Nigeria’s dairy industry is on the cusp of a major technological leap as advances in artificial intelligence (AI) coupled with spectroscopic sensing are poised to revolutionise how milk quality is monitored and assured across the value chain.
The dairy sector in Nigeria has long faced the challenges of improving the volume 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 Using Mid-Infrared Spectroscopy (MIR),” Habeeb Babatunde, a Nigerian Researcher, demonstrated how AI can revolutionise milk quality testing, ushering in a new era of precision, speed, and transparency in dairy analysis.
Habeeb Babatunde is a researcher and a 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 vital milk proteins: β-lactoglobulin and α-lactalbumin, using Mid-Infrared (MIR) spectroscopy.
“These proteins are crucial 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 the nonlinear patterns in biological systems like milk, reducing accuracy for complex samples.
Babatunde’s research demonstrated that SVR can overcome these limitations, delivering higher accuracy, robustness, and adaptability than conventional PLS methods. Although SVR has shown success across scientific and industrial fields, its application in milk analysis has remained limited.
Nigeria’s dairy sector continues to face challenges in quality assurance, safety, and traceability. With over 60 percent of dairy products imported, local producers often struggle with adulteration, contamination, and inefficiencies in collection and processing.
Babatunde’s approach offers a cost-effective, reagent-free, and real-time solution that could be deployed even at smallholder farms or cooperative dairy centers. Using MIR spectroscopy coupled with SVR, milk can be analyzed instantly using light-based detection, without requiring expensive chemicals or laboratory infrastructure.
“Our goal is to bring laboratory-grade milk analysis to the farm gate,” said Babatunde. “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 could help monitor milk quality directly at collection points in dairy-producing regions such as Oyo, Kaduna, and Plateau States.
By embracing this technology, Nigeria can strengthen its dairy value chain, reduce import dependence, and advance the National Dairy Policy’s 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 added. “With the right support, we can move from being import-dependent to innovation-driven.”
