Prediction of Andrographolide Content in Andrographis paniculata Using NIR Spectroscopy

Abstract

The aim of this work is to estimate andrographolide contents in Andrographis paniculata with the near infrared reflectance (NIR) spectroscopy. The calibration and prediction model of the regression analysis on NIR spectra was developed using partial least squares (PLS) algorithm. The latent variables of PLS and the optimal preprocessing methods were chosen at the same time by means of leave-one-sample out cross- validation at the time of the model calibration. The efficiency of the developed model was evaluated using root mean square error of cross-validation (RMSECV), root mean square error of prediction (RMSEP) and correlation coefficient (R) which have been found as 0.297, 0.011 and 0.925, respectively. Finally, the results obtained illustrated that NIR spectroscopy with PLS algorithm could be used for concentration analysis of andrographolide in Andrographis paniculata with more than 90% of accuracy.