Rapid detection of sunset yellow adulteration in tea powder with variable selection coupled to machine learning tools using spectral data

In the present study sunset yellow (SY), a synthetic colour, which is a common adulterant in tea powders has been analysed using FT-IR spectral data coupled to machine learning tools for efficient classification and quantification of the SY adulteration. Earlier established real coded genetic algorithm (RCGA) was used as variable selection method to predict the key fingerprints of SY in the FT-IR spectra. Here, RCGA was used to select 20, 30, 40, 50 and 60 characteristic wavenumbers for SY. Classification was carried using support vector machine (SVM), random forest (RF) and extreme gradient boosting (XGB) classifiers. SVM classifier using 50 variables could give an accuracy of 0.90 amongst the three. Quantification of SY based on PLS (partial least squares), LS-SVM (least squares-SVM), RF and XGBoost were built on characteristic wavenumbers. Both RF and LS-SVM models were observed to be superior to PLS when coupled to RCGA obtained 20 fingerprint variables. Overall, RCGA-LS-SVM model resulted in lowest RMSECV (0.1956) with regression co-efficient values R C 2 = 0.9989 and R P 2 = 0.9979, when 50 fingerprint variables were used. These results demonstrated that FT-IR combined with RCGA-LS-SVM procedure could be a robust technique for rapid detection of SY in tea powder.