Prediction of milk composition using multivariate chemometric modelling of infrared, Raman, and fluorescence spectroscopic data: A review
Saeedeh Mohammadi, Aoife Gowen, Jiani Luo, Colm P. O’Donnell
Abstract
Quality assessment of milk which is a comprehensive source of nutrients for humans and an important raw material for other dairy products is required in the dairy industry. Rapid, cost-effective, and non-destructive spectroscopic techniques are more preferable than classic wet chemistry approaches for milk analysis. The objective of this work was to review the prediction of milk composition including macronutrients such as fat, protein and lactose and micronutrients such as fatty acids and vitamins using multivariate chemometric modelling of Near Infrared (NIR), Mid Infrared (MIR), fluorescence, and Raman spectral data and data fusion approaches. Literature sources describing spectroscopic analysis of milk samples and the application of multivariate data analysis methods are outlined in this literature review. In addition, the importance of data fusion strategies employed for combing different spectroscopic techniques are reviewed to evaluate their potential to improve the accuracy of the prediction models developed. Recent research studies have demonstrated that the use of data fusion strategies improves the performance of milk composition prediction models developed.