Chemical Profiling and Quality Evaluation of Raw and Vinegar-Processing Frankincense by Multiple UPLC-MS/MS Techniques
PHYTOCHEMICAL ANALYSIS（2024）
Tianjin Univ Tradit Chinese Med
Abstract
IntroductionFrankincense is used for analgesic, tumor-suppressive, and anti-inflammatory treatments in Traditional Chinese Medicine but poses toxicological concerns. Vinegar processing is a common technique used to reduce the toxicity of frankincense.ObjectiveThis study aimed to investigate the chemical composition and quality evaluation of raw and vinegar-processing frankincense by multiple UPLC-MS/MS techniques. Additionally, we purposed refining the vinegar processing technique and identifying potentially harmful ingredients in the raw frankincense.MethodologySub-chronic oral toxicity studies were conducted on raw and vinegar-processing frankincense in rats. The composition of frankincense was identified by UPLC-Q-TOF-MS/MS. Chemometrics were used to differentiate between raw and vinegar-processing frankincense. Potential chemical markers were identified by selecting differential components, which were further exactly determined by UPLC-QQQ-MS/MS. Moreover, the viability of the HepG2 cells of those components with reduced contents after vinegar processing was assessed.ResultsThe toxicity of raw frankincense is attenuated by vinegar processing, among which vinegar-processing frankincense (R40) (herb weight: rice vinegar weight = 40:1) exhibited the lowest toxicity. A total of 83 components were identified from frankincense, including 40 triterpenoids, 37 diterpenoids, and 6 other types. The contents of six components decreased after vinegar-processing, with the lowest levels in R40. Three components, specifically 3 alpha-acetoxy-11-keto-beta-boswellic acid (AKBA), 3 alpha-acetoxy-alpha-boswellic acid (alpha-ABA), and 3 alpha-acetoxy-beta-boswellic acid (beta-ABA), inhibited the viability of HepG2 cells. The processing of frankincense with vinegar at a ratio of 40:1 could be an effective method of reducing the toxicity in raw frankincense.ConclusionOur research improves understanding of the toxic substance basis and facilitates future assessments of frankincense quality. Frankincense is commonly used in clinics but poses toxicological concerns. This study examines the chemical composition and quality of raw and vinegar-processing frankincense using UPLC-MS/MS. A total of 83 components were identified from frankincense, with six decreasing after vinegar-processing. Three components-AKBA, alpha-ABA, and beta-ABA-inhibited HepG2 cell viability. The toxicity of raw frankincense is attenuated by vinegar-processing, among which R40 (herb weight: rice vinegar weight = 40:1) exhibited the lowest toxicity.
MoreTranslated text
Key words
chemometrics,frankincense,toxicity,UPLC-MS/MS,vinegar processing
上传PDF
View via Publisher
AI Read Science
AI Summary
AI Summary is the key point extracted automatically understanding the full text of the paper, including the background, methods, results, conclusions, icons and other key content, so that you can get the outline of the paper at a glance.
Example
Background
Key content
Introduction
Methods
Results
Related work
Fund
Key content
- Pretraining has recently greatly promoted the development of natural language processing (NLP)
- We show that M6 outperforms the baselines in multimodal downstream tasks, and the large M6 with 10 parameters can reach a better performance
- We propose a method called M6 that is able to process information of multiple modalities and perform both single-modal and cross-modal understanding and generation
- The model is scaled to large model with 10 billion parameters with sophisticated deployment, and the 10 -parameter M6-large is the largest pretrained model in Chinese
- Experimental results show that our proposed M6 outperforms the baseline in a number of downstream tasks concerning both single modality and multiple modalities We will continue the pretraining of extremely large models by increasing data to explore the limit of its performance
Upload PDF to Generate Summary
Must-Reading Tree
Example
Generate MRT to find the research sequence of this paper
Data Disclaimer
The page data are from open Internet sources, cooperative publishers and automatic analysis results through AI technology. We do not make any commitments and guarantees for the validity, accuracy, correctness, reliability, completeness and timeliness of the page data. If you have any questions, please contact us by email: report@aminer.cn
Chat Paper