Discrimination of Gentiana and Its Related Species Using IR Spectroscopy Combined with Feature Selection and Stacked Generalization

Abstract

Gentiana, which is one of the largest genera of Gentianoideae, most of which had potential pharmaceutical value, and applied to local traditional medical treatment. Because of the phytochemical diversity and difference of bioactive compounds among species, which makes it crucial to accurately identify authentic Gentiana species. In this paper, the feasibility of using the infrared spectroscopy technique combined with chemometrics analysis to identify Gentiana and its related species was studied. A total of 180 batches of raw spectral fingerprints were obtained from 18 species of Gentiana and Tripterospermum by near-infrared (NIR: 10,000–4000 cm−1) and Fourier transform mid-infrared (MIR: 4000–600 cm−1) spectrum. Firstly, principal component analysis (PCA) was utilized to explore the natural grouping of the 180 samples. Secondly, random forests (RF), support vector machine (SVM), and K-nearest neighbors (KNN) models were built while using full spectra (including 1487 NIR variables and 1214 FT-MIR variables, respectively). The MIR-SVM model had a higher classification accuracy rate than the other models that were based on the results of the calibration sets and prediction sets. The five feature selection strategies, VIP (variable importance in the projection), Boruta, GARF (genetic algorithm combined with random forest), GASVM (genetic algorithm combined with support vector machine), and Venn diagram calculation, were used to reduce the dimensions of the data variable in order to further reduce numbers of variables for modeling. Finally, 101 NIR and 73 FT-MIR bands were selected as the feature variables, respectively. Thirdly, stacking models were built based on the optimal spectral dataset. Most of the stacking models performed better than the full spectra-based models. RF and SVM (as base learners), combined with the SVM meta-classifier, was the optimal stacked generalization strategy. For the SG-Ven-MIR-SVM model, the accuracy (ACC) of the calibration set and validation set were both 100%. Sensitivity (SE), specificity (SP), efficiency (EFF), Matthews correlation coefficient (MCC), and Cohen’s kappa coefficient (K) were all 1, which showed that the model had the optimal authenticity identification performance. Those parameters indicated that stacked generalization combined with feature selection is probably an important technique for improving the classification model predictive accuracy and avoid overfitting. The study result can provide a valuable reference for the safety and effectiveness of the clinical application of medicinal Gentiana.

A Novel Multi-Preprocessing Integration Method for the Qualitative and Quantitative Assessment of Wild Medicinal Plants: Gentiana rigescens as an Example

“…Indeed, two very recent studies used data fusion in an attempt to integrate instrument/sample information for the rapid quality assessment of wild G. rigescens. The first one by Shen et al (2020) used low-and mid-level data fusion strategies to integrate the different instrument information of wild G. rigescens, including near-infrared (NIR) and midinfrared (MIR) spectroscopies, for the discrimination of the wild G. rigescens and its related species. The second one by Liu et al (2020a) utilized a data fusion strategy to integrate homologous information of multi-part (root, stem, and leaf) samples for tracing the geographical origins of wild Gentiana rigescens.…”

Determination of quality markers for quality control of Zanthoxylum nitidum using ultra-performance liquid chromatography coupled with near infrared spectroscopy

“…Since the late 1980s, NIRS has been used as a continuous monitoring and process control tool for food, agriculture, and pharmaceutical industries [9,10]. Rapid discrimination of Flos Mume [11], Gentiana [12], Chrysanthemum [13], Digitalis purpurea [14], and their related species using NIRS has been shown to be feasible. However, a method for the rapid identification of Z. nitidum and discrimination from its confused species from the same family needs to be developed.…”

Microscopic investigations and pharmacognostic techniques for the standardization of the fruits of Rosa laxa Retz

“…Approximately 2 mg each of E1, E2, E3, and E4 was mixed with 20 mg KBr, ground into fine powder, and then pressed into transparent slices (Shen, Yu, & Wang, 2020). The slices were analyzed using a Fourier transform infrared (FTIR) spectrometer.…”