Leveraging milk mid-infrared spectroscopy to authenticate animal welfare, farming practices, and dairy systems of Parmigiano Reggiano cheese.

Increasing consumer concerns underscore the importance of verifying the practices and origins of food, especially certified premium products. The aim of this study was to evaluate the ability of Fourier-transform mid-infrared (FT-MIR) spectroscopy to authenticate animal welfare parameters, farming practices, and dairy systems. Data on farm characteristics were obtained from the Parmigiano Reggiano Consortium in northern Italy.

Characterization of the Detailed Fatty Acid Profiles of a Large Number of Types of Cheese from the Mountains and Plains.

The aim of this study was to characterize the highly detailed fatty acid (FA) profiles of 258 cheeses of 18 different categories of cheese collected in the mountains and on the plains of the Veneto region (Italy). The results clearly showed that, aside from the distinctive FA profiles of goat cheeses (more short-chain FAs and fewer MUFAs), the three categories of Formaggio di Malga (artisanal cheeses produced on temporary summer farms on Alpine pastures where transhumance is practiced) were very different from the other cheese categories in terms of their much higher CLA and omega-3 contents. Two categories of cheese from permanent farms in the mountains (Morlacco del Grappa and Monte Veronese PDO) were intermediate, and two other categories of cheeses originating in the mountains (Asiago PDO and Montasio PDO), but now produced mainly on the plains, were not distinguishable from the other cheese categories.

Added Value of Products from Endangered Local Sheep Breeds in Mountain Areas.

Local sheep breeds in the Italian eastern Alps passed from ex situ to in situ conservation. These breeds are mainly reared by smallholders in low-input farming systems. To allow the sustainable use of genetic resources, the economic sustainability of farmers must be supported through production guidelines.

Infrared spectroscopy coupled with machine learning algorithms for predicting the detailed milk mineral profile in dairy cattle.

Milk minerals are not only essential components for human health, but they can be informative for milk quality and cow's health. Herein, we investigated the feasibility of Fourier Transformed mid Infrared (FTIR) spectroscopy for the prediction of a detailed panel of 17 macro, trace, and environmental elements in bovine milk, using partial least squares regression (PLS) and machine learning approaches. The automatic machine learning significantly outperformed the PLS regression in terms of prediction performances of the mineral elements.

Detailed mineral profile of milk, whey, and cheese from cows, buffaloes, goats, ewes, and dromedary camels, and efficiency of recovery of minerals in their cheese.

Milk and dairy products are important in the human diet not only for the macronutrients, such as proteins and fats, that they provide, but also for the supply of essential micronutrients, such as minerals. Minerals are present in milk in soluble form in the aqueous phase and in colloidal form associated with the macronutrients of the milk. These 2 forms affect the nutritional functions of the minerals and their contribution to the technological properties of milk during cheese making.