aceSample-efficient machine learning for dairy pathogen detection and origin traceability under multivariable conditions using gold nanoparticle colorimetry
Storage temperature, humidity, and time jointly influence pathogen growth in dairy products, yet most rapid detection methods are validated under single-variable conditions that do not represent real supply chain scenarios. Testing across these combined conditions sharply increases the number of samples needed, whereas dairy safety studies usually work with small data sets, which limits the use of conventional machine learning. Here we combined gold nanoparticle colorimetric sensing, a time-temperature-humidity…
A sample-efficient Random Forest model trained on colorimetric readings predicted pathogen concentration and identified dairy pathogen species and production region with high accuracy under combined storage conditions.
Because each region was represented by a single commercial product per type and one production batch, the results are only proof-of-concept and may not generalize to wider supply chains.
Each region was represented by a single commercial product per type and a single production batch, so the findings are proof-of-concept and not validated across wider products or batches.
Evidence
- Peer-reviewedJournal of Dairy Science2026-10-09
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Truvace Impact Record TRV-2026-1875, v1: “Sample-efficient machine learning for dairy pathogen detection and origin traceability under multivariable conditions using gold nanoparticle colorimetry.” Truvace, 2026-10-19. /record/TRV-2026-1875 (accessed at citation time). sha256 e9e52321123abdb1…
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