ABSTRACT
ObjectiveIn order to establish a systematic quality evaluation system for Fritillariae Cirrhosae Bulbus adulteration， portable near-infrared （NIR） spectroscopy was used to identify Fritillariae Cirrhosae Bulbus and its adulterants and detect their adulteration quantity. MethodA total of 72 batches of Fritillariae Cirrhosae Bulbus samples were collected and 570 batches of adulterated products （dry bulbs of Fritillaria thunbergii， F. ussuriensis， F. pallidiflora and F. hupehensis， Bulbus Tulipae， flour） were prepared， NIR spectral data of samples were collected by the portable NIR spectrometer. Linear discriminant analysis （LDA） was used to establish the qualitative correction models of Fritillariae Cirrhosae Bulbus-adulterants and adulterants of different categories， partial least squares （PLS） was used to establish the quantitative correction models of adulteration quantity of different kinds of adulterants. ResultThe recognition rates of qualitative analysis model of Fritillariae Cirrhosae Bulbus and its adulterants were 99.49% （calibration set） and 100.00% （validation set）， respectively. In different adulterant models， the recognition rates of calibration set and validation set were 70.47% and 73.68%， respectively. Moreover， the correlation coefficients of validation set （R2P） of the six quantitative models of adulteration ratio were 0.840 2 （Fritillariae Cirrhosae Bulbus adulterated with F. thunbergii dry bulbs）， 0.960 2 （Fritillariae Cirrhosae Bulbus adulterated with F. ussuriensis dry bulbs）， 0.765 7 （Fritillariae Cirrhosae Bulbus adulterated with F. pallidiflora dry bulbs）， 0.902 5 （Fritillariae Cirrhosae Bulbus adulterated with F. hupehensis dry bulbs）， 0.957 4 （Fritillariae Cirrhosae Bulbus adulterated with Bulbus Tulipae）， 0.976 1 （Fritillariae Cirrhosae Bulbus adulterated with flour）， the root mean square error of prediction （RMSEP） were 10.948 5， 5.463 9， 13.256 4， 8.549 2， 5.655 3， 4.235 6， respectively. The two qualitative models and six quantitative models showed good prediction performance. ConclusionThe portable NIR spectroscopy can be used to identify Fritillariae Cirrhosae Bulbus and its adulterants in real time， the method is rapid and accurate， which can meet the requirements of nondestructive identification of Fritillariae Cirrhosae Bulbus on site.
ABSTRACT
Objective:To propose a new method for detecting and evaluating traditional Chinese medicine （TCM） by artificial intelligence and machine vision technology. Method:Taking Fritillariae Cirrhosae Bulbus， Crataegi Fructus and Pinelliae Rhizoma as the research objects， big data of pictures was collected by machine vision and the image database was established. Through the intelligent analysis of the external characteristics of TCM， the deep convolutional neural network model was established to realize the functions of location detection and variety identification by means of deep learning， so as to significantly improve the accuracy of rapid identification of TCM. Result:The classification accuracy of 11 kinds of Chinese herbal pieces （raw， fried， parched and charred products of Crataegi Fructus， Pinelliae Rhizoma， Pinelliae Rhizoma Praeparatum Cum Zingibere et Alumine， Pinelliae Rhizoma Praeparatum， Pinelliae Rhizoma Praeparatum Cum Alumine and three products of Fritillariae Cirrhosae Bulbus） could be more than 99%， and the average recognition accuracy of specific categories could reach more than 97%. Conclusion:The intelligent identification technology of TCM decoction pieces realized by deep learning algorithms has the advantages of simplicity， rapidity， high precision and quantifiable detection， which can provide technical support for the quality detection and evaluation of TCM， and enrich the research ideas of quality evaluation of TCM.
