Evaluation of four different analytical tools to determine the regional origin of Gastrodia elata and Rehmannia glutinosa on the basis of metabolomics study

Identification of geographical origins of Gastrodia elata Blume based on multisource data fusion

INTRODUCTION

Identifying the geographical origin of Gastrodia elata Blume contributes to the scientific and rational utilization of medicinal materials. In this study, infrared spectroscopy was combined with machine learning algorithms to distinguish the origin of G. elata BI.

OBJECTIVE

Realization of rapid and accurate identification of the origin of G. elata BI.

MATERIALS AND METHODS

Attenuated total reflection Fourier transform infrared (ATR-FTIR) spectra and Fourier transform near-infrared (FT-NIR) spectra were collected for 306 samples of G. elata BI.

SAMPLES

Firstly, a support vector machine (SVM) model was established based on the single-spectrum and the full-spectrum fusion data. To investigate whether feature-level fusion strategy can enhance the model's performance, the sequential and orthogonalized partial least squares discriminant analysis (SO-PLS-DA) model was established to extract and combine two types of spectral features. Next, six algorithms were employed to extract feature variables, SVM model was established based on the feature-level fusion data. To avoid complicated preprocessing and feature extraction processes, a residual convolutional neural network (ResNet) model was established after converting the raw spectral data into spectral images.

RESULTS

The accuracy of the feature-level fusion model is better as compared to the single-spectrum model and the fusion model with full-spectrum, and SO-PLS-DA is simpler than feature-level fusion based on the SVM model. The ResNet model performs well in classification but requires more data to enhance its generalization capability and training effectiveness.

CONCLUSION

Sequential and orthogonalized data fusion approaches and ResNet models are powerful solutions for identifying the geographic origin of G. elata BI.

Subcritical water extraction of polysaccharides from Gastrodiae Rhizoma: optimization, characterization and in vitro hepatoprotective activity

Subcritical water extraction (SWE) is an efficient and eco-friendly technology that rapidly extracts valuable compounds from natural materials. In this study, response surface methodology (RSM) was utilized to determine the optimal extraction conditions for Gastrodiae Rhizoma using SWE (GRP-S). The optimum conditions were found to be 161 °C extraction temperature, 41 min extraction time, and a solid-to-liquid ratio of 1.55 mg/mL. Under these optimal conditions, the experimental yield of GRP-S was 66.32% ± 0.10% (n = 3), demonstrating a significant increase compared to hot water reflux extraction (HWE) in the extraction yield of polysaccharides. Characterization studies employing SEM, FT-IR, and HPAEC-PAD confirmed the differences between GRP-S and GRP-H (GRP obtained by HWE). Furthermore, both GRP-S and GRP-H exhibited a significant ability to protect HepG2 cells from ethanol-induced damage, with GRP-S showcasing a superior effect. The widespread adoption of SWE technology can lead to high GRP content in extracts and promote the green and sustainable development of natural products extraction processes.

Application for Identifying the Origin and Predicting the Physiologically Active Ingredient Contents of Gastrodia elata Blume Using Visible-Near-Infrared Spectroscopy Combined with Machine Learning

Gastrodia elata (G. elata) Blume is widely used as a health product with significant economic, medicinal, and ecological values. Due to variations in the geographical origin, soil pH, and content of organic matter, the levels of physiologically active ingredient contents in G. elata from different origins may vary. Therefore, rapid methods for predicting the geographical origin and the contents of these ingredients are important for the market. This paper proposes a visible-near-infrared (Vis-NIR) spectroscopy technology combined with machine learning. A variety of machine learning models were benchmarked against a one-dimensional convolutional neural network (1D-CNN) in terms of accuracy. In the origin identification models, the 1D-CNN demonstrated excellent performance, with the F1 score being 1.0000, correctly identifying the 11 origins. In the quantitative models, the 1D-CNN outperformed the other three algorithms. For the prediction set of eight physiologically active ingredients, namely, GA, HA, PE, PB, PC, PA, GA + HA, and total, the RMSEP values were 0.2881, 0.0871, 0.3387, 0.2485, 0.0761, 0.7027, 0.3664, and 1.2965, respectively. The Rp2 values were 0.9278, 0.9321, 0.9433, 0.9094, 0.9454, 0.9282, 0.9173, and 0.9323, respectively. This study demonstrated that the 1D-CNN showed highly accurate non-linear descriptive capability. The proposed combinations of Vis-NIR spectroscopy with 1D-CNN models have significant potential in the quality evaluation of G. elata.

