Article(id=1322631303162343750, tenantId=1146029695717560320, journalId=1302319053441957962, issueId=1322631259684192984, articleNumber=null, orderNo=null, doi=10.7501j.issn.0253-2670.2026.19.007, pmid=null, cstr=null, oa=null, hot=0, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=null, receivedDate=1775059200000, receivedDateStr=2026-04-02, revisedDate=null, revisedDateStr=null, acceptedDate=null, acceptedDateStr=null, onlineDate=1793269475974, onlineDateStr=2026-10-29, pubDate=null, pubDateStr=null, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1793269475974, onlineIssueDateStr=2026-10-29, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1793269475974, creator=13701087609, updateTime=1793269475974, updator=13701087609, issue=Issue{id=1322631259684192984, tenantId=1146029695717560320, journalId=1302319053441957962, year='2026', volume='57', issue='19', pageStart='7501', pageEnd='7928', issueExtLink='null', onlineDate='null', pubDate='1791734400000', pubDateStr='2026-10-12', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=1, specialIssue=null, createTime=1793269465608, creator='13701087609', updateTime=1793269554856, updator='13701087609', preIssue=null, nextIssue=null, articleTotal=null, ext={EN=IssueExt(id=1322631634101326494, tenantId=1146029695717560320, journalId=1302319053441957962, issueId=1322631259684192984, language=EN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=), CN=IssueExt(id=1322631634101326495, tenantId=1146029695717560320, journalId=1302319053441957962, issueId=1322631259684192984, language=CN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=)}, issueFiles=null, downloadFileDto=null}, startPage=7560, endPage=7572, ext={EN=ArticleExt(id=1322631303619522888, articleId=1322631303162343750, tenantId=1146029695717560320, journalId=1302319053441957962, language=EN, title=Visualization research on mixing uniformity of Zhisou Qingguo Wan based on pixel unmixing, columnId=null, journalTitle=Chinese Traditional and Herbal Drugs, columnName=null, runingTitle=null, highlight=null, articleAbstract=Objective To achieve quality control during the production of Zhisou Qingguo Wan (ZQW, 止嗽青果丸), the pixel unmixing method from remote sensing was introduced into the uniformity evaluation of traditional Chinese medicine preparations, so as to visually analyze the mixing uniformity of medicinal powder and honey in ZQW. Methods Multiple preprocessing methods, including Savitzky-Golay (SG) smoothing, first derivative (1D), second derivative (2D), standard normal variate (SNV) and multiplicative scatter correction (MSC) were adopted to optimize spectral data and eliminate interferences such as background noise and spectral scattering. Based on pure substance spectra combined with the mixture tuned matched filtering (MTMF) algorithm, the abundance scores and mixture feasibility scores of target endmembers were integrated. Material abundance estimation and binarization at the pixel level of mixed samples were then performed, enabling the visualization of the spatial distribution of medicinal powders. Meanwhile, a residual network (ResNet) model was constructed for the prediction and pixel-level visualization of honey content. Results Compared with traditional quantitative models such as partial least squares regression (PLSR), multiple linear regression (MLR), ridge regression (RR), least absolute shrinkage and selection operator (Lasso), random forest (RF) and ElasticNet, the R 2 of the prediction set was significantly improved from 0.837 6 to 0.931 1 with the proposed model. Conclusion The method combining pixel unmixing and deep learning intuitively presents the