Effectiveness of two different at-line instruments for the assessment of cheese composition, major minerals and fatty acids content

Comparison of chemical composition of organic and conventional Italian cheeses from parallel production

Although there are several studies comparing organic and conventional milk characteristics, very few focused on dairy processed products such as cheese. Thus, this study aimed for a detailed controlled examination of gross composition, minerals, and the fatty acid profile of organic (ORG) and conventional (CON) Italian cheeses from parallel production. Four Italian cheese types were analyzed: Latteria (ORG, n = 9; CON, n = 10); Asiago Protected Designation of Origin (PDO) fresco (ORG, n = 9; CON, n = 9); Caciotta (ORG, n = 8; CON, n = 8); and Mozzarella Traditional Specialty Guaranteed (TSG; ORG, n = 14; CON, n = 14). Cheese samples were collected from September 2020 to August 2021. Gross composition, minerals, and fatty acids were determined using infrared spectroscopy. Within each cheese type, paired ORG and CON samples were compared using a nonparametric Wilcoxon signed-rank test. Latteria showed lower PUFA, n-3, and n-6 content, and greater Fe, K, C10:0, C12:0, and C16:0 content in ORG than in CON. Asiago PDO fresco showed lower protein and Zn content and greater salt, ash, and Na content in ORG than in CON. Caciotta showed lower ash, n-3, and n-6 content and greater K, C4:0, C8:0, C10:0, C14:0, and C16:0 content in ORG than in CON. Mozzarella TSG showed lower fat and, therefore, fatty acid content, and greater moisture, ash, and Mg content in ORG than in CON. In conclusion, few significant differences in chemical composition were observed between ORG and CON cheeses, regardless of the type considered. Moreover, Asiago PDO fresco showed fewer significant differences between ORG and CON compared with Latteria, Caciotta, and Mozzarella TSG.

Selecting Milk Spectra to Develop Equations to Predict Milk Technological Traits

Including all available data when developing equations to relate midinfrared spectra to a phenotype may be suboptimal for poorly represented spectra. Here, an alternative local changepoint approach was developed to predict six milk technological traits from midinfrared spectra. Neighbours were objectively identified for each predictand as those most similar to the predictand using the Mahalanobis distances between the spectral principal components, and subsequently used in partial least square regression (PLSR) analyses. The performance of the local changepoint approach was compared to that of PLSR using all spectra (global PLSR) and another LOCAL approach, whereby a fixed number of neighbours was used in the prediction according to the correlation between the predictand and the available spectra. Global PLSR had the lowest RMSEV for five traits. The local changepoint approach had the lowest RMSEV for one trait; however, it outperformed the LOCAL approach for four traits. When the 5% of the spectra with the greatest Mahalanobis distance from the centre of the global principal component space were analysed, the local changepoint approach outperformed the global PLSR and the LOCAL approach in two and five traits, respectively. The objective selection of neighbours improved the prediction performance compared to utilising a fixed number of neighbours; however, it generally did not outperform the global PLSR.