In this study (open access) researchers developed and piloted a single in-line sensor to classify yoghurt as either sheep, goat or milk origin and simultaneously check viscosity and pH Quality Attribute Specifications. Their goal is a rapid in-line sensor that incorporates automated decision making, for routine use in the dairy industry.
Their reference dataset was sourced directly from two reputable Spanish companies and included both pasteurised and UHT yoghurts.
They found that the animal origin of milk could be predicted by building models based on the spectral data between 400 and 600 nm whilst viscosity and pH could be predicted by building models based on the spectral data between 800 and 1800 nm. To identify the animal origin of milk, they used Partial Least Squares-Discriminant Analysis (PLS-DA), achieving 100 % accuracy (95 % confidence interval). The model used to predict pH and viscosity was built with Partial Least Squares Regression (PLSR). The predictive power was generally very good (MSE=0.04–0.06; R2=0.94–0.96; MAE=0.16–0.17).
They conclude that their study demonstrates that the proposed spectroscopic method offers a more efficient approach for the simultaneous prediction of pH, viscosity, and milk origin in yogurt compared to existing methods, that require separate and slower analyses. Further work still needs to be carried out to optimize the model and achieve real-time monitoring that enables automated decision-making.
[picture – from the publication]
