返回 
加入收藏
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
x
请在关注微信后，向客服人员索取文件
| 篇名： | 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 |
| TITLE： | Discrimination of processing degree of Zingiber officinale charcoal and analysis of the correlation between color and component based on machine vision system |
| 摘要： | 目的基于机器视觉系统探讨姜炭炮制程度判别及颜色-成分含量相关性，为姜炭炮制程度控制和质量评价提供参考。方法采用高效液相色谱法测定干姜及其不同炮制程度姜炭饮片中6-姜酚、8-姜酚、10-姜酚、6-姜烯酚、姜酮5种成分的含量；采用机器视觉技术获得饮片图像并提取饮片RGB、L*a*b*、HSV3个不同颜色空间的颜色特征，采用机器学习，如主成分分析（PCA）、线性判别分析（LDA）、偏最小二乘法-判别分析（PLS-DA）和支持向量机（SVM）等方法对姜炭不同炮制程度姜炭建立定性判别模型，将颜色特征值与测得的5种成分含量进行相关性分析，建立颜色-成分的定量预测模型。结果随着炮制程度的加深，姜酮在炮制后产生且含量先增加后降低，标炭饮片中含量最高；6-姜酚、8-姜酚和10-姜酚的含量逐渐降低；6-姜烯酚含量先增加后降低。基于饮片图像颜色客观量化结合有监督判别模式识别方法的LDA和SVM建立的定性判别模型，其交叉验证训练预测准确率达到100%，外部验证准确率达到95.83%。基于饮片图像颜色客观量化结合SVM建立5种成分含量预测模型，RPD值均大于2，各成分的R2P与R2C值中姜酮为0.6339与0.6833，其他成分值均大于0.75，说明SVM对除姜酮以外的4种成分均有较好的预测能力。结论基于机器视觉系统对姜炭炮制程度的判别和含量预测模型效果良好，可为姜炭饮片的质量控制和炮制程度的判断提供参考。 |
| 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. |
| 期刊： | 2022年第33卷第22期 |
| 作者： | 张一凡，周苏娟，孟江，左蓉，林华坚，孙悦，王淑美 |
| AUTHORS： | ZHANG Yifan，ZHOU Sujuan，MENG Jiang，ZUO Rong，LIN Huajian，SUN Yue，WANG Shumei |
| 关键字： | 姜炭；机器视觉；机器学习；质量评价；炮制程度 |
| KEYWORDS： | Zingiber officinale charcoal； machine vision； machine learning； quality evaluation； processing degree |
| 阅读数： | 160 次 |
| 本月下载数： | 3 次 |
* 注：未经本站明确许可，任何网站不得非法盗链资源下载连接及抄袭本站原创内容资源!在此感谢您的支持与合作！
