A rapid quality grade discrimination method for Gastrodia elata powderusing ATR-FTIR and chemometrics

A breakthrough in phytochemical profiling: ultra-sensitive surface-enhanced Raman spectroscopy platform for detecting bioactive components in medicinal and edible plants

A perennial challenge in harnessing the rich biological activity of medicinal and edible plants is the accurate identification and sensitive detection of their active compounds. In this study, an innovative, ultra-sensitive detection platform for plant chemical profiling is created using surface-enhanced Raman spectroscopy (SERS) technology. The platform uses silver nanoparticles as the enhancing substrate, excess sodium borohydride prevents substrate oxidation, and methanol enables the tested molecules to be better adsorbed onto the silver nanoparticles. Subsequently, nanoparticle aggregation to form stable "hot spots" is induced by Ca2+, and the Raman signal of the target molecule is strongly enhanced. At the same time, deuterated methanol was used as the internal standard for quantitative determination. The method has excellent reproducibility, RSD ≤ 1.79%, and the enhancement factor of this method for the detection of active ingredients in the medicinal plant Coptis chinensis was 1.24 × 109, with detection limits as low as 3 fM. The platform successfully compared the alkaloid distribution in different parts of Coptis chinensis: root > leaf > stem, and the difference in content between different batches of Coptis chinensis decoction was successfully evaluated. The analytical technology adopted by the platform can speed up the determination of Coptis chinensis and reduce the cost of analysis, not only making better use of these valuable resources but also promoting development and innovation in the food and pharmaceutical industries. This study provides a new method for the development, evaluation, and comprehensive utilization of both medicinal and edible plants. It is expected that this method will be extended to the modern rapid detection of other medicinal and edible plants and will provide technical support for the vigorous development of the medicinal and edible plants industry.

Traditional uses, phytochemistry, pharmacology, applications, and quality control of Gastrodia elata Blume: A comprehensive review

ETHNOPHARMACOLOGICAL RELEVANCE

Gastrodia elata Blume (G. elata) has a long historical application in Asian countries and its tubers, seeds, and stalks are capable of being utilized for medicine, food, or health care products.

AIM OF THE REVIEW

This study aimed to offer a systematic and up-to-date analysis of the current review of the G. elata research advances in traditional uses, phytochemistry, pharmacology, applications, and quality control, as well as a scientific reference for the development and utilization of this plant.

MATERIALS AND METHODS

Electronic databases including PubMed, Web of Science, Google Scholar, ScienceDirect, SciFinder, and CNKI were used for the collection of publications on G. elata. The following keywords of G. elata were used truncated with other relevant topic terms, such as phenolic compounds, polysaccharides, glycosides, neuroprotection, learning and memory improvement effects, cardioprotection, applications, and quality control.

RESULTS AND CONCLUSIONS

Approximately 134 chemical components mainly categorizing as phenolic compounds, polysaccharides, glycosides, organic acids, and sterols were reported from this plant. Moreover, preclinical studies indicated that G. elata performs several functions, including neuroprotection, learning and memory improvement effects, cardioprotection, vaso-modulatory effect, anti-depression, anti-cancer, and other effects. Currently, G. elata has been widely applied to clinics and foods. The available literature shows that the quality of G. elata might be affected by factors such as origin, fungus, and harvest time, which will have an impact on the drug efficacy. According to past research, G. elata is a potential medicinal and edible plant with several active components and pharmacological activity that has a high application value in medicine and the food business. Nevertheless, few studies have concentrated on characterization of polysaccharides structure and study of non-medicinal parts, implying that further comprehensive research on its polysaccharides structure and non-medicinal parts is critical for full utilization of resources of G. elata.

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.

Rapid authentication of variants of Gastrodia elata Blume using near-infrared spectroscopy combined with chemometric methods