ABSTRACT
In recent years, near-infrared spectroscopy has developed into an analytical technique widely used in various fields. Because of its advantages of fast, green and non-destructive, it plays an increasingly prominent role in the field of traditional Chinese medicine (TCM) analysis. However, due to the complexity and overlap of spectra, near-infrared spectroscopy needs to be combined with chemometrics for analysis and calculation. The principle, application scope, advantages and limitations of near infrared spectroscopy and chemometrics are summarized in detail, in addition, their combined applications in the identification of the origin, authenticity, processed products, composition prediction and water content detection of TCM are reviewed. The authors discussed and analyzed the joint application of near-infrared spectroscopy and chemometrics in the field of TCM analysis, and summarized the unique advantages of the combined technology in the field of TCM, which had certain guiding significance for medical workers to better use this technology.
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Objective To establish the HPLC fingerprint and simultaneous method for the determination of 11 saponins in steamed Panax notoginseng from different origins. Methods Agilent Zorbax SB-C18 (250 mm × 4.6 mm, 5 μm) column was adopted, the mobile phase consisted of acetonitrile-water with gradient elution at the flow rate of 1.0 mL/min (elution program: 0-30 min, 20% acetonitrile; 30-60 min, 20%-45% acetonitrile; 60-88 min, 45%-75% acetonitrile; 88-98 min, 75% acetonitrile; 98-100 min, 75%-20% acetonitrile), and the detection wavelength was 203 nm. The establishment of steamed P. notoginseng HPLC fingerprint, and determination method of notoginsenoside R1, ginsenoside Rg1, Re, Rb1, 20S-Rh1, 20R-Rh1, Rd, Rk3, Rh4, 20S-Rg3, and 20R-Rg3 index components were studied methodologically. The content of 11 saponins in 10 batches was determined. Results The HPLC fingerprint was establish, and thirty common peaks were selected as characteristic peaks of steamed P. notoginseng. The similarities of different samples from 10 areas were 0.941, 0.938, 0.945, 0.951, 0.913, 0.909, 0.920, 0.928, 0.917, and 0.919. In quantitative analysis, eleven saponins were separated well and the average content was 5.274, 20.515, 2.838, 23.651, 3.476, 1.407, 5.239, 1.784, 1.580,0.904, and 0.294 mg/g, respectively. Additionally, all calibration curves showed good linear regression relationship, with correlation indexes of 0.999 7, 0.999 5, 0.999 5, 0.999 7, 0.999 7, 0.999 6, 0.999 7, 0.999 6, 0.999 7, 0.999 7, and 0.999 6; The average recoveries were 101.23%, 98.52%, 97.67%, 99.62%, 98.17%, 98.92%, 99.44%, 99.14%, 100.25%, 98.23%, and 96.89%, with RSDs of 1.35%, 1.58%, 2.44%, 1.05%,1.48%, 1.56%, 0.85%, 2.34%, 2.85%, 1.25%, and 1.08%. Conclusion This method is sensitive, accurate and reproducible. It can be used to provide a reference for the standard and evaluation of quality of steamed P. notoginseng.
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AIM To analyze the optimal "Huohou" (fermentation time and temperature) of fermentation process of Jianqu Leaven [fermentation product composed of medicinal materials (Artemisiae annuae Herba,Menthae haplocalycis Herba,Angelicae dahuricae Radix,etc.),wheat bran and flour).METHODS The colors and odors of twelve batches of samples during fermentation process were detected by machine vision and electronic nose technologies,respectively.The contents of total sugar and total protein,together with activities of amylase,protease and lipase,were determined to analyze constituent changes.RESULTS The content of total sugar was significantly decreased,but that of total protein was slightly increased,and the activities of three enzymes were increased to different degrees.At temperature of 28-30 ℃ and relative humidity of 70％-80％,the optimal fermentation terminal point of Jianqu Leaven was 30-36 h.CONCLUSION The Jianqu Leaven obtained by this fermentation method is totally covered with white mould,and has wine flavor,which meets the requirement of traditional processing.