabolomics are analytic tools used in combination with bioinformatics to study proteins and metabolites which contribute to describing complex biological systems. The growing interest in research concerning the resolution of these systems has stimulated the development of sophisticated procedures and new applications. This paper introduces the evolution of statistical techniques for the treatment of data, suggesting the possibility to successfully characterize the milk-whey syneresis process by applying two-dimensional correlation analysis (2DCOR) to a series of CE electropherograms referring to milk-whey samples collected during cheese manufacturing. Two cheese-making processes to produce hard cheese (Grana type) and fresh cheese (Crescenza) were taken as models. The applied chemometric tools were shown to be useful for the treatment of data acquired in a systematically perturbed chemical system as a function of time.

Extensive grazing in alpine environments represents a livestock management strategy that contributes to landscape conservation, animal welfare, and allows the production of high-quality dairy products. Digital technologies offer a potential solution to these challenges by supporting farmers and facilitating generational turnover. In this work, 20 lactating cows were monitored during the grazing season using GPS devices to track movements across the pasture area and activometers composed of an accelerometer-based collar that recorded cow neck movements continuously, classifying them into three possible behaviours: rumination, feeding and resting. Data provided by the activometers were collected with a resolution of 15 min intervals, reporting the duration of each activity, while GPS data were collected at an average frequency of 5 min. GPS data were filtered for potential outliers and subsequently aligned with activometers data through a Visual Basic for Applications (VBA) algorithm. This data fusion optimization enabled the creation of a robust integrated dataset in which each GPS position was associated with the animal’s predominant activity, allowing the analysis of grazing patterns and the generation of high-resolution grazing maps. The combination of these two digital tools proved effective in assessing animal behaviour in mountainous environments, providing operational indicators to support pasture management and improve forage biomass utilisation.