Cold-hot nature identification of Chinese herbal medicines based on the similarity of HPLC fingerprints

Wei, Guohui; Jia, Ronghao; Kong, Zhiyong; Ji, Chengjie; Wang, Zhenguo

doi:10.3389/fchem.2022.1002062

Cited by 3 publications

(2 citation statements)

References 18 publications

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“…Using the chromatogram fingerprint of sample 1 (Figure 3) as a reference atlas, the similarity analysis of the fingerprints of Yuganzi was carried out using the Similarity Evaluation System for Chromatographic Fingerprints of Traditional Chinese Medicine (version 2012 A). The similarity values for the 10 batches (S1-S10) of Yuganzi from Yunnan were larger than 0.995 (Table 3), indicating good consistency among the samples from the same source [27,28]. The similarity values of the three samples (S11-S13) from India, Gaoligong Mountain, and Fujian, respectively, were less than or equal to 0.986, indicating differences in composition among the samples from different regions.…”

Section: Similarity Analysis Of the Hplc Fingerprints Of Yuganzimentioning

confidence: 95%

Quality Evaluation and Identification of Phyllanthi fructus (Yuganzi) Based on the Spectrum-Effect Relationship

Zhou,

Wu,

Liu

et al. 2024

Separations

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To establish a quality evaluation and identification method for Phyllanthi fructus (Yuganzi), the spectrum-effect relationship was explored. A high-performance liquid chromatography (HPLC) fingerprint was established using ultraviolet spectrophotometry, and the in vitro antioxidant activity was determined using a total antioxidant capacity assay kit. Similarity analysis, hierarchical cluster analysis (HCA), and partial least squares regression (PLSR) were performed to establish the spectrum-effect relationships. Thirteen batches of Yuganzi were collected for testing. The results revealed that the optimal chromatographic conditions for the HPLC fingerprint were as follows: the mobile phase consisted of 0.1% phosphoric acid solution (A) and acetonitrile (B), the detection wavelength was 214 nm, the column temperature was 30 °C, and the flow rate was 0.8 mL/min. Among the batches of samples, the similarity values of 10 samples (S1–S10) from Yunnan were larger than 0.995; the similarity values of 3 samples (S11–S13) from India, Gaoligong Mountain, and Fujian were less than or equal to 0.986. Furthermore, nineteen characteristic peaks of Yuganzi were calibrated using fingerprint evaluation software. The study on the spectrum-effect relationship further revealed that compounds corresponding to peaks 5 and 8 were potentially key ingredients for the quality evaluation and identification of Yuganzi, closely related to the stable antioxidant activities of Yuganzi. The spectrum-effect relationship is an agile and efficient approach that can ensure the intra-assay stability of Yuganzi from same region and identify Yuganzi from different regions. Compounds with antioxidant activity can be identified as quality markers for Yuganzi.

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Section: Similarity Analysis Of the Hplc Fingerprints Of Yuganzimentioning

confidence: 95%

Quality Evaluation and Identification of Phyllanthi fructus (Yuganzi) Based on the Spectrum-Effect Relationship

Zhou,

Wu,

Liu

et al. 2024

Separations

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“…Typically, biological techniques and chemical methods, such as mass spectrometry, gas chromatography, etc., can be used for adulteration detection [11][12][13]. However, these analyses require highly trained professionals, and also it is time-consuming.…”

Section: Introductionmentioning

confidence: 99%

Rapid identification of medicinal plants via visual feature-based deep learning

Tan,

Tian,

et al. 2024

Plant Methods

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Background Traditional Chinese Medicinal Plants (CMPs) hold a significant and core status for the healthcare system and cultural heritage in China. It has been practiced and refined with a history of exceeding thousands of years for health-protective affection and clinical treatment in China. It plays an indispensable role in the traditional health landscape and modern medical care. It is important to accurately identify CMPs for avoiding the affected clinical safety and medication efficacy by the different processed conditions and cultivation environment confusion. Results In this study, we utilize a self-developed device to obtain high-resolution data. Furthermore, we constructed a visual multi-varieties CMPs image dataset. Firstly, a random local data enhancement preprocessing method is proposed to enrich the feature representation for imbalanced data by random cropping and random shadowing. Then, a novel hybrid supervised pre-training network is proposed to expand the integration of global features within Masked Autoencoders (MAE) by incorporating a parallel classification branch. It can effectively enhance the feature capture capabilities by integrating global features and local details. Besides, the newly designed losses are proposed to strengthen the training efficiency and improve the learning capacity, based on reconstruction loss and classification loss. Conclusions Extensive experiments are performed on our dataset as well as the public dataset. Experimental results demonstrate that our method achieves the best performance among the state-of-the-art methods, highlighting the advantages of efficient implementation of plant technology and having good prospects for real-world applications.

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Bitter–Pungent Flavor Identification Based on Ingredient Information Similarity of Chinese Herbal Medicines

Wei,

Qiu,

et al. 2023

Journal of AOAC International

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Background The flavor theory of Chinese herbal medicines (CHMs) is one of the core theories of traditional Chinese medicine (TCM). Accurate flavor identification of CHMs is essential to guide the clinical application of CHMs. Objective To develop a new method for flavor identification of CHMs according to ingredient information of CHMs. Methods It was found that the chemical basis of medicinal flavors was CHM ingredients. We developed a bitter-pungent flavor identification scheme to build a relationship between medicinal flavors and CHM ingredients. We firstly proposed a scientific hypothesis that “CHMs with similar flavors should have similar chemical basis”. To test this scientific hypothesis, we then explored an intelligent algorithm for bitter-pungent flavor identification of CHMs based on the information similarity of CHM ingredients. Gas chromatography (GC) was used to separate the chemical ingredients of CHMs and analyze the ingredient information of CHMs. A distance metric learning algorithm was built to measure the similarity of GC chemical fingerprints. A bitter-pungent flavor identification scheme (BPFI) was proposed to predict the bitter-pungent flavor of CHMs. Finally, a number of experiments were built to evaluate the identification performance of our scheme. Results Comparing with classical algorithms, our proposed BPFI scheme has better flavor prediction performance. The total identification accuracy of our BPFI scheme reached 0.843. The area under ROC curve (AUC) is 0.899. Conclusions The experimental results confirmed our inference that the chemical basis of CHM flavors was CHM ingredients, and implied that CHMs with similar flavors had similar composition of substances. The BPFI model was proved to be effective and feasible. Highlights To construct a flavor identification model for analysis of bitter-pungent flavor of CHMs according to ingredient information of CHMs.

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Cold-hot nature identification of Chinese herbal medicines based on the similarity of HPLC fingerprints

Cited by 3 publications

References 18 publications

Quality Evaluation and Identification of Phyllanthi fructus (Yuganzi) Based on the Spectrum-Effect Relationship

Quality Evaluation and Identification of Phyllanthi fructus (Yuganzi) Based on the Spectrum-Effect Relationship

Rapid identification of medicinal plants via visual feature-based deep learning

Bitter–Pungent Flavor Identification Based on Ingredient Information Similarity of Chinese Herbal Medicines

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