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Updated: Sep 5, 2026

An Efficient Single-Person Technique for Milk Sampling from Laboratory Mice
Published on: March 28, 2025
Nonlinear relationships between somatic cell count and technological properties of bovine milk: A longitudinal
Emilia Bagnicka1, Paulina Brzozowska1, Adrianna Szprynca1
1Institute of Genetics and Animal Biotechnology Polish Academy of Sciences, Postępu 36A, Jastrzębiec, Poland.
Abstract:
This longitudinal observational study investigated the impact of somatic cell count (lnSCC) on milk composition and technological traits in 332 clinically healthy cows. Over 2 years, 865 samples were collected at 4 lactation stages. The analysis covered milk yield, major components (fat, protein, lactose, FFA, citric acid, urea contents), and processing-related traits (casein and TS contents, acidity, pH, coagulation time, and curd/whey quality and curd yield). The mixed model included cow as a random effect, while HYS, HYM, parity, and milking time were fixed effects. DIM (quadratic) and lnSCC (linear, quadratic, cubic) served as fixed covariates. Results showed that lnSCC significantly affected 20 out of 26 studied traits, often through nonlinear relationships. While total protein and casein concentrations initially rose, their percentages peaked at approximately 1,187,000 and 348,000 cells/mL, respectively, before declining. The casein-to-protein ratio reached its maximum at only 37,000 cells/mL. A cubic effect was a significant predictor for fat content, titratable acidity, and curd/whey quality. Notably, curd quality began to deteriorate at a threshold as low as 22,600 cells/mL, while total solids (TS) contents reached a maximum at 385,000 cells/mL. Beyond these physiological thresholds, the technological value of milk dropped sharply, alongside a significant linear decline in milk yield and citric acid. These findings demonstrate that early alterations in milk integrity occur well below traditional regulatory limits; however, these values represent model-derived mathematical estimates that require independent validation before any practical diagnostic or regulatory application.
