Application of Near Infrared Spectroscopy (NIRs), PCA and PLS models for the analysis of dried medicinal plants

In traditional medicine, botanicals and medicinal plants in their natural and processed form are widely used [1] due to their medicinal and antioxidant properties. Numerous analytical methods have been developed for the analysis of chemical composition of medicinal plants extracts like gas chromatography (GC), mass spectrometry (MS), thin layer chromatography (TLC), UV spectrometry, and high performance liquid chromatography (HPLC). All these methods are precise but expensive, time-consuming and require many reagents. As an alternative, near infrared spectroscopy (NIRs), as a simple, selective, and environmentally friendly method , [2], can be used. NIR spectroscopy is a non-destructive measurement method that allows intact measuring, without any additional sample preparation or pre-treatment. Use of spectroscopy in the near infrared region allows a wide range of applications in the food chain production, from control of raw materials to intermediary and final products [3] in order to provide a quality guarantee for consumers. NIR spectroscopy is based on the electromagnetic absorption in the near infrared region. Spectral analysis has to be assisted with various chemometric techniques, such as multiple linear regression analysis (MLRA), principal component analysis (PCA) and partial least squares regression (PLSR) [4]. Chemometric techniques and chemometric modelling have become an integral part of spectral data analysis which also includes pre-processing of NIR spectra. The pre-processing objective is removal of physical phenomena in the spectra in order to improve the subsequent multivariate regression, classification model or exploratory analysis [5]. In this work, most widely used pre-processing techniques including (i) scatter-correction methods and (ii) spectral derivatives are explained through analysis of spectra of dried medicinal plants collected during the size reduction process (milling), as well as during analysis of the kinetics of the solid-liquid extraction process using water as a solvent [6]. In order to identify patterns in large set of data and express the data to highlight similarities and differences among them, PCA was used. PCA presents the pattern of similarity of the observations and the variables by displaying them as points in maps [7]. PLS regression was used to predict or analyse a set of dependent variables from a set of independent variables or predictors. The predictive ability of a PLS model is expressed as one or more statistical measures. Which parameter should be used is described by R-Squared Coefficient, Ratio of standard error of Performance to standard Deviation (RPD) and Range Error Ratio (RER).