Improving the Added Value of Raw Milk and Dairy Products through Innovative Tools and Techniques

Natural selection and domestication have shaped modern sheep populations into a vast range of phenotypically diverse breeds. Among these breeds, dairy sheep have a smaller population than meat sheep and wool sheep, and less research is performed on them, but the lactation mechanism in dairy sheep is critically important for improving animal-production methods. In this study, whole-genome sequences were generated from 10 sheep breeds, including 57 high-milk-yield sheep and 44 low-milk-yield sheep, to investigate the genetic signatures of milk production in dairy sheep, and 59,864,820 valid SNPs (Single Nucleotide Polymorphisms) were kept after quality control to perform population-genetic-structure analyses, gene-detection analyses, and gene-function-validation analyses. For the population-genetic-structure analyses, we carried out PCA (Principal Component Analysis), as well as neighbor-joining tree and structure analyses to classify different sheep populations. The sheep used in our study were well distributed in ten groups, with the high-milk-yield-group populations close to each other and the low-milk-yield-group populations showing similar classifications. To perform an exact signal-selection analysis, we used three different methods to find SNPs to perform gene-annotation analyses within the 995 common regions derived from the fixation index (F_ST), nucleotide diversity (Ɵπ), and heterozygosity rate (ZHp) results. In total, we found 553 genes that were located in these regions. These genes mainly participate in the protein-binding pathway and the nucleoplasm-interaction pathway, as revealed by the GO- and KEGG-function-enrichment analyses. After the gene selection and function analyses, we found that FCGR3A, CTSK, CTSS, ARNT, GHR, SLC29A4, ROR1, and TNRC18 were potentially related to sheep-milk-production traits. We chose the strongly selected genes, FCGR3A, CTSK, CTSS, and ARNT during the signal-selection analysis to perform a RT-qPCR (Reale time Quantitative Polymerase Chain Reaction) experiment to validate their expression-level relationship with milk production, and the results showed that FCGR3A has a significant negative relationship with sheep-milk production, while other three genes did not show any positive or negative relations. In this study, it was discovered and proven that the candidate gene FCGR3A potentially contributes to the milk production of dairy sheep and a basis was laid for the further study of the genetic mechanism underlying the strong milk-production traits of sheep. Full article

Ruminant milk is an essential part of the human diet and is widely accepted as a major nutrient source in developing countries. However, the polar and neutral lipid content variation in milk fat globules (MFG)among cow, goat, and water buffalo is poorly understood. This study used high-performance thin layer chromatography to identify and quantify five major polar (PL) and three neutral lipids (NL) from the MFG of cow, goat, and water buffalo. Optimal separation was achieved for PLs using chloroform: methanol: water (65:25:4), and hexane: diethyl ether: acetic acid (70:30:1) for NLs. The lower detectable (0.12 to 1.53 μg/mL) and quantification (0.12 to 1.53 μg/mL) limits indicated the high sensitivity of the method. Quantification at 540 nm showed the highest abundance of phosphatidylethanolamine and triglycerides. Fat globules were further characterized for size and microstructural properties, which revealed smaller globules in goats (0.99 ± 0.04 μm) than cows (1.85 ± 0.03 μm) and water buffaloes (2.91 ± 0.08 μm), indicating a negative correlation with PL but a positive correlation with NL. The variation in lipid quantity among different animal species suggests more research to support their selection as a suitable source for developing functional food to impact human health positively. Full article

The production of lactic acid (LA) through the microbial conversion of agro-industrial residuals is an important process in the biotechnology industry. The growth kinetics of 30 strains of lactic acid bacteria (LAB) isolated from agro-industrial residues were determined and nine strains were selected for microbioreactor fermentation. Lactiplantibacillus pentosus_70-1 (1.662) and L. pentosus_19-2 (1.563) showed the highest OD₆₀₀ values, whereas the highest growth rates were observed for L. pentosus_19-2 (0.267 h⁻¹) and Weissella soli_31 (0.256 h⁻¹). The production of LA and acetic acid (AA), glucose consumption, and metabolic profiles were determined, without finding significant differences in the production of LA; however, W. soli_29 produced the highest amount of LA (20.833 gL⁻¹) and was able to metabolize most of the studied carbohydrates. Based on these results, W. soli_29 was chosen for a 20 h fermentation in a 7 L bioreactor using both standard medium and milk whey supplemented medium. W. soli_29 produced 16.27 gL⁻¹ and 7.21 gL⁻¹ of LA in each of these mediums, respectively. These results show the underlying potential of Weissella strains for biotechnological applications. Additional analysis which should contemplate different agro-industrial residues and other conditions in bioreactors must be carried out. Full article

In this study, a multiple linear regression equation was developed to measure and predict quality parameters of unconcentrated and concentrated milk based on color components. The viscosity, density, pH, moisture, and fat content could be measured using image processing technology. The multiple linear regression model had a good fitting on experimental data considering the limited errors (0.00–1.12%), standard deviation (0.000–0.043), and root mean square error (0.0007–0.3721). Therefore, these models can be used to predict the quality parameters of milk, including fat percentage, pH, viscosity, density, and moisture content, based on color components of unconcentrated and concentrated milk. The maximum and minimum of color change were 12.28 and 5.96, respectively. The values of browning index were also well-predicted and were within the standard limits. The non-destructive and quick procedure that proposed in this study showed a percentage of accuracy in assessing and predicting the quality parameters milk based on color components. Overall, the color correlates with different compositional and physical characteristics, and provide a possible internet of things (IoT)-based approach to accompany the conventional approaches in the future after further evaluation at large scale for various types of milks subjected to various processes. Full article

In order to improve the flavor and taste of Camembert cheese, the use of Monascus as an adjunct starter for the production of Camembert-type cheese was studied to investigate its effect on the proteolysis, lipolysis, and volatile compounds during ripening for 40 days. The Camembert cheese without Monascus was used as a control. The results showed that proteolytic and lipolytic activities increased to a certain extent. The addition of Monascus promoted primary and secondary proteolysis, due to the release of some proteases by Monascus. Aspartic, Threonine, Glutamic, Glycine, Methione, Isoleucine, Phenyalanine, and Lysine contents in experimental group (R) cheese were significantly higher than those in control group (W) cheeses. In addition, the free amino acid and fatty acid contents were also affected. The identification of flavor components using gas-mass spectrometry (GC-MS) showed that 2-undecone, 2-tridecanone, phenylethyl alcohol, butanediol (responsible for the production of flowery and honey-like aroma), ethyl hexanoate, ethyl octanoate, and ethyl citrate (fruit-like aroma) were significantly higher (p < 0.05) in the experimental cheeses than in the control. The contents of 2-nonanone, 2-octanone and 2-decanone (showing milky flavor), and 1-octene-3 alcohol with typical mushroom-like flavor were lower than the control. Full article