Effect of the manufacturing process on Fiore Sardo PDO cheese microstructure by multi-frequency NMR relaxometry

Differentiation among dairy products by combination of fast field cycling NMR relaxometry data and chemometrics

A set of commercial milk and Sicilian cheeses was analysed by a combination of fast field cycling (FFC) nuclear magnetic resonance (NMR) relaxometry and chemometrics. The NMR dispersion (NMRD) curves were successfully analysed with a mathematical model applied on Parmigiano-Reggiano (PR) cheese. Regression parameters were led back to the molecular components of cheeses (water trapped in casein micelles, proteins and fats) and milk samples (water belonging to hydration shells around dispersed colloidal particles of different sizes and bulk water). The application of chemometric analysis on relaxometric data enabled differentiating milk from cheeses and revealing differences within the two sample groups of either cheeses or milk samples. Marked differences among cheeses were evidenced by statistical analysis of the sole quadrupolar peaks parameters, suggesting that these contain information on the nature of the milk used during cheese production. Hence, combination of FFC NMR and chemometrics represents a powerful tool to investigate alterations in dairy products.

Quality Control in Fiore Sardo PDO Cheese: Detection of Heat Treatment Application and Production Chain by MRI Relaxometry and Image Analysis

Fiore Sardo (FS), a traditional Italian cheese, is present in the market as a heterogeneous variety of products. The use of heat-treated (HT) milk is forbidden by the official production protocol, but no official analytical method able to detect heat application is yet available. Here, a combined magnetic resonance imaging (MRI) relaxometry and image analysis approach to recognize FS made from raw milk is presented. Artisanal FS cheeses were produced from raw milk (RC) by five shepherds in accordance with the official protocol. They were compared to HT-milk counterparts (HTC). Additionally, industrially manufactured commercial FS cheeses (I) were also purchased and compared to RC and HTC. Relaxometry data of FS indicated the presence of two water populations; the ratio of characteristic relaxation time constant T2 and area fraction (Score, Ṩ) of the fastest relaxing population was used to compare RC, HTC and I samples. RC from HTC were successfully discriminated, the latter exhibiting lower Ṩ (enhanced protein hydration). I cheeses exhibited the lowest Ṩ values, sometimes comparable to HTC. Since visual appearance of RC and HTC is appreciably different, an image analysis deep learning approach using MRI and photographic pictures was adopted to discriminate the two productions, with promising percentages (>93%).