Traditional extraction methods of total saponins of Panax notoginseng include cold soaking method,water decoction method,alcohol reflux method,percolation method,macroporous resin adsorption method,and accelerated sol...Traditional extraction methods of total saponins of Panax notoginseng include cold soaking method,water decoction method,alcohol reflux method,percolation method,macroporous resin adsorption method,and accelerated solvent extraction( ASE) method. Modern extraction methods include ultrasonic extraction,microwave assisted extraction,supercritical CO_2 extraction,microbial fermentation assisted extraction,neural network model optimized extraction method,and multi-stage countercurrent extraction method. This paper discussed principles of these methods and compared their advantages and disadvantages.展开更多
Objective:To establish a deep-learning architecture based on faster region-based convolutional neural networks(Faster R-CNN)algorithm for detection and sorting of red ginseng(Ginseng Radix et Rhizoma Rubra)with intern...Objective:To establish a deep-learning architecture based on faster region-based convolutional neural networks(Faster R-CNN)algorithm for detection and sorting of red ginseng(Ginseng Radix et Rhizoma Rubra)with internal defects automatically on an online X-ray machine vision system.Methods:A Faster R-CNN based classifier was trained with around 20000 samples with mean average precision value(mAP)of 0.95.A traditional image processing method based on feedforward neural network(FNN)obtained a bad performance with the accuracy,recall and specificity of 69.0%,68.0%,and70.0%,respectively.Therefore,the Faster R-CNN model was saved to evaluate the model performance on the defective red ginseng online sorting system.Results:An independent set of 2000 red ginsengs were used to validate the performance of the Faster RCNN based online sorting system in three parallel tests,achieving accuracy of 95.8%,95.2%and 96.2%,respectively.Conclusion:The overall results indicated that the proposed Faster R-CNN based classification model has great potential for non-destructive detection of red ginseng with internal defects.展开更多
Objective: Angelicae Sinensis Radix(ASR, Danggui in Chinese), Cistanches Herba(CH, Roucongrong in Chinese), Ginseng Radix et Rhizoma(PG, Renshen in Chinese), and Panacis Quinquefolii Radix(PQ,Xiyangshen in Chinese), w...Objective: Angelicae Sinensis Radix(ASR, Danggui in Chinese), Cistanches Herba(CH, Roucongrong in Chinese), Ginseng Radix et Rhizoma(PG, Renshen in Chinese), and Panacis Quinquefolii Radix(PQ,Xiyangshen in Chinese), widely used as medicine and dietary supplement around the world, are susceptible to fungal and mycotoxin contamination. In this study, we aim to analyze their fungal community by DNA metabarcoding.Methods: A total of 12 root samples were collected from three main production areas in China. The samples were divided into four groups based on herb species, including ASR, CH, PG, and PQ groups. The fungal community on the surface of four root groups was investigated through DNA metabarcoding via targeting the internal transcribed spacer 2 region(ITS2).Results: All the 12 samples were detected with fungal contamination. Rhizopus(13.04%-74.03%),Aspergillus(1.76%-23.92%), and Fusarium(0.26%-15.27%) were the predominant genera. Ten important fungi were identified at the species level, including two potential toxigenic fungi(Penicillium citrinum and P. oxalicum) and eight human pathogenic fungi(Alternaria infectoria, Candida sake, Hyphopichia burtonii, Malassezia globosa, M. restricta, Rhizopus arrhizus, Rhodotorula mucilaginosa, and Ochroconis tshawytschae). Fungal community in ASR and CH groups was significantly different from other groups,while fungal community in PG and PQ groups was relatively similar.Conclusion: DNA metabarcoding revealed the fungal community in four important root herbs. This study provided an important reference for preventing root herbs against fungal and mycotoxin contamination.展开更多
基金Supported by the 12 th Five-Year TCM Key Discipline Chinese Medicine Chemistry Construction Program of State Administration of Traditional Chinese Medicine(Guo Zhong Yi Yao Ren Jiao Fa[2012]32)Key Discipline Chinese Medicine Chemistry Construction Program of Guangxi(Gui Jiao Ke Yan[2013]16)+2 种基金Natural Science Foundation Project of Guangxi(2013GXNSFAA019240)Program of Key Laboratory of Guangxi Universities on National Medicine in Youjiang River Basin(Gui Jiao Ke Yan[2014]14)Innovation and Entrepreneurship Ethnical Medicine Teaching Team Program of Guangxi Zhuang Autonomous Region(Gui Jiao Gao Jiao[2015]93&Gui Jiao Gao Jiao[2016]6)
文摘Traditional extraction methods of total saponins of Panax notoginseng include cold soaking method,water decoction method,alcohol reflux method,percolation method,macroporous resin adsorption method,and accelerated solvent extraction( ASE) method. Modern extraction methods include ultrasonic extraction,microwave assisted extraction,supercritical CO_2 extraction,microbial fermentation assisted extraction,neural network model optimized extraction method,and multi-stage countercurrent extraction method. This paper discussed principles of these methods and compared their advantages and disadvantages.
