Quality Evaluation of Tibetan Medicine Meconopsis Quintuplinervia Regel. Based on Data Fusion and Study on Its Environmental Impact Factors
10.13748/j.cnki.issn1007-7693.20222172
- VernacularTitle:基于数据融合的藏药五脉绿绒蒿品质评价及其环境影响因素研究
- Author:
LONG Ruolan
1
,
2
;
FENG Dan
1
,
2
;
LI Peipei
1
,
2
;
LI Duo
1
,
2
;
SUN Jing
1
Author Information
1. Qinghai Key Laboratory of Qinghai-Tibet Plateau Biological Resource, Northwest Institute of Plateau Biology, Chinese Academy of Sciences, Xining 810008, China
2. University of Chinese Academy of Science, Beijing 101408, China
- Publication Type:Journal Article
- Keywords:
infrared spectroscopy;
data fusion;
Meconopsis quintuplinervia Regel.;
total alkaloids;
quality evaluation;
environment factor
- From:
Chinese Journal of Modern Applied Pharmacy
2023;40(13):1810-1817
- CountryChina
- Language:Chinese
-
Abstract:
OBJECTIVE To develop a model for the quick quality evaluation of total alkaloid content and to explore the effects of environmental factors on the accumulation of alkaloids in Meconopsis quintuplinervia Regel.. METHODS The total alkaloid contents of Meconopsis quintuplinervia Regel. from 19 different areas in Qinghai-Tibet Plateau were detected by ultraviolet spectrophotometry. The near-infrared(NIR), mid-infrared(MIR) and mid-infrared with attenuated total reflectance(ATR) spectroscopy information of Meconopsis quintuplinervia Regel. were determined and integrated, respectively. Combined spectroscopy information with the content of total alkaloid, three single-spectrum and two data fusion quantitative models were performed by principal component regression. Pearson correlation analysis was utilized to explain the relationship between geographical factors, climatic factors, soil factors, and total alkaloid content. RESULTS It was found that the data fusion model of NIR coupled with ATR was the best model, with the related coefficient of calibration 0.980 3 and prediction 0.997 2, the root mean square error of calibration 0.060 3 and prediction 0.063 3, and residual predictive deviation>4. And altitude and annual precipitation were significantly positively correlated with total alkaloid content, while latitude and annual temperature were significantly negatively correlated with total alkaloid content. Moreover, total nitrogen and alkali-hydrolyzable nitrogen were positively correlated with total alkaloid content. CONCLUSION The spectral data fusion can enhance the prediction ability of the model. The accumulation of alkaloids was affected by both large geographical environmental factor, small environmental soil factors and meteorological factors. This study can provide a scientific basis for rapid quality evaluation and rational planning of resource utilization of Meconopsis quintuplinervia Regel.
