Rapid detection of neutralising acid adulterants in raw milk using a milk component analyser and chemometrics

Abstract

This study focused on the development of a method for the rapid detection of acid-neutralising adulterants in raw milk using a milk composition analyser. Qualitative analysis for the discrimination of different acid-neutralising acid adulterants in raw milk and quantification of NaSCN in adulterated raw milk were conducted, combined with chemometrics. The results showed that the milk component analyser combined with principal component analysis (PCA) could judge whether raw milk samples were adulterated but cannot identify the types of adulterated substances. Although partial least squares discrimination analysis (PLS-DA) can distinguish some adulterated raw milk samples, the accuracy rate was only 56.3%; the random forest (RF) model could recognise most adulterated raw milk samples with an accuracy rate of 97.5% and the F1-score was 0.9638. In the prediction model of NaSCN adulteration concentration in raw milk constructed by RF, the coefficient of determination (R²) was 0.9889, and the root means square error (RMSE) was 3.28 × 10⁻⁴, suggesting a high prediction performance of the model. The effectiveness of the method for the detection of real samples in practical production was also proved. Based on the above results, it could conclude that the milk component analyser, combined with chemometrics, effectively distinguished acid-neutralising adulterants in raw milk. These findings provide a reference for the rapid detection of adulterants and the quality control of raw milk.

Keywords:

• Raw milk
• milk adulteration
• principal component analysis
• partial least squares discrimination analysis
• random forest

Disclosure statement

No potential conflict of interest was reported by the authors.

Additional information

Funding

This research was supported by the "Science and Technology Innovation Action Plan" Agricultural Project of Shanghai Science and Technology Commission (No. 19391902600), the Shanghai Engineering Technology Research Center of Shanghai Science and Technology Commission (No. 20DZ2255600) for financial support.