Comparison on Bioactivities and Characteristics of Polysaccharides From Four Varieties of Gastrodia elata Blume

The composition, physicochemical properties, in vitro biological activity, and hypoglycemic activity exhibited by polysaccharides from four varieties of G. elata were investigated in this study; the four extracted GaE polysaccharides were termed as GaE-B (G. elata Bl. f. glauca S. chow polysaccharides), GaE-R (G. elata Bl. f. elata polysaccharides), GaE-Hyb (hybridization of G. elata Bl. f. glauca S. chow and G. elata Bl. f. elata polysaccharides), and GaE-G (G. elata Bl. f. viridis Makino polysaccharides). As revealed by the results, the GaE polysaccharides were found with the same monosaccharide composition, primarily including glucose, whereas the content of each variety was significantly different. In addition, different degrees of differences were found in the in vitro antioxidant and hypoglycemic activity, molecular weight, yield, and chemical composition exhibited by the abovementioned varieties. However, GaE-B and GaE-Hyb were found with similar physical properties, chemical composition, and antioxidant and hypoglycemic activity. GaE-R had the lowest yield, total sugar content, and molecular weight, whereas it involved higher xylose, binding protein, and polyphenols as well as higher antioxidant and hypoglycemic activity. In contrast, GaE-G was found with the highest yield, total sugar content, and molecular weight, whereas it contained the lowest xylose, binding protein, and polyphenols, as well as the weakest antioxidant and hypoglycemic activity. In brief, the polysaccharide of G. elata, a plant resource for homology of medicine and food, could more significantly enhance the biological activity of G. elata as it was released in the process of decocting and stewing. To be specific, the assessment of polysaccharide activity alone suggested that GaE-R was the best.

Comparative analysis of metabolic variations, antioxidant potential and cytotoxic effects in different parts of Chelidonium majus L

Metabolic variations, antioxidant potential and cytotoxic effects were investigated in the different plant parts like the leaf, stem, flower, pod, and root of C. majus L. using spectroscopic and chromatographic methods. Total phenolics and flavonoids were studied in the different parts of C. majus L., leaf showed higher flavonoid content (137.43 mg/g), while the pod showed the highest phenolic (23.67 mg/g) content, when compared with the stem, flower and root. In the ABTS antioxidant assay, the flower extract showed 57.94% effect, while the leaf, pod and root extract exhibited 39.10%, 36.08% and 28.88% activity, respectively. The pod and leaf extracts demonstrated the potential effect, exhibiting 45.46 and 41.61% activity, respectively, for the DPPH assay. Similar to the phosphomolybdenum assay, the flower revealed higher antioxidant activity (46.82%) than the other plant parts. The in vitro SRB assay facilitated evaluation of the cytotoxic effect against the HeLa and CaSki human cervical cancerous cells. The extract displayed dose-dependent inhibitory effect on both the cell lines. The highest cytotoxic effect was observed in the pod and flower extracts post 48 h of exposure at 1000 μg/mL. The results of C. majus L. offered new insights in the preliminary steps regarding the development of a high value product for phytomedicine applications though promising metabolic variations with antioxidant and anticancer potentials.

Near-Infrared Spectroscopy as a Process Analytical Technology Tool for Monitoring the Steaming Process of Gastrodiae rhizoma with Multiparameters and Chemometrics

Steaming is a vital unit operation in traditional Chinese medicine (TCM), which greatly affects the active ingredients and the pharmacological efficacy of the products. Near-infrared (NIR) spectroscopy has already been widely used as a strong process analytical technology (PAT) tool. In this study, the potential usage of NIR spectroscopy to monitor the steaming process of Gastrodiae rhizoma was explored. About 10 lab scale batches were employed to construct quantitative models to determine four chemical ingredients and moisture change during the steaming process. Gastrodin, p-hydroxybenzyl alcohol, parishin B, and parishin A were modeled by different multivariate calibration models (SMLR and PLS), while the content of the moisture was modeled by principal component regression (PCR). In the optimized models, the root mean square errors of prediction (RMSEP) for gastrodin, p-hydroxybenzyl alcohol, parishin B, parishin A, and moisture were 0.0181, 0.0143, 0.0132, 0.0244, and 2.15, respectively, and correlation coefficients (R _p ²) were 0.9591, 0.9307, 0.9309, 0.9277, and 0.9201, respectively. Three other batches' results revealed that the accuracy of the model was acceptable and that was specific for next drying step. In addition, the results demonstrated the method was reliable in process performance and robustness. This method holds a great promise to replace current subjective color judgment and time-consuming HPLC or UV/Vis methods and is suitable for rapid online monitoring and quality control in the TCM industrial steaming process.

Optimal Extraction Study of Gastrodin-Type Components from Gastrodia Elata Tubers by Response Surface Design with Integrated Phytochemical and Bioactivity Evaluation

Gastrodia elata tuber (GET) is a popular traditional Chinese medicines (TCMs). In this study, response surface methodology (RSM) with a Box–Behnken design (BBD) was performed to optimize the extraction parameters of gastrodin-type components (gastrodin, gastrodigenin, parishin A, parishin B, parishin C and parishin E). Different from the conventional studies that merely focused on the contents of phytochemical, we gave consideration to both quantitative analysis of the above six components by HPLC and representative bioactivities of GET, including antioxidation and protection of human umbilical vein endothelial cells (HUVEC). Four independent variables (ethanol concentration, liquid-material ratio, soaking time and extraction time) were investigated with the integrated evaluation index of phytochemical contents. With the validation experiments, the optimal extraction parameters were as follows: ethanol concentration of 41%, liquid–solid ratio of 28.58 mL/g, soaking time of 23.91 h and extraction time of 46.60 min. Under the optimum conditions, the actual standardized comprehensive score was 1.8134 ± 0.0110, which was in accordance with the predicted score of 1.8100. This firstly established method was proved to be feasible and reliable to optimize the extraction parameters of the bioactive components from GET. Furthermore, it provides some reference for the quality control and extraction optimization of TCMs.

