Real-coded GA coupled to PLS for rapid detection and quantification of tartrazine in tea using FT-IR spectroscopy

Abstract

In this study, Fourier-transform infrared (FT-IR) spectral data was combined with variable selection methods to measure tartrazine adulteration in tea powder. Partial least square (PLS) regression and its variants such as backward interval PLS (BiPLS), genetic algorithm PLS (GA-PLS), and competitive adaptive reweighted sampling PLS (CARS-PLS) for variable selection were established as calibration models for the quantitative prediction of tartrazine. A simple and efficient real-coded GA (RCGA) was also implemented as a variant of GA-PLS regression. The performance of these models was adjudged based on root mean square errors (RMSE) for both cross-validation (RMSECV) and prediction (RMSEP) along with their respective correlation coefficients (RC and RP). The developed RCGA-PLS was observed to be a robust technique to achieve a model with low RMSECV and RMSEP values of 0.8331 and 0.923, respectively. This model uses 30 selection variables (1.19% of full variable count) to predict tartrazine in the range of 0–30 mg/g with a correlation coefficient of 0.987. This study demonstrated that FT-IR spectroscopy, combined with the developed RCGA-PLS procedure for variable selection could be a robust technique for the rapid detection of tartrazine in tea samples.