Orthogonal signal correction-based prediction of total antioxidant activity using partial least squares regression from chromatograms

Abstract

The multivariate calibration methodspartial least squares (PLS), orthogonal signal correction and partial least squares (OSC-PLS)were employed for the prediction of total antioxidant activities of four Prunella L. species. High-performance liquid chromatography (HPLC) and spectrophotometric approaches were used to determine the total antioxidant activity of the Prunella L. samples. Several preprocessing techniques such as smoothing and normalization were employed to extract the chemically relevant information from the data after alignment with correlation optimized warping. The importance of the preprocessing was investigated by calculating the root mean square error for the calibration set for the total antioxidant activity of Prunella L. samples. The models developed on the basis of the preprocessed data were able to predict the total antioxidant activity with a precision comparable to that of the reference 2,2-azino-di-(3-ethylbenzothialozine-sulfonic acid) and 2,2-diphenyl-1-picrylhydrazyl methods. The OSC-PLS model seems preferable because of its predictive and describing abilities and good interpretability of the contribution of compounds to the total antioxidant activity. The contribution of individual phenolic compounds to the total antioxidant activity was identified by HPLC.