spatial distribution of medicinal raw materials and excipients in the preparation. A novel insight for the refined production and quality control of traditional Chinese medicine preparations is provided., authors=SHEN Tingting1 , ZHU Jieqiang1 , FANG Cuifen2 , DONG Ting2 , LUO Lei2 , YAN Jizhong1 , GONG Xingchu3 , ZHANG Hui1 , authorsList=SHEN Tingting, ZHU Jieqiang, FANG Cuifen, DONG Ting, LUO Lei, YAN Jizhong, GONG Xingchu, ZHANG Hui, authorCompany=1 College of Pharmaceutical Science, Zhejiang University of Technology, Hangzhou 310014, China; 目的 为实现止嗽青果丸生产过程的质量控制，将遥感领域的像元解混方法引入中药制剂的均匀度评价，以可视化分析止嗽青果丸粉末混合均匀度和蜂蜜混合均匀度。方法 采用SG平滑（Savitzky-Golay，SG）、一阶导数（first derivative，1D）、二阶导数（second derivative，2D）、标准正态变量变换（standard normal variate，SNV）、多元散射校正（multiplicative scatter correction，MSC）等多种预处理方法优化光谱数据，消除背景噪声等干扰。通过利用纯物质光谱，结合混合调谐匹配滤波解混（mixture tuned matched filtering，MTMF）算法，综合目标端元的丰度得分与混合可行性得分，对混合样本像素点进行物质丰度估计与二值化，从而实现药材粉末的空间分布可视化，进一步构建了残差网络（residual network，ResNet）模型对蜂蜜含量进行预测与像素级可视化。结果 ResNet模型与偏最小二乘回归（partial least squares regression，PLSR）、多元线性回归（multiple linear regression，MLR）、岭回归（ridge regression，RR）、最小绝对收缩和选择算子回归（least absolute shrinkage and selection operator，Lasso）、随机森林（random forest，RF）、ElasticNet等传统定量模型相比，预测集R ²从0.837 6显著提升至0.931 1。结论 像元解混结合深度学习模型直观展示了药材原料与辅料在制剂中的空间分布，为中药制剂的精细化生产与质量控制提供了新思路。, authors=沈婷婷1 , 朱捷强1 , 方翠芬2 , 董婷2 , 罗镭2 , 颜继忠1 , 龚行楚3 , 张慧1 , authorsList=沈婷婷, 朱捷强, 方翠芬, 董婷, 罗镭, 颜继忠, 龚行楚, 张慧, authorCompany=1 浙江工业大学药学院, 浙江 杭州 310014; Chin Herb Med , 2025, 17(3): 575-583.et al . Identification of crude drugs and processed drugs in Chinese herbal slices using hyperspectral imaging [J]. J Food Compos Anal , 2025, 148: 108669.et al . Rapid identification of Astragalus membranaceus processing with rice water based on intelligent color recognition and multi-source information fusion technology [J]. Chin Herb Med , 2025, 17(4): 724-733.2 含量[J]. 中国中药杂志, 2022, 47(7): 1864-1870.et al . Integrating multimodal data fusion for comprehensive characterization, antioxidant marker discovery, and geographical origin tracing of Platycodonis Radix [J]. Food Chem , 2026, 507: 148231.et al . Authenticity identification of Pinellia ternata based on hyperspectral imaging combined with spectral-texture multimodal data fusion strategy [J]. Microchem J , 2025, 218: 115133.et al . Hyperspectral BRDF based on UAV measurements can characterize optical properties of flat desert surfaces: A comprehensive comparison with laboratory and satellite data [J]. Remote Sens Environ , 2026, 334: 115228.et al . Strategies for analysing hyperspectral imaging data for food quality and safety issues-A critical review of the last 5 years [J]. Microchem J , 2025, 214: 113994.et al . Hyperspectral image compression with deep learning: A review [J]. Signal Process , 2026, 239: 110270.et al . Exploring Kolmogorov-Arnold networks for hyperspectral crop classification-An evaluative study [J]. Adv Space Res , 2026, 77(10): 10175-10193.et al . Rapid discrimination of Platycodonis Radix geographical origins using hyperspectral imaging and deep learning [J]. Optics , 2025, 6(4): 52.et al . Enhanced Raman hyperspectral imaging using RS-NMF: A novel regularized sparse non-negative matrix factorization for spectral unmixing [J]. Chemom Intell Lab Syst , 2026, 269: 105602.Spectrochim Acta A Mol Biomol Spectrosc , 2024, 323: 124868.et al . Research on predicting photosynthetic pigments in tomato seedling leaves based on near-infrared hyperspectral imaging and machine learning [J]. Microchem J , 2024, 204: 111076.et al . A two-step iterative algorithm for sparse hyperspectral unmixing via total variation [J]. Appl Math Comput , 2021, 401: 126059.
