Systematic vs. stepwise parameter optimization for discriminant model development: A case study of differentiating Pinellia ternata from Pinellia pedatisecta with near infrared spectroscopy

Abstract

Near infrared (NIR) spectroscopy is an effective technique for adulteration detection in traditional Chinese medicine. The aim is to develop a discriminant model with the aid of chemometrics tools. The discriminant model is conventionally established by the means of stepwise optimization. This approach is often limited to trial-and-error and considered as a burden. In this study, a systematic optimization approach was proposed to develop the discriminant model with the aid of the design of experiment tools and applied to a case study of differentiating Pinellia ternata from Pinellia pedatisecta and adulterated Pinellia ternata using NIR spectroscopy. Spectral pretreatment, variable selection, and discriminant methods were identified as critical factors. The classification accuracy and no-error rate of the calibration set, cross-validation, and the prediction set were calculated to evaluate the performance of discriminant models. A full factorial design was applied to analyze the effect of critical factors at different levels on the model performance and optimize these factors. Three discriminant models including discriminant analysis coupled with principal component analysis (PCA-DA), partial least squares – discriminant analysis (PLS-DA), and k-nearest neighbors (KNN) were obtained by systematic optimization. The performance of PCA-DA and PLS-DA models obtained by systematic optimization was very good, and no samples were misclassified, which were better than those obtained by stepwise optimization. The performance of the KNN model obtained by systematic optimization was not desired and it was equal to that obtained by stepwise optimization. The results showed that Pinellia ternata could be successfully discriminated from Pinellia pedatisecta and adulterated Pinellia ternata by the PCA-DA and PLS-DA models. Compared to the stepwise optimization approach, the systematic optimization approach can improve the PCA-DA and PLS-DA model performance for differentiating Pinellia ternata from Pinellia pedatisecta and adulterated Pinellia ternata.