Nigeria's dairy industry is on the cusp of a major technological leap as advances in artificial intelligence (AI) combined with spectroscopic sensing are set to revolutionize how milk quality is monitored and assured across the value chain.
The dairy sector in Nigeria has long faced the challenges of improving the quantity and quality of domestically produced milk and ensuring consumer safety in a market where adulteration and contamination remain concerns.
In a pioneering study titled “SVR Chemometrics to Quantify β-Lactoglobulin and α-Lactalbumin in Milk by Mid-Infrared Spectroscopy (MIR)”, Nigerian researcher Habib Babatunde demonstrated how AI can revolutionize milk quality testing, ushering in a new era of accuracy, speed and transparency in dairy analysis.
Habib Babatunde is a researcher and data science scholar at Boise State University, Idaho, USA.
According to him, the study applies Support Vector Regression (SVR), an advanced machine learning algorithm, to predict the concentration of two important milk proteins: β-lactoglobulin and α-lactalbumin using mid-infrared (MIR) spectroscopy.
“These proteins are important indicators of milk quality, nutritional value and suitability for industrial uses such as infant formula and dairy-based foods.”
Traditionally, the dairy industry relies on partial least squares (PLS) regression for spectral analysis and quality prediction. While PLS has been the global standard for decades, it often struggles with non-linear patterns in biological systems like milk, reducing accuracy for complex samples.
Babatunde's research showed that SVR can overcome these limitations, providing higher accuracy, robustness, and adaptability than traditional PLS methods. Although SVR has shown success in scientific and industrial fields, its application in milk analysis has been limited.
Nigeria's dairy sector faces challenges in quality assurance, safety and traceability. With more than 60 percent of dairy products imported, local producers often struggle with adulteration, contamination, and inefficiencies in collection and processing.
Babatunde's approach provides a cost-effective, reagent-free and real-time solution that can be deployed even on small farms or cooperative dairy centres. Using MIR spectroscopy with SVR, milk can be analyzed immediately using light-based detection, without the need for expensive chemicals or laboratory infrastructure.
“Our goal is to bring laboratory-grade milk analysis to the farm gate,” Babatunde said. “AI-powered spectroscopy can help Nigeria detect adulteration early, monitor milk protein quality in real-time, and restore consumer confidence in locally produced dairy products.”
The study also calls for collaboration between Nigerian universities, dairy cooperatives and agri-tech startups to develop portable MIR devices powered by AI models like SVR. Such innovations can help in monitoring milk quality directly at collection points in dairy producing areas like Oyo, Kaduna and Plateau states.
By adopting this technology, Nigeria can strengthen its dairy value chain, reduce import dependence and advance the national dairy policy goal of achieving local milk self-sufficiency and food security.
“Nigeria has the talent and scientific capacity to lead Africa in digital agriculture and food analytics,” Babatunde said. “With the right support, we can move from being import-dependent to being innovation-driven.”
