Early-warning evaluation of pasteurized milk spoilage during storage and distribution

Hidden temperature deviations within the cold chain are a major contributor to global food waste, yet existing quality assurance methods predominantly rely on lagging indicators that fail to capture early-stage spoilage. Here, we integrate high-resolution foodomics with machine learning approaches to identify a biomarker for pasteurized milk freshness: N-((4-amino-2-methylpyrimidin-5-yl)methyl)formamide (AMF). Through structural elucidation and isotope tracing, we revealed that AMF is formed via a specific hydrolytic ring-opening pathway of thiamine. Importantly, perturbation experiments using sterile UHT milk, NaOH, and formic acid further suggested that alkaline microenvironments and spoilage-associated biochemical activity may facilitate this transformation. Validated on datasets from diverse geographic regions and processing methods, we established a quantitative spoilage warning threshold at 1.7 ng/mL, and the early-warning sensitivity allows detection of transient high-temperature exposures as brief as 6 h. This promising molecular indicator enables a shift in milk quality control from retrospective rejection to prospective prevention, acting as an indicator of thermal history and allowing precise, early detection of temperature abuse in complex dairy supply chains.