Near-infrared spectroscopy and machine learning-based classification and calibration methods in detection and measurement of anionic surfactant in milk

Abstract

The monitoring of milk and milk-based products are crucial in the dairy industry because of their global consumption, fraudulent practice applied to mask the quality parameters for gaining more profit. Non-destructive near-infrared (NIR) spectroscopy and machine learning (ML) methods like interval partial least squares (iPLS), principal components analysis (PCA), and soft independent modeling of class analogy (SIMCA) were used to classify and discriminate pure milk samples from adulterated with an anionic surfactant. The surfactant’s model was sodium dodecylbenzene sulfate (SDBS) in different concentrations (1.94–19.4 gkg−1). ML models were assessed by sensitivity, specificity and efficiency rates as classification figures of merit. As a result, iPLS regression method based on standard normal variate (SNV) pre-processed spectra provided better performance compared to partial least squares (PLS) regression with a full-spectrum dataset. The root mean square error of cross-validation (RMSECV) was 0.123 and 0.135 for the iPLS and full-spectrum models, respectively. PCA as a qualitative grouping analysis demonstrated separate clusters of samples in terms of their concentration level of SDBS. For the iPLS dataset in SIMCA model, all classification figures of merit were recorded 100 % for pure milk samples. In contrast, the average sensitivity, specificity and efficiency rates were 97.6 %, 93 %, and 94.9 % for adulterated samples.