Development of a comprehensive quality control method for the quantitative analysis of volatiles and lignans in Magnolia biondii Pamp. by near infrared spectroscopy

Ethnomedicinal Importance of Patuletin in Medicine: Pharmacological Activities and Analytical Aspects

BACKGROUND

Plant-derived bioactive molecules have been a major source of therapeutic agents for human and veterinarian purposes. Different traditional medicine system across the globe had relied on natural resources to meet their demand of healthcare. Still in modern world, pharmaceutical industries look for phytochemicals to develop new drugs. The current review explores patuletin, a flavonoid for its diverse reported pharmacological activities along with its analytical techniques.

METHODS

Scientific data published on patuletin was collected from Scopus, Science Direct, Pubmed, Google, and Google Scholar. The collected data were analyzed and arranged as per specific pharmacological activities performed using in-vitro or in-vivo methods. Analytical methods of patuletin have been presented next to pharmacological activities Results: Available scientific literature indicates patuletin has anti-inflammatory, cytotoxic, genotoxic, hepatoprotective, antiproliferative, antiplatelet, antinociceptive, and antioxidant activity. In addition to these activities, its biological potential on breast cancer, rheumatoid arthritis, aldose reductase, and different types of microorganisms has been also presented in this work. Analytical data on patuletin signified the importance of patuletin for the standardization of herbal products and derived medicine.

CONCLUSION

It may be concluded that patuletin with its diverse biological activities and readily available analytical methods, holds the potential to be translated into a new drug entity.

Quality evaluation of Keteling capsules based on fingerprinting, multicomponent quantification, and quantitative prediction

The Keteling capsule (KC) is a traditional Chinese medicine (TCM) made from the dried extract of Ficus microphylla and an appropriate amount of chlorpheniramine maleate. It is widely used to treat cough and relieve asthma. Despite its extensive usage, a rapid and comprehensive quality evaluation strategy for KC remains a challenge. This study introduces an electrochemical fingerprint analysis technique, in addition to the commonly employed HPLC fingerprints, for efficient and convenient quality evaluation. Moreover, a cost-effective, rapid, and accurate multi-component quantification technique known as the "Multi-markers assay by the monolinear method (MAML)" and the "FT-IR quantitative model" were explored. The HPLC fingerprints were evaluated using a systematically quantified fingerprint method, while the electrochemical fingerprints, based on the Belousov-Zhabotinsky oscillation reaction principle, were effectively analyzed and characterized using oxidation induction times and oscillation lifetimes. Multi-component quantitative analysis was carried out through the MAML and FT-IR quantitative models. The HPLC fingerprint successfully classified the 22 samples into eight grades with excellent discrimination. Active ingredient content analysis was achieved using reliable parameters obtained from electrochemical fingerprinting. The no significant difference in the quantitative results proves the accuracy of the MAML method. Additionally, successful FT-IR quantitative prediction models were developed for chlorogenic acid, isovitexin, and chlorpheniramine maleate. This study offers a dependable and effective approach for enhancing the quality control of KC, and it can provide new insights for improving the quality analysis methods in the field of TCM.

Recent trends of machine learning applied to multi-source data of medicinal plants

In traditional medicine and ethnomedicine, medicinal plants have long been recognized as the basis for materials in therapeutic applications worldwide. In particular, the remarkable curative effect of traditional Chinese medicine during corona virus disease 2019 (COVID-19) pandemic has attracted extensive attention globally. Medicinal plants have, therefore, become increasingly popular among the public. However, with increasing demand for and profit with medicinal plants, commercial fraudulent events such as adulteration or counterfeits sometimes occur, which poses a serious threat to the clinical outcomes and interests of consumers. With rapid advances in artificial intelligence, machine learning can be used to mine information on various medicinal plants to establish an ideal resource database. We herein present a review that mainly introduces common machine learning algorithms and discusses their application in multi-source data analysis of medicinal plants. The combination of machine learning algorithms and multi-source data analysis facilitates a comprehensive analysis and aids in the effective evaluation of the quality of medicinal plants. The findings of this review provide new possibilities for promoting the development and utilization of medicinal plants.

An integrated strategy of spectrum-effect relationship and near-infrared spectroscopy rapid evaluation based on back propagation neural network for quality control of Paeoniae Radix Alba

The quantitative analysis of near-infrared spectroscopy in traditional Chinese medicine has still deficiencies in the selection of the measured indexes. Then Paeoniae Radix Alba is one of the famous "Eight Flavors of Zhejiang" herbs, however, it lacks the pharmacodynamic support, and cannot reflect the quality of Paeoniae Radix Alba accurately and reasonably. In this study, the spectrum-effect relationship of the anti-inflammatory activity of Paeoniae Radix Alba was established. Then based on the obtained bioactive component groups, the genetic algorithm, back propagation neural network, was combined with near-infrared spectroscopy to establish calibration models for the content of the bioactive components of Paeoniae Radix Alba. Finally, three bioactive components, paeoniflorin, 1,2,3,4,6-O-pentagalloylglucose, and benzoyl paeoniflorin, were successfully obtained. Their near-infrared spectroscopy content models were also established separately, and the validation sets results showed the coefficient of determination (R² > 0.85), indicating that good calibration statistics were obtained for the prediction of key pharmacodynamic components. As a result, an integrated analytical method of spectrum-effect relationship combined with near-infrared spectroscopy and deep learning algorithm was first proposed to assess and control the quality of traditional Chinese medicine, which is the future development trend for the rapid inspection of traditional Chinese medicine.