基金funded by National Natural Science Foundation of China(Grant No.82074276)Projects of International Cooperation of Traditional Chinese Medicine(Grant No.06102040NF020928)+1 种基金National S&T Major Project of China(Grant No.2018ZX09201011)Innovation Team and Talents Cultivation Program of National Administration of Traditional Chinese Medicine.(No.ZYYCXTD-D-202002)。
文摘Objective:To establish a deep-learning architecture based on faster region-based convolutional neural networks(Faster R-CNN)algorithm for detection and sorting of red ginseng(Ginseng Radix et Rhizoma Rubra)with internal defects automatically on an online X-ray machine vision system.Methods:A Faster R-CNN based classifier was trained with around 20000 samples with mean average precision value(mAP)of 0.95.A traditional image processing method based on feedforward neural network(FNN)obtained a bad performance with the accuracy,recall and specificity of 69.0%,68.0%,and70.0%,respectively.Therefore,the Faster R-CNN model was saved to evaluate the model performance on the defective red ginseng online sorting system.Results:An independent set of 2000 red ginsengs were used to validate the performance of the Faster RCNN based online sorting system in three parallel tests,achieving accuracy of 95.8%,95.2%and 96.2%,respectively.Conclusion:The overall results indicated that the proposed Faster R-CNN based classification model has great potential for non-destructive detection of red ginseng with internal defects.
基金supported by CAMS Innovation Fund for Medical Sciences(CIFMS)(No.2021-I2M-1-071).
文摘Objective: Angelicae Sinensis Radix(ASR, Danggui in Chinese), Cistanches Herba(CH, Roucongrong in Chinese), Ginseng Radix et Rhizoma(PG, Renshen in Chinese), and Panacis Quinquefolii Radix(PQ,Xiyangshen in Chinese), widely used as medicine and dietary supplement around the world, are susceptible to fungal and mycotoxin contamination. In this study, we aim to analyze their fungal community by DNA metabarcoding.Methods: A total of 12 root samples were collected from three main production areas in China. The samples were divided into four groups based on herb species, including ASR, CH, PG, and PQ groups. The fungal community on the surface of four root groups was investigated through DNA metabarcoding via targeting the internal transcribed spacer 2 region(ITS2).Results: All the 12 samples were detected with fungal contamination. Rhizopus(13.04%-74.03%),Aspergillus(1.76%-23.92%), and Fusarium(0.26%-15.27%) were the predominant genera. Ten important fungi were identified at the species level, including two potential toxigenic fungi(Penicillium citrinum and P. oxalicum) and eight human pathogenic fungi(Alternaria infectoria, Candida sake, Hyphopichia burtonii, Malassezia globosa, M. restricta, Rhizopus arrhizus, Rhodotorula mucilaginosa, and Ochroconis tshawytschae). Fungal community in ASR and CH groups was significantly different from other groups,while fungal community in PG and PQ groups was relatively similar.Conclusion: DNA metabarcoding revealed the fungal community in four important root herbs. This study provided an important reference for preventing root herbs against fungal and mycotoxin contamination.
