Abstract
Multivariate calibration are gaining popularity in assaying food matrices. Partial least squares is a powerful multivariate calibration method that used to build a quantitative relationship between measured variables and a property of interest (i.e., concentration) of the system under study. Partial least squares PLS calibration along with UV/vis spectral data was efficient to account for indirect food matrix and direct interference effects resulted from overlapping food dyes. PLS was able to quantify tartrazine TAT, allura red AR, sunset yellow SY and brilliant black BB that added to wide selection sugar-based candies. The results indicated that 70% of samples containing single dye while 8% containing TAT-SY mix and certain samples containing TAT + SY + AR + BB. Lollypops were found to contain high levels of AR (77–120 mg/kg) and TAT (56–166 mg/kg). The maximum adulteration was 50% observed in lollypops. PLS calibration was workable to predict colorants with prediction errors of 7%. Using PLS, dyes were detected down to 0.1 mg/L with acceptable accuracy and precision. PLS showed comparable performance with liquid chromatography for dyes quantification and can substitute laborious chromatography for quick detection of coloring agents in candies.