The Dynamic Accumulation Rules of Chemical Components in Different Medicinal Parts of Angelica sinensis by GC-MS

Chen, Yuying; Li, Qian; Qiu, Daiyu

doi:10.3390/molecules27144617

Cited by 6 publications

(2 citation statements)

References 7 publications

(7 reference statements)

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“…Through an analysis of the retention time and mass spectrum data of the compound, the obtained mass spectrum diagram was retrieved using the NIST14 standard mass spectrum library, and the chromatographic peak with a matching degree higher than 80% was selected for the qualitative identification of the compound. At the same time, the relative content of each component was calculated according to the peak area normalization method [35]. Four common components were identified in each period and part based on the chemical compositions of the various parts of A. dahurica during the different periods measured using GC-MS technology.…”

Section: Discussionmentioning

confidence: 99%

The Prediction of Antioxidant Q-Markers for Angelica dahurica Based on the Dynamics Change in Chemical Compositions and Network Pharmacology

Gao

2023

Molecules

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Objective: To clarify the accumulation and mutual transformation patterns of the chemical components in Angelica dahurica (A. dahurica) and predict the quality markers (Q-Markers) of its antioxidant activity. Method: The types of and content changes in the chemical components in various parts of A. dahurica during different periods were analyzed by using gas chromatography-mass spectrometry technology (GC-MS). The antioxidant effect of the Q-Markers was predicted using network pharmacological networks, and molecular docking was used to verify the biological activity of the Q-Markers. Result: The differences in the content changes in the coumarin compounds in different parts were found by using GC-MS technology, with the relative content being the best in the root, followed by the leaves, and the least in the stems. The common components were used as potential Q-Markers for a network pharmacology analysis. The component-target-pathway-disease network was constructed. In the molecular docking, the Q-Markers had a good binding ability with the core target, reflecting better biological activity. Conclusions: The accumulation and mutual transformation patterns of the chemical components in different parts of A. dahurica were clarified. The predicted Q-Markers lay a material foundation for the establishment of quality standards and a quality evaluation.

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Section: Discussionmentioning

confidence: 99%

The Prediction of Antioxidant Q-Markers for Angelica dahurica Based on the Dynamics Change in Chemical Compositions and Network Pharmacology

Gao

2023

Molecules

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“…The whole body of AS consists of the head and the tail, as shown in Figure 3. The various parts of AS exhibit distinct differences in their efficacy and medicinal properties, as reported by Chen et al [32]. Therefore, when extracting the shape features of AS, the shape features of the head, the tail, and the whole body were all extracted.…”

Section: Extraction Of Shape Featuresmentioning

confidence: 99%

Automated Grading of Angelica sinensis Using Computer Vision and Machine Learning Techniques

Zhang,

Xiao,

Wang

et al. 2024

Agriculture

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Angelica sinensis (Oliv.) Diels, a member of the Umbelliferae family, is commonly known as Danggui (Angelica sinensis, AS). AS has the functions of blood tonic, menstrual pain relief, and laxatives. Accurate classification of AS grades is crucial for efficient market management and consumer health. The commonly used method to classify AS grades depends on the evaluator’s observation and experience. However, this method has issues such as unquantifiable parameters and inconsistent identification results among different evaluators, resulting in a relatively chaotic classification of AS in the market. To address these issues, this study introduced a computer vision-based approach to intelligently grade AS. Images of AS at five grades were acquired, denoised, and segmented, followed by extraction of shape, color, and texture features. Thirteen feature parameters were selected based on difference and correlation analysis, including tail area, whole body area, head diameter, G average, B average, R variances, G variances, B variances, R skewness, G skewness, B skewness, S average, and V average, which exhibited significant differences and correlated with grades. These parameters were then used to train and test both the traditional back propagation neural network (BPNN) and the BPNN model improved with a growing optimizer (GOBPNN). Results showed that the GOBPNN model achieved significantly higher average testing precision, recall, F-score, and accuracy (97.1%, 95.9%, 96.5%, and 95.0%, respectively) compared to the BPNN model. The method combining machine vision technology with GOBPNN enabled efficient, objective, rapid, non-destructive, and cost effective AS grading.

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Non-Targeted Analytical Technology in Herbal Medicines: Applications, Challenges, and Perspectives

Wang

2022

Critical Reviews in Analytical Chemistry

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The Dynamic Accumulation Rules of Chemical Components in Different Medicinal Parts of Angelica sinensis by GC-MS

Cited by 6 publications

References 7 publications

The Prediction of Antioxidant Q-Markers for Angelica dahurica Based on the Dynamics Change in Chemical Compositions and Network Pharmacology

The Prediction of Antioxidant Q-Markers for Angelica dahurica Based on the Dynamics Change in Chemical Compositions and Network Pharmacology

Automated Grading of Angelica sinensis Using Computer Vision and Machine Learning Techniques

Non-Targeted Analytical Technology in Herbal Medicines: Applications, Challenges, and Perspectives

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