Application of Authentication Evaluation Techniques of Ethnobotanical Medicinal Plant Genus <i>Paris</i>: A Review

Pei, Yifei; Zhang, Qingzhi; Wang, Yuanzhong

doi:10.1080/10408347.2019.1642734

Cited by 28 publications

(12 citation statements)

References 92 publications

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“…In the simultaneous determination of multiple components, one of the typical components can be selected as the internal standard to establish the RCF (f km ) between the internal component and other components, as showed in Equation ( 1). Through the f km of the components to be tested and the internal standard component, the concentration of other components in the sample can be calculated according to Equation (2).…”

Section: Principle Of Quantitative Analysis Of Multi-components Via a Single Marker Methodsmentioning

confidence: 99%

“…Rhizoma Paridis contains saponins, nucleosides, sterones, flavonoids, and volatile oils [1]. Saponins are considered as the main markers of the current quality regulation [2]. However, Rhizoma Paridis is traditionally decocted with water, so it is necessary to explore the possibility of its water-soluble components, F I G U R E 1 Chemical structures of nine nucleosides such as nucleosides, as the quality control markers.…”

Section: Introductionmentioning

confidence: 99%

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Determination of nine nucleosides in Rhizoma Paridis by quantitative analysis of multi‐components via a single marker method

Xie

Wang

et al. 2021

J of Separation Science

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In this work, a new quantitative analysis method of multi-components analysis via a single marker strategy coupled with high-performance liquid chromatography (HPLC) analysis, was proposed to analyze nine nucleosides (cytidine, uridine, 2′-deoxyuridine, inosine, guanosine, 2′-deoxyguanosine, thymidine, adenosine, and 2′-deoxyadenosine) as quality control markers in Rhizoma Paridis. Guanosine was set as the internal reference substance, whose content in Rhizoma Paridis was determined using conventional external standard method. Then, relative correction factors between guanosine and the other eight nucleosides were measured respectively. The amounts of the other eight components were calculated according to the relative correction factors by the quantitative analysis of multi-components via a single marker method. Finally, the result of vector angle cosine analysis showed that there was no significant difference of the contents between the external standard method and the quantitative analysis of multi-components via a single marker method, indicating that the quantitative analysis of multi-components via a single marker method can be applied for the quality control of Rhizoma Paridis. As far as we know, this is also the first report to analyze nucleosides by the quantitative analysis of multi-components via a single marker method, providing an efficient and promising quality assessment method for other traditional Chinese medicine containing nucleosides.

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Section: Principle Of Quantitative Analysis Of Multi-components Via a Single Marker Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Determination of nine nucleosides in Rhizoma Paridis by quantitative analysis of multi‐components via a single marker method

Xie

Wang

et al. 2021

J of Separation Science

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“…Traditional identification and evaluation techniques for medicinal plants mainly include the technology of DNA barcoding, macroscopic identification, microscopic identification, chromatography, spectroscopy, etc. (Pang et al, 2011;Pei et al, 2020;Liu et al, 2021). Among them, spectroscopy has the advantages of simplicity, speed, economy, and high throughput, which can fully characterize the chemical information of samples with complex mixed systems (Pasquini, 2018).…”

Section: Introductionmentioning

confidence: 99%

Superiority Verification of Deep Learning in the Identification of Medicinal Plants: Taking Paris polyphylla var. yunnanensis as an Example

Yue¹,

Li²,

Wang³

2021

Front. Plant Sci.

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Medicinal plants have a variety of values and are an important source of new drugs and their lead compounds. They have played an important role in the treatment of cancer, AIDS, COVID-19 and other major and unconquered diseases. However, there are problems such as uneven quality and adulteration. Therefore, it is of great significance to find comprehensive, efficient and modern technology for its identification and evaluation to ensure quality and efficacy. In this study, deep learning, which is superior to conventional identification techniques, was extended to the identification of the part and region of the medicinal plant Paris polyphylla var. yunnanensis from the perspective of spectroscopy. Two pattern recognition models, partial least squares discriminant analysis (PLS-DA) and support vector machine (SVM), were established, and the overall discrimination performance of the three types of models was compared. In addition, we also compared the effects of different sample sizes on the discriminant performance of the models for the first time to explore whether the three models had sample size dependence. The results showed that the deep learning model had absolute superiority in the identification of medicinal plant. It was almost unaffected by factors such as data type and sample size. The overall identification ability was significantly better than the PLS-DA and SVM models. This study verified the superiority of the deep learning from examples, and provided a practical reference for related research on other medicinal plants.

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“…After that, Cunningham et al (2018) assessed the scale of P. polyphylla trade, reviewing evidence about the impacts of wild harvest on P. polyphylla populations and the role of cultivation as an alternative to wild harvest. More recently, Pei et al (2020) reviewed medicinal records of ethnic minorities for P. polyphylla in China and indicated the applications of authentic evaluation techniques of ethnobotanical medicinal plant of genus P. polyphylla.…”

Section: Introductionmentioning

confidence: 99%

Genetic Diversity, Chemical Components, and Property of Biomass Paris polyphylla var. yunnanensis

Zhang

Park

et al. 2021

Front. Bioeng. Biotechnol.

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Paris polyphylla var. yunnanensis is a kind of biomass resource, which has important medicinal and economical values with a huge market. This review article aims to summarize the recent development of biomass P. polyphylla var. yunnanensis. The genetic diversity and chemical components of biomass P. polyphylla var. yunnanensis were reviewed based on the literature. Both the genetic diversity and genetic structure of biomass P. polyphylla var. yunnanensis were compared by using molecular marker technologies. All the extraction processes, harvest time, and drying methods on the chemical components were summarized in detail. The differences of arbuscular mycorrhizal fungi on the infection rate, diosgenin content, microorganisms, enzyme activities, rhizospheric environment, and endogenous hormones were discussed. This review article is beneficial for the applications of biomass P. polyphylla var. yunnanensis as a biomass resource in the biomedical field.

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Application of Authentication Evaluation Techniques of Ethnobotanical Medicinal Plant Genus Paris: A Review

Cited by 28 publications

References 92 publications

Determination of nine nucleosides in Rhizoma Paridis by quantitative analysis of multi‐components via a single marker method

Determination of nine nucleosides in Rhizoma Paridis by quantitative analysis of multi‐components via a single marker method

Superiority Verification of Deep Learning in the Identification of Medicinal Plants: Taking Paris polyphylla var. yunnanensis as an Example

Genetic Diversity, Chemical Components, and Property of Biomass Paris polyphylla var. yunnanensis

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