Correlation between the dielectric properties and the physicochemical characteristics and proximate composition of whole, semi-skimmed and skimmed sheep milk using chemometric tools

Dielectric spectroscopy technology combined with machine learning methods for nondestructive detection of protein content in fresh milk

To quickly achieve nondestructive detection of protein content in fresh milk, this study utilized a network analyzer and an open coaxial probe to analyze the dielectric spectra of milk samples at 100 frequency points within the 2-20 GHz range, focusing on the dielectric constant ε' and the dielectric loss factor ε''. Feature variables were extracted from the full dielectric spectra using the successive projections algorithm (SPA), uninformative variables elimination (UVE), and the combined UVE-SPA method. These variables were then used to develop partial least squares regression (PLSR), support vector machine (SVM), decision tree (DT), random forest (RF), and least squares boosting (LSBOOST) models for predicting protein content. The results showed that ε' decreased monotonically with increasing frequency, while ε'' increased monotonically. The UVE-SPA method for feature extraction demonstrated superior performance, with the UVE-SPA-PLSR model being the best for predicting milk protein content, achieving the highest RC 2 = 0.998 and RP 2 = 0.989 and the lowest RMSEC = 0.019% and RMSEP = 0.032%. This study provides a theoretical reference for evaluating milk quality and developing intelligent detection equipment for natural milk.

Characterization and Comparative Evaluation of Milk Protein Variants from Pakistani Dairy Breeds

The aim of study was to scrutinize the physicochemical and protein profile of milk obtained from local Pakistani breeds of milch animals such as Nilli-Ravi buffalo, Sahiwal cow, Kajli sheep, Beetal goat and Brela camel. Physicochemical analysis unveiled maximum number of total solids and protein found in sheep and minimum in camel. Buffalo milk contains the highest level of fat (7.45%) while camel milk contains minimum (1.94%). Ash was found maximum in buffalo (0.81%) and sheep (0.80%) while minimum in cow's milk (0.71%). Casein and whey proteins were separated by subjecting milk to isoelectric pH and then analyzed through sodium dodecyl sulphate-polyacrylamide gel electrophoresis (SDS-PAGE). The results showed heterogeneity among these species. Different fractions including α_S1, α_S2, κ-casein, β-casein and β-lactoglobulen (β-Lg) were identified and quantitatively compared in all milk samples. Additionally, this electrophoretic method after examining the number and strength of different protein bands (α_S1, α_S2, β-CN, α-LAC, BSA, and β-Lg, etc.), was helpful to understand the properties of milk for different processing purposes and could be successfully applied in dairy industry. Results revealed that camel milk was best suitable for producing allergen free milk protein products. Furthermore, based on the variability of milk proteins, it is suggested to clarify the phylogenetic relationships between different cattle breeds and to gather the necessary data to preserve the genetic fund and biodiversity of the local breeds. Thus, the study of milk protein from different breed and species has a wide range of scope in producing diverse protein based dairy products like cheese.

Thermal properties and volatile compounds profile of commercial dark-chocolates from different genotypes of cocoa beans (Theobroma cacao L.) from Latin America

There is a growing interest in the identification of chemometric markers that allow the distinction and authentication of dark-chocolates according to their cocoa geographical origin and/or genotype. However, samples derived from Latin American cocoa, including specimens from North and South America, have not been studied in this context. An exploration of the melting behavior, fat composition, bioactive content, and volatile profile of commercial darkchocolates was conducted to identify possible patterns related to the genotype and/or origin of cocoa from Latin America. The melting properties were evaluated by DSC and related to fat content and fatty acids profile. Total polyphenol, anthocyanin, methylxanthine, and catechin content were analyzed. Finally, the volatile compounds were extracted and identified by HS-SPME/GC-MS and were analyzed through Principal Component Analysis (PCA) and the Hierarchical Cluster Analysis Heatmap (HCA Heatmap). The fatty acids profile showed a relationship with the melting properties of dark chocolate. The samples exhibited two glass-transition temperatures (T_g) at ≈19 °C and ≈25.5 °C, possibly related to traces of unstable polymorphic forms of monounsaturated triacylglycerides. The analysis of bioactive compounds demonstrated great variability among samples independent of the cocoa origin, genotype, and content. The PCA and HCA Heatmaps allowed discriminating against the chocolates in relation to the cocoa origin and genotype. Compounds like tetramethylpyrazine, trimethylpyrazine, benzaldehyde, and furfural could be considered as dark-chocolate aroma markers derived from Latin American cocoas (North American region). The 2-phenylethyl alcohol, 2-methylpropanoic acid, 2,3-butanediol, 2-nonanone, and limonene for derived from South America. And the 2-phenylethyl acetate, 3-methyl-butanal, and cinnamaldehyde could allow to distinguishing between regional genotypes.