A Comparative Study Between Partial Least Squares and Principal Component Regression for Nondestructive Quantification of Piperine Contents in Black Pepper by Raman Spectroscopy

Abstract

The aim of this work was to compare principal component regression (PCR) and partial least squares (PLS) regression methods while estimating the piperine content in black pepper using Raman spectroscopy. The calibration and prediction models of the regression analysis on Raman spectra were developed using PCR and PLS algorithm. The efficiency of the developed models was evaluated by means of root mean square error of calibration (RMSEC), root mean square error of prediction (RMSEP), and correlation coefficient (R2). For PCR algorithm, these parameters were obtained as 0.1, 0.1, and 0.97, respectively; and for PLS regression, the parameters were found as 0.05, 0.08, and 0.99, respectively. The results revealed that Raman spectroscopy with PCR and PLS algorithm could be used for determining the concentration of piperine in black pepper with an accuracy of 92.35% and 94.74% respectively.