No abstract

The nutritional peculiarities of dairy products made with milk from pasture-fed ruminants would require a rapid control to be authenticated and limit the risk of fraud. In the current study, ninety milk samples from two groups of goats were analysed by electronic nose, quantitative descriptive sensory (QDA) and gas chromatography-mass spectrometry analysis with the aim of discriminating between milk produced on grazing and on a confinement feeding system. The raw milk samples were taken at five different times over a period of three months (April, May and June 2021) from eighteen individual Saanen goats divided into two groups, one of which was fed outdoors on a highly biodiverse pasture. Linear discriminant analysis (LDA), carried out on electronic nose data, was able to classify the two types of milk in terms of an animal feeding system (88% correct classification). Pasture milk scored higher for sensory descriptors such as “Grassy” and “Sweet aromatic” odours. Terpene compounds were the chemical class that qualitatively differentiates the pasture milk while volatile fatty acids were the most present quantitatively. Electronic nose has proven to be a rapid, reproducible and simple method for authenticating pasture raw milk in routine control analyses.

Sustainable production systems in line with consumer expectations are attractive for the dairy sector. The objective of this review is to examine the benefits of an Italian method, named the Noble Method ('Metodo Nobileâ'), in order to improve the nutritional properties of milk and environmental sustainability. The prohibition of silage and the use of polyphite pastures are some of the rules established by the Noble Method. The greater amount of unsaturated fatty acids and other beneficial compounds found in milk and dairy products produced by using milk from animals fed on wellmanaged pasture could have positive implications on consumers' health.

The traceability of the geographical origin of coffee is a challenging issue to protect producers and consumers from the risk of fraud. A total of 162 Arabica from Peru, Colombia and Brazil, and Robusta from India, Vietnam and Uganda, espresso coffee (EC) samples of different degrees of roasting (light, medium and dark) were characterized for physico-chemical features (lipids, solids, and chlorogenic acids) and analyzed via SHS-GC/MS analysis, with the aim of discriminating the samples according to their geographical origin. Linear discriminant analysis (LDA), performed on the data of the chemical classes of the volatile organic compounds (VOCs), was able to correctly identify 97.53% of the tested samples through cross-validation. The dark roasting of the coffee beans implied a higher quantity of volatile compounds in the headspace of the EC, belonging to chemical classes of furans, esters, N-heterocyclic and sulfur compounds, reducing the differences by geographical origin. Light- and medium-roasted Robusta EC showed a major contribution of pyrazines and pyrimidines, while aldehydes, alcohols and ketones were generally more representative in Arabica samples. The quantitative distribution of volatile compounds proved to be a useful tool to discriminate samples by geographical origin.