Optimization of NIR Spectroscopy Based on Ant Colony Optimization and Genetic Algorithm for the Anthocyanin Content in Scented Tea

Abstract:

Optimization of Near infrared (NIR) spectroscopy for quantitative analysis of the anthocyanin content in scented tea was discussed by selecting the optimal spectra intervals from the whole NIR spectroscopy using two variable models: Ant colony optimization interval partial least squares (ACO-iPLS) and Genetic Algorithm interval partial least squares (GA-iPLS). The ACO-iPLS full-spectrum was split into 12 intervals. The optimal intervals selected were the 1st interval, 9th interval and 10th interval. The calibration and prediction correlation coefficient of ACO-iPLS model were 0.901 3 and 0.864 2, in which the root mean square error of cross validation (RMSECV) of 0.160 0 mg/g and the root mean square error of prediction (RMSEP) of 0.206 0 mg/g were achieved. As in the GA-iPLS model, the data set was split into 15 intervals for optimization where 1st and 5th intervals were selected. The calibration and prediction correlation coefficient of GA-iPLS model were 0.901 3 and 0.864 2, and the RMSECV and RMSEP of GA-iPLS models based on these intervals were 0.156 0 mg/g and 0.206 0 mg/g, respectively. The results showed that both ACO-iPLS and GA-iPLS models could efficiently select spectrum intervals for quantitative analysis of anthocyanin in scented tea. The optimal GA-iPLS model had better performance with higher accuracy.