
Milk Adulteration Indicators
Screening signals from milk physiochemical properties.
Intelligent screening for milk quality, cattle health and dairy traceability — helping identify abnormality closer to the point of milk collection.
We aim to build an analytical platform using AI/ML to identify issues and AI/ML-driven resolutions across the dairy journey — from cattle to consumer.
Milk is collected from individual farmers at collection centers. Fat and SNF are measured for payment, while farmers' milk is then mixed in containers and moved through the dairy supply chain.
When quality testing happens later at the processing plant, the mixed milk makes it difficult to identify the original farmer or source of an abnormal sample.
See the proposed approach →We upgraded the milk analyzer machines to capture additional measurements at the collection center so milk quality and cattle-health indicators can be screened closer to the point of origin.
Additional milk parameters (milk physiochemical properties) and AI/ML driven analytical models, create an early screening layer at the collection center.
Milk from an individual farmer is received.
Additional milk parameters are captured.
Mathematical and scoring models evaluate indicators.
Potential milk or cattle-health abnormalities are flagged.
Samples can be prioritized for standard diagnosis and action.
We have developed AI/ML driven mathematical and scoring models that generate indicators for further investigation.

Screening signals from milk physiochemical properties.

Early indicators that can support farmer education and corrective measures.
When an abnormality is found, the sample should be diagnosed according to the applicable standard process. The models help identify sources and narrow the sample size for corrective measures.
Track milk collected from individual dairy farmers and connect collection-point intelligence with the downstream dairy chain.





Capture milk parameters at the collection center in addition to Fat and SNF.
Apply physio-chemical relationships and analytical scoring to the sample.
Generate indicators for potential adulteration and cattle-health conditions.
Use the indicators to educate farmers and prioritize standard diagnostic action.
Our vision is to connect collection-center measurements with AI/ML analytical models, farmer-level history and traceability — creating a foundation for data-driven dairy analytics platform.
Our initiative has been strengthened through the support of leading institutions and participation in diverse accelerator programs.
RKVY-RAFTAARAgri-Business Incubator
DPIITDept. for Promotion of Industry and Internal Trade
I3F, IIT(BHU)Indian Institute of TechnologyWhether you are a dairy organization, collection network, technology partner, researcher or investor, let's discuss how Prashaar can support data-driven dairy intelligence and traceability with AI/ML capabilities.