The variety is one of the most important factors to generate difference of chemical compositions, which unavoidably influences the quality of natural medicine. Thus, simple and rapid authentication of different variants has great academic and practical significance. In this study, the goal was achieved with the help of near infrared spectroscopy (NIR) and chemometrics by using Gastrodia elata Blume as an example. A total of 540 samples including two classes of variants and their forms were investigated as a whole. The mean spectra of samples of each class and their 2-D synchronous correlation spectra were simultaneously applied to discover the difference of chemical characteristics. After hybrid pre-processing of the first and second derivative combined with Savitzky-Golay and Norris filtering, partial least squares discrimination analysis (PLS-DA) on the basis of latent variable projection was used to assess the feasibility for classification. The results show higher prediction accuracy in both internal test set and external prediction set. In order to further improve the robustness for modeling, three methods for wavelength selection were comprehensively compared to optimize PLS-DA models, including variable importance in the projection (VIP), random frog (RF), and Monte Carlo uninformative variable elimination (MC-UVE). The prediction accuracy of combination of the 2nd derivative, Norris, MC-UVE and PLS-DA achieved to 99.11% and 98.89% corresponding to the internal test set and external prediction set, respectively. The strategies proposed in this work perform effectiveness for rapid and accurate authentication of variants of plants with high chemical complexity.

Authentication of Marantodes pumilum (Blume) Kuntze: A Systematic Review

Botanical drug products consist of complex phytochemical constituents that vary based on various factors that substantially produce different pharmacological activities and possible side effects. Marantodes pumilum (Blume) Kuntze (Primulaceae) is one of the most popular Malay traditional botanical drugs and widely recognized for its medicinal use. Many studies have been conducted focusing on the identification of bioactive substances, pharmacological and toxicological activities in its specific varieties but less comprehensive study on M. pumilum authentication. Lack of quality control (QC) measurement assessment may cause different quality issues on M. pumilum containing products like adulteration by pharmaceutical substances, substitution, contamination, misidentification with toxic plant species, which may be detrimental to consumers' health and safety. This systematic literature review aims to provide an overview of the current scenario on the quality control of botanical drug products as determined by pharmacopoeia requirements specifically for M. pumilum authentication or identification. A systematic search for peer-reviewed publications to document literature search for M. pumilum authentication was performed using four electronic databases: Web of Science, PubMed, Scopus and ScienceDirect for related studies from January 2010 to December 2021. The research studies published in English and related articles for identification or authentication of M. pumilum were the main inclusion criteria in this review. A total 122 articles were identified, whereby 33 articles met the inclusion criteria. Macroscopy, microscopy, chemical fingerprinting techniques using chromatography, spectroscopy and hyphenated techniques, and genetic-based fingerprinting using DNA barcoding method have been used to identify M. pumilum and to distinguish between different varieties and plant parts. The study concluded that a combination of approaches is necessary for authenticating botanical drug substances and products containing M. pumilum to assure the quality, safety, and efficacy of marketed botanical drug products, particularly those with therapeutic claims.

Determination of idiopathic female infertility from infrared spectra of follicle fluid combined with gonadotrophin levels, multivariate analysis and machine learning methods

By in vitro fertilization, oocytes can be removed and the embryo can be cultured, and then trans cervically replaced when they reach cleavage or when the blastocyst stage. The characterization of the follicular fluid is important for the treatment process. Women who applied to the Academic Hospital in vitro fertilization (IVF) Center diagnosed with idiopathic female infertility (IFI) were sought in the patient group. Demographics and clinical gonadotropin measurements of the study population were recorded. Of the 116 follicular fluid samples (n=58 male-induced infertility; n=58 control) were analyzed using the FTIR system. To identify FTIR spectral characteristics of follicular fluids associated with an ovarian reserve and reproductive hormone levels from control and IFI, six machine learning methods and multivariate analysis were used. To assess the quantitative information about the total biochemical composition of a follicular fluid across various diagnoses. FTIR spectra showed a higher level of vibrations corresponding to lipids and a lower level of amide vibrations in the IFI group. Furthermore, the T square plot from Partial Last Square (PLS) analysis showed, that these vibrations can be used to distinguish IFI from the control group which was obtained by principal component analysis (PCA). Proteins and lipids play an important role in the development of IFI. The absorption dynamics of FTIR spectra showed wavenumbers with around 100% discrimination probability, which means, that the presented wavenumbers can be used as a spectroscopic marker of IFI. Also, six machine learning methods showed, that classification accuracy for the original set was from 93.75% to 100% depending on the learning algorithm used. These results can inform about IFI women's follicular fluid has biomacromolecular differentiation in their follicular fluid. By using a safe and effective tool for the characterization of changes in follicular fluid during in vitro fertilization, this study builds upon a comprehensive examination of the idiopathic female infertility remodeling process in human studies. We anticipate that this technology will be a valuable adjunct for clinical studies.