Mastitis significantly impacts both the yield and quality of milk. The somatic cell count (SCC) and differential somatic cell count (DSCC), which are related to immune cells, are primary indicators for assessing mammary gland health. In this study, eight previously established mid-infrared spectroscopy models were utilized to predict the content of milk protein fractions (αs1-CN, β-CN, κ-CN, total CN, α-LA, β-LG, IgG, and LF) in milk samples from 21,388 lactating cows across 33 herds. Four linear mixed models were applied to analyze the secretion patterns of milk protein fractions by days in milk (DIM) and parity, their variations under different mastitis conditions, and their associations with the somatic cell score (SCS), DSCC, and immune cell counts (PMN + LYM score (PMN + LYMS) and MAC score (MACS)). The primary findings of the investigation comprised the following: (1) IgG was higher in early lactation, decreased with advancing lactation days, and slightly increased in late lactation, while seven other protein factions decreased from early to peak lactation and increased during mid-to-late lactation. Parity influenced all milk protein fractions except αs1-CN, with total CN, β-CN, and α-LA decreasing and κ-CN, β-LG, IgG, and LF increasing as parity increased (p < 0.05). (2) Mastitis significantly reduced the milk yield, fat percentage, protein percentage, and the contents of total CN, β-CN, κ-CN, and α-LA while increasing β-LG, IgG, and LF. (3) The SCS was negatively correlated with milk yield and α-LA but positively correlated with the fat percentage, protein percentage, κ-CN, β-LG, IgG, and LF. (4) When the DSCC increased to 50%, the milk yield decreased, while the milk protein percentage and κ-CN content significantly increased (p < 0.05). When the DSCC exceeded 50%, the fat percentage, protein percentage, total casein, αs1-CN, β-CN, κ-CN, β-LG, IgG, and LF decreased, while the α-LA content increased (p < 0.05). (5) When the PMN + LYMS increased, the milk yield and α-LA content rose, while the milk fat percentage, the milk protein percentage, and the contents of αs1-CN, β-CN, κ-CN, total CN, β-LG, IgG, and LF decreased (p < 0.05). Conversely, when the MACS increased, the milk yield and α-LA content declined, whereas the milk fat percentage, the milk protein percentage, and the contents of αs1-CN, β-CN, κ-CN, total CN, β-LG, IgG, and LF increased (p < 0.05). This study offers valuable insights into enhancing milk product quality, advancing the early diagnosis and mechanistic research of bovine mastitis, and the sustainable development of the dairy farming industry.

Fourier Transform Mid-Infrared Spectroscopy (FT-MIRS) can be used for quantitative detection of milk components. Here, milk samples of 458 Chinese Holstein cows from 11 provinces in China were collected and we established a total of 22 quantitative prediction models in milk fatty acids by FT-MIRS. The coefficient of determination of the validation set ranged from 0.59 (C18:0) to 0.76 (C4:0). The models were adopted to predict the milk fatty acids from 2138 cows and a new high-throughput computing software HiBLUP was employed to construct a multi-trait model to estimate and analyze genetic parameters in dairy cows. Finally, genome-wide association analysis was performed and seven novel SNPs significantly associated with fatty acid content were selected, investigated, and verified with the FarmCPU method, which stands for “Fixed and random model Circulating Probability Unification”. The findings of this study lay a foundation and offer technical support for the study of fatty acid trait breeding and the screening and grouping of characteristic dairy cows in China with rich, high-quality fatty acids. It is hoped that in the future, the method established in this study will be able to screen milk sources rich in high-quality fatty acids.

Fatty acid is an important factor affecting the nutritional quality of milk. In this study, we collected and assessed 78,086 milk samples from 12,065 Chinese Holstein cows from 11 farms in Northern China from November 2019 to September 2022. The contents of eight fatty acid groups were predicted using FT-MIRS-based models. The contents of TFAs, SFAs, UFAs, MUFAs, PUFAs, and LCFAs in milk reached the highest at 96–125 DIM, and SCFA and MCFA contents reached the highest at 276–305 DIM. With the increase in somatic cell score, the contents of various fatty acid groups in milk gradually decreased, and the nutritional value of milk and flavor of dairy products gradually deteriorated. The contents of high-quality fatty acids in milk, particularly UFAs and MUFAs, were significantly higher in the non-pregnant state than in the pregnant state. However, SCFA and MCFA contents exhibited the opposite pattern. Our findings provided valuable information on the content and distribution range of fatty acid groups in milk from Chinese Holstein cows. Further analysis is warranted to explore the breeding of Chinese Holstein cows providing milk with abundant beneficial fatty acids.