Discrimination of Gastrodia elata from Different Geographical Origin for Quality Evaluation Using Newly-Build Near Infrared Spectrum Coupled with Multivariate Analysis

Discrimination of Gastrodia elata (G. elata) geographical origin is of great importance to pharmaceutical companies and consumers in China. this paper focuses on the feasibility of near infrared spectrum (NIRS) combined multivariate analysis as a rapid and non-destructive method to prove its fit for this purpose. Firstly, 16 batches of G. elata samples from four main-cultivation regions in China were quantified by traditional HPLC method. It showed that samples from different origins could not be efficiently differentiated by the contents of four phenolic compounds in this study. Secondly, the raw near infrared (NIR) spectra of those samples were acquired and two different pattern recognition techniques were used to classify the geographical origins. The results showed that with spectral transformation optimized, discriminant analysis (DA) provided 97% and 99% correct classification for the calibration and validation sets of samples from discriminating of four different main-cultivation regions, and provided 98% and 99% correct classifications for the calibration and validation sets of samples from eight different cities, respectively, which all performed better than the principal component analysis (PCA) method. Thirdly, as phenolic compounds content (PCC) is highly related with the quality of G. elata, synergy interval partial least squares (Si-PLS) was applied to build the PCC prediction model. The coefficient of determination for prediction (R_p²) of the Si-PLS model was 0.9209, and root mean square error for prediction (RMSEP) was 0.338. The two regions (4800 cm⁻¹–5200 cm⁻¹, and 5600 cm⁻¹–6000 cm⁻¹) selected by Si-PLS corresponded to the absorptions of aromatic ring in the basic phenolic structure. It can be concluded that NIR spectroscopy combined with PCA, DA and Si-PLS would be a potential tool to provide a reference for the quality control of G. elata.

Recent applications of NMR in food and dietary studies

Over the last decade, a wide variety of new foods have been introduced into the global marketplace, many with health benefits that exceed those of traditional foods. Simultaneously, a wide range of analytical technologies has evolved that allow greater capability for the determination of food composition. Nuclear magnetic resonance (NMR), traditionally a research tool used for structural elucidation, is now being used frequently for metabolomics and chemical fingerprinting. Its stability and inherent ease of quantification have been exploited extensively to identify and quantify bioactive components in foods and dietary supplements. In addition, NMR fingerprints have been used to differentiate cultivars, evaluate sensory properties of food and investigate the influence of growing conditions on food crops. Here we review the latest applications of NMR in food analysis. Published 2016. This article is a U.S. Government work and is in the public domain in the USA.

The rhizome of Gastrodia elata Blume - An ethnopharmacological review

ETHNOPHARMACOLOGICAL RELEVANCE

Gastrodia elata Blume (Orchidaceae) is commonly called Tian ma in Chinese and mainly distributed in the mountainous areas of eastern Asia, such as China, Korea, Japan and India. It is an extensively used traditional Chinese herbal medicine in the clinical practice of traditional Chinese medicine, to treat headache, migraine, dizziness, epilepsy, infantile convulsion, tetany and so on. The present paper reviews the advancements in investigation of botany and ethnopharmacology, phytochemistry, pharmacology, toxicology and quality control of Gastrodia elata Blume. Finally, the possible tendency and perspective for future investigation of this plant are also put forward.

MATERIALS AND METHODS

The information on Gastrodia elata Blume was collected via piles of resources including classic books about Chinese herbal medicine, and scientific databases including Pubmed, Google Scholar, ACS, Web of science, ScienceDirect databases, CNKI and others. Plant taxonomy was validated by the databases "The Plant List", and "Mansfeld's Encyclopedia".

RESULTS

Over 81 compounds from this plant have been isolated and identified, phenolics and polysaccharides are generally considered as the characteristic and active constituents of Gastrodia elata Blume. Its active compounds possess wide-reaching biological activities, including sedative, hypnotic, antiepileptic, anticonvulsive, antianxietic, antidepressant, neuroprotective, antipsychotic, anti-vertigo, circulatory system modulating, anti-inflammationary, analgesic, antioxidative, memory-improving and antiaging, antivirus and antitumor effects.

CONCLUSION

Despite the publication of various papers on Gastrodia elata Blume, there is still, however, the need for definitive research and clarification of other bioactive compounds using bioactivity-guided isolation strategies, and the possible mechanism of action as well as potential synergistic or antagonistic effects of multi-component mixtures derived from Gastrodia elata Blume need to be evaluated. It is also necessary and important to do more quality control and toxicological study on human subjects in order to maintain its efficacy stable in the body and validate its safety in clinical uses. In addition, more investigations on other parts of this plant beyond the tubers are needed. Further studies on Gastrodia elata Blume will lead to the development of new drugs and therapeutics for various diseases, and how to utilize it better should be paid more attention to.