A green method for the quantification of polysaccharides in Dendrobium officinale

Abstract

Polysaccharides are one of the active components of Dendrobium officinale (D. officinale) and its content is used as one of the main quality assessment criteria. The existing methods for polysaccharide quantification involve sample destruction, tedious sample processing, high cost, and non-environmentally friendly pretreatment. The aim of this study is to develop a simple, rapid, green and nondestructive analytical method based on near infrared (NIR) spectroscopy and chemometrics methods. A set of 84 D. officinale samples from different origins was analyzed using NIR spectroscopy. Potential outlying samples were initially removed from the collected NIR data in two steps using the Monte Carlo sampling (MCS) method. Spectral data preprocessing was studied in the construction of a partial least squares (PLS) model. To eliminate uninformative variables and improve the performance of the model, the pretreated full spectrum was calculated using different wavelength selection methods, including competitive adaptive reweighted sampling (CARS), Monte Carlo-uninformative variable elimination (MC-UVE) and interval random frog (iRF). The selected wavelengths model met the following three points: (1) improved the prediction performance; (2) reduced the number of variables; (3) provided a better understanding and interpretation, which proves that it was necessary to conduct wavelength selection in the NIR analytical systems. When comparing the three wavelength selection methods, the results show that CARS has the best performance with the lowest root mean square error of prediction (RMSEP) on the independent test set and least number of latent variables (nLVs). This study demonstrates that the NIR spectral technique with the wavelength selection algorithm CARS could be used successfully for the quantification of the polysaccharide content in D. officinale.