Rapid assessment and prediction of microbiological quality of raw milk using machine learning based on RGB-colourimetric resazurin assay

• Published in: International dairy journal Vol. 146; p. 105750
• Main Authors: Thanasirikul, Chayapon, Patumvan, Atit, Lipsky, David, Bovonsombut, Sakunnee, Singjai, Pisith, Boonchieng, Ekkarat, Chitov, Thararat
• Format: Journal Article
• Language: English
• Published: 01.11.2023
• Subjects: class; color; data collection; microbiological quality; milk; milk quality; prediction; rapid methods; raw milk; support vector machines
• ISSN: 0958-6946
• DOI: 10.1016/j.idairyj.2023.105750

The feasibility of adapting an RGB-based colour sensor to determine raw milk quality based on the resazurin test was demonstrated and a model for rapid milk quality prediction proposed. One hundred and two raw milk samples containing different microbial concentrations were subject to a resazurin assay in a six-well microplate configuration and then to the colourimetric device to measure the resazurin test colours as red, green, and blue. A dataset of RGB colour measurements and corresponding microbial concentrations was created, from which a machine learning model for milk quality prediction was developed. A support vector machine model that was considered most suitable for this purpose demonstrated 100% prediction accuracy for milk with acceptable or “low” microbial concentrations (<5.0 × 10⁵ cfu mL⁻¹) and 96% accuracy for milk with unacceptable or “high” microbial concentrations (>1.0 × 10⁷ cfu mL⁻¹) but was less accurate for milk in the intermediate class.