ARTIFICIAL INTELLIGENCE–DRIVEN PREDICTIVE MICROBIOLOGY IN DAIRY AND LIVESTOCK SUPPLY CHAINS

Abstract

This study had examined Artificial Intelligence Driven Predictive Microbiology in Dairy and Livestock Supply Chains using a quantitative, stage-based framework that integrated sensor-derived exposure histories, laboratory microbiology outcomes, operational process logs, and traceability context. The final analytic dataset had comprised 6,420 traceable lots (3,280 dairy and 3,140 livestock) drawn from four facilities, with facility contributions of 1,860, 1,710, 1,520, and 1,330 lots, respectively. A total of 18,760 microbiological observations had been retained across checkpoints, including 6,340 intake, 6,270 post-process, and 6,150 pre-dispatch observations. Sensor coverage had been 84.6% overall, and 16.6% of lots had contained at least one laboratory value recorded below detection thresholds. Descriptive patterns had shown that pre-dispatch segments had exhibited higher exposure variability than post-process segments, with mean temperature instability increasing from 1.4 ± 1.1 post-process to 3.1 ± 2.0 pre-dispatch and handling intensity increasing from 1.4 ± 2.1 to 4.6 ± 5.2 events per lot. Correlation analysis had shown positive associations between microbial counts and temperature instability (r = 0.34) and cumulative warm exposure (r = 0.29), with stage-specific correlations for instability of 0.26 at intake, 0.18 post-process, and 0.41 pre-dispatch. Reliability testing had indicated strong internal consistency for the cold-chain stability indicator (α = 0.88) and traceability completeness (α = 0.84), with moderate reliability for sanitation timing (α = 0.76). Collinearity diagnostics had identified redundancy within temperature features, including elevated variance inflation for maximum excursion (VIF = 6.9) and cumulative warm exposure (VIF = 5.6), supporting predictor consolidation. Mixed-effects regression had shown significant stage-dependent effects, including temperature instability coefficients of β = 0.18 (intake), β = 0.07 (post-process), and β = 0.24 (pre-dispatch). For exceedance prediction, facility-held-out benchmarking had improved from PR-AUC 0.24 to 0.39 and recall from 0.58 to 0.74, while shelf-life prediction error had declined from 2.8 to 1.9 days median absolute error.