Discrimination of processing degree of Zingiber officinale charcoal and analysis of the correlation between color and component based on machine vision system
- VernacularTitle:基于机器视觉系统的姜炭炮制程度判别及颜色-成分相关性分析
- Author:
Yifan ZHANG
1
;
Sujuan ZHOU
2
;
Jiang MENG
1
;
Rong ZUO
1
;
Huajian LIN
1
;
Yue SUN
1
;
Shumei WANG
1
Author Information
1. School of Traditional Chinese Medicine，Guangdong Pharmaceutical University/Key Laboratory of Digital Quality Evaluation of Chinese Materia Medica，State Administration of Traditional Chinese Medicine/Engineering Technology Research Center for Chinese Materia Medica Quality of Guangdong Universities and Colleges （Province），Guangzhou 510006，China
2. College of Medical Information Engineering，Guangdong Pharmaceutical University，Guangzhou 510006，China
- Publication Type:Journal Article
- Keywords:
Zingiber officinale charcoal;
machine vision;
machine learning;
quality evaluation;
processing degree
- From:
China Pharmacy
2022;33(22):2712-2718
- CountryChina
- Language:Chinese
-
Abstract:
OBJECTIVE To explore the discrimination of processing degree of Zingiber officinale charcoal and the correlation between color and component content based on machine vision system， and provide reference for quality evaluation and processing degree control of Z. officinale charcoal. METHODS High-performance liquid chromatography method was used to determine the contents of 5 components in Z. officinale charcoal and its different processed products， such as 6-gingerol， 8-gingerol， 10-gingerol， 6-shogaol， gingerone. Machine vision system was used to obtain the image of the decoction pieces and extract the color features of the decoction pieces in RGB， L*a*b* and HSV color spaces. Machine learning methods， such as principal component analysis （PCA）， linear discriminant analysis （LDA）， partial least squares-discriminant analysis （PLS-DA） and support vector machine （SVM）， were used to establish qualitative identification model for Z. officinale charcoal processed products of different processing degree. The correlation between the color eigenvalues and the contents of measured 5 components were analyzed， and the color- component content prediction model was established.RESULTS With the deepening of processing， gingerone was produced after processing and the content firstly increased and then decreased， and the content of gingerone in standard carbon was the highest； the contents of 6-gingerol， 8-gingerol and 10-gingerol decreased gradually； the content of 6-shogaol increased firstly and then decreased. The prediction accuracy of qualitative discriminant model， which was established on the basis of objective quantization of image and color combined with LDA and SVM of supervised discriminant pattern recognition method， reached 100% in cross-validation training and 95.83% in the external validation. Content prediction model of 5 components was established on the basis of objective quantization of image and color combined with SVM， the RPD values were all greater than 2， the R2P and R2C values of gingerone were 0.633 9 and 0.683 3， and the values of other components were all greater than 0.75， indicating SVM had good prediction ability for the contents of 4 components except for gingerone. CONCLUSIONS The machine vision system is excellent for the discrimination of the processing degree of Z. officinale charcoal and the content prediction， which can provide a reference for the quality control of Z. officinale charcoal decoction pieces and the judgment of the processing degree.
