Predicting adulteration of Palm oil with Sudan IV dye using shortwave handheld spectroscopy and comparative analysis of models

Abstract

Shortwave handheld NIR spectroscopy coupled with multivariate algorithm was attempted to simultaneously classify and measure Sudan IV dye adulteration in palm oil samples. K-nearest neighbour (KNN) was used to develop a classification model to discriminate between authentic palm oil samples and Sudan IV dye adulterated (0.10−0.002 % w/w) ones. Principal component regression (PCR), partial least square regression (PLSR) and support vector machine regression (SVMR) algorithms were comparatively employed to quantify the addition of Sudan IV dye in authentic palm oil samples. The models were evaluated by the classification rate (R), correlation co-efficient in the calibration set/prediction set (Rp2/Rc2), and root mean square error of calibration/prediction (RMSEC/P). The developed multiplicative scatter correction plus KNN technique was found to accurately classify where R = 95.48 % and 97.00 % in the calibration set and prediction set respectively. Among the quantification model developed for measuring Sudan IV dye, standard normal variant preprocessing plus partial least square regression (SNV-PLSR) gave the best performance at Rc2 = 0.91, Rp2 = 0.90 and RMSEC = 0.0841, RMSEP = 0.0868 while standard normal variant preprocessing plus principal component regression gave Rc2 = 0.90, Rp2 = 0.90 and RMSEC = 0.0846, RMSEP = 0.870. The findings have proved that, the integrity of palm oil samples can be certified rapidly and nondestructively in terms of the presence of Sudan IV by using short wave handheld NIR spectroscopy. This offers the opportunity for incorporating NIR spectroscopy into mobile phone devices to enhance mobile detection.