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This analysis finds good accuracy for fat and protein in raw milk, suggesting reliable inline compositional monitoring.
The dairy industry has recognised the importance of compositional analysis of raw milk. Near-infrared (NIR) spectroscopy is a promising tool for real-time analysis of raw milk composition, supporting value-added in manufacturing as well as product-specific yields and quality. Therefore, the objective of this study was to evaluate an NIR spectrometer attached to a transflectance probe via optical fibre to quantify the macro-composition of raw milk under both laboratory and inline conditions at different flow rates. A total of 350 raw milk samples were collected to develop calibration models for fat, protein, lactose, total solids, and casein. The results showed good prediction accuracy for fat (root mean square error of prediction, [RMSEP] = 0.12%), protein (RMSEP = 0.09%), total solids (RMSEP = 0.18%), casein (RMSEP = 0.06%), and lactose (RMSEP = 0.09%) under laboratory conditions. However, prediction accuracy for inline measurement decreased significantly for total solids (RMSEP = 0.36%) and lactose (RMSEP = 0.30%), although it remained similar for other components. Flow rate variability during inline measurement did not affect prediction performance; however, repeated measurements showed deviations in predicted results, indicating slightly lower repeatability.
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Khan et al. (2025) studied this question.
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