Extraction Techniques and Analytical Methods for Isolation and Characterization of Lignans

Lignans are a group of natural polyphenols present in medicinal plants and in plants which are a part of the human diet for which more and more pharmacological activities, such as antimicrobial, anti-inflammatory, hypoglycemic, and cytoprotective, are being reported. However, it is their cytotoxic activities that are best understood and which have shed light on this group. Two anticancer drugs, etoposide, and teniposide, were derived from a potent cytotoxic agent—podophyllotoxin from the roots of Podophyllum peltatum. The evidence from clinical and observational studies suggests that human microbiota metabolites (enterolactone, enterodiol) of dietary lignans (secoisolariciresinol, pinoresinol, lariciresinol, matairesinol, syringaresinol, medioresinol, and sesamin) are associated with a reduced risk of some hormone-dependent cancers. The biological in vitro, pharmacological in vivo investigations, and clinical studies demand significant amounts of pure compounds, as well as the use of well-defined and standardized extracts. That is why proper extract preparation, optimization of lignan extraction, and identification are crucial steps in the development of lignan use in medicine. This review focuses on lignan extraction, purification, fractionation, separation, and isolation methods, as well as on chromatographic, spectrometric, and spectroscopic techniques for their qualitative and quantitative analysis.

Moxa Wool in Different Purities and Different Growing Years Measured by Terahertz Spectroscopy

Moxa wool is a traditional Chinese herbal medicine, which can warm channels to dispel coldness. At present, there is no unified index to evaluate the purity and growing years of moxa wool in the market. Terpineol is one of the effective substances in the volatile oil of moxa wool. Here, we characterize the purity and growing years of moxa wool by studying terpineol. Gas chromatography-mass spectrometry (GC-MS) and high-performance liquid chromatography (HPLC) are the methods for monitoring terpineol at present, all of which have defects of complicated procedures. We established linear fitting to distinguish the different purities of moxa wool through the intensities (areas) of terpineol, the characteristic peaks, and the consequence presented; the coefficient of determination (R2) was higher than 0.90. Furthermore, based on the characteristic peak position of standard terpineol, the correlation model with the purity and growing year of moxa wool was set up, thereby differentiating the quality of moxa wool. We have built the partial least squares (PLS) model of the growing years of moxa wool with high accuracy, and the determination coefficient is greater than 0.98. In addition, we compare the quantitative accuracy of Raman spectroscopy with terahertz technology. Finally, a new method of terahertz spectroscopy to evaluate quality of moxa wool was found. It provides a new idea for the identification of inferior moxa wool in the market and a new method for identifying the quality of moxa wool in traditional Chinese medicine.

Terahertz spectroscopy combined with data dimensionality reduction algorithms for quantitative analysis of protein content in soybeans

Protein content in soybean is a key determinant of its nutritional and economic value. The paper investigated the feasibility of terahertz (THz) spectroscopy and dimensionality reduction algorithms for the determination of protein content in soybean. First of all, the THz sample spectrum was data processed by pre-processing or dimensionality reduction algorithms. Secondly, by calibration set, using partial least squares regression (PLSR), genetic algorithms-support vector regression (GA-SVR), grey wolf optimizer-support vector regression (GWO-SVR) and back propagation neural network (BPNN) were respectively used to model protein content determination. Afterwards, the model was validated by the prediction set. Ultimately, the BPNN model combined with linear discriminant analysis (LDA) for related coefficient of prediction set (Rp), root mean square error of prediction set (RMSEP), relative standard deviation (RSD), the time required for the operation was respectively 0.9677, 1.2467%, 3.3664%, and 53.51 s. The experimental results showed that the rapid and accurate quantitative determination of protein in soybean using THz spectroscopy is feasible after a suitable dimensionality reduction algorithm.

Application of Fourier transform near-infrared spectroscopy combined with GC in rapid and simultaneous determination of essential components in Amomum villosum

A method is described using rapid and sensitive Fourier transform near-infrared spectroscopy combined with Gas Chromatograpy internal standard method detection for the simultaneous identification and determination of three bioactive compounds in Amomum villosum samples. Partial least squares regression is selected as the analysis type and multiplicative scatter correction, second derivative, and SNV were adopted for the spectral pretreatment. The correlation coefficients (R) of the calibration models were above 0.95 and the root mean square error of predictions were under 0.8. The developed models were applied to unknown samples with satisfantory results. The established method was validated and can be applied to the intrinsic quality control of Amomum villosum.