中草药
|药剂与工艺
2026
, 57
(19) :
7560
-7572
基于像元解混的止嗽青果丸混合均匀度可视化研究
全屏
沈婷婷1 , 朱捷强1 , 方翠芬2 , 董婷2 , 罗镭2 , 颜继忠1 , 龚行楚3 , 张慧1
作者信息
1 浙江工业大学药学院, 浙江 杭州 310014;
通讯作者:
张慧
作者简介:
沈婷婷: 沈婷婷，硕士研究生，研究方向为中药药效物质基础及质量评价。E-mail:stt718156428@163.com
Visualization research on mixing uniformity of Zhisou Qingguo Wan based on pixel unmixing
SHEN Tingting1 , ZHU Jieqiang1 , FANG Cuifen2 , DONG Ting2 , LUO Lei2 , YAN Jizhong1 , GONG Xingchu3 , ZHANG Hui1
Affiliations
1 College of Pharmaceutical Science, Zhejiang University of Technology, Hangzhou 310014, China;
doi: 10.7501j.issn.0253-2670.2026.19.007
文章导航
目的 为实现止嗽青果丸生产过程的质量控制，将遥感领域的像元解混方法引入中药制剂的均匀度评价，以可视化分析止嗽青果丸粉末混合均匀度和蜂蜜混合均匀度。方法 采用SG平滑（Savitzky-Golay，SG）、一阶导数（first derivative，1D）、二阶导数（second derivative，2D）、标准正态变量变换（standard normal variate，SNV）、多元散射校正（multiplicative scatter correction，MSC）等多种预处理方法优化光谱数据，消除背景噪声等干扰。通过利用纯物质光谱，结合混合调谐匹配滤波解混（mixture tuned matched filtering，MTMF）算法，综合目标端元的丰度得分与混合可行性得分，对混合样本像素点进行物质丰度估计与二值化，从而实现药材粉末的空间分布可视化，进一步构建了残差网络（residual network，ResNet）模型对蜂蜜含量进行预测与像素级可视化。结果 ResNet模型与偏最小二乘回归（partial least squares regression，PLSR）、多元线性回归（multiple linear regression，MLR）、岭回归（ridge regression，RR）、最小绝对收缩和选择算子回归（least absolute shrinkage and selection operator，Lasso）、随机森林（random forest，RF）、ElasticNet等传统定量模型相比，预测集R ²从0.837 6显著提升至0.931 1。结论 像元解混结合深度学习模型直观展示了药材原料与辅料在制剂中的空间分布，为中药制剂的精细化生产与质量控制提供了新思路。
止嗽青果丸
/
高光谱
/
像元解混
/
混合均匀度
/
可视化
Objective To achieve quality control during the production of Zhisou Qingguo Wan (ZQW, 止嗽青果丸), the pixel unmixing method from remote sensing was introduced into the uniformity evaluation of traditional Chinese medicine preparations, so as to visually analyze the mixing uniformity of medicinal powder and honey in ZQW. Methods Multiple preprocessing methods, including Savitzky-Golay (SG) smoothing, first derivative (1D), second derivative (2D), standard normal variate (SNV) and multiplicative scatter correction (MSC) were adopted to optimize spectral data and eliminate interferences such as background noise and spectral scattering. Based on pure substance spectra combined with the mixture tuned matched filtering (MTMF) algorithm, the abundance scores and mixture feasibility scores of target endmembers were integrated. Material abundance estimation and binarization at the pixel level of mixed samples were then performed, enabling the visualization of the spatial distribution of medicinal powders. Meanwhile, a residual network (ResNet) model was constructed for the prediction and pixel-level visualization of honey content. Results Compared with traditional quantitative models such as partial least squares regression (PLSR), multiple linear regression (MLR), ridge regression (RR), least absolute shrinkage and selection operator (Lasso), random forest (RF) and ElasticNet, the R 2 of the prediction set was significantly improved from 0.837 6 to 0.931 1 with the proposed model. Conclusion The method combining pixel unmixing and deep learning intuitively presents the spatial distribution of medicinal raw materials and excipients in the preparation. A novel insight for the refined production and quality control of traditional Chinese medicine preparations is provided.
Zhisou Qingguo Wan
/
hyperspectral
/
pixel unmixing
/
mixing uniformity
/
visualization
沈婷婷, 朱捷强, 方翠芬, 董婷, 罗镭, 颜继忠, 龚行楚, 张慧.
基于像元解混的止嗽青果丸混合均匀度可视化研究.
中草药,
2026
, 57
(19)
: 7560
-7572
.
DOI: 10.7501j.issn.0253-2670.2026.19.007
SHEN Tingting, ZHU Jieqiang, FANG Cuifen, DONG Ting, LUO Lei, YAN Jizhong, GONG Xingchu, ZHANG Hui.
Visualization research on mixing uniformity of Zhisou Qingguo Wan based on pixel unmixing[J].
Chinese Traditional and Herbal Drugs ,
2026
, 57
(19)
: 7560
-7572
.
DOI: 10.7501j.issn.0253-2670.2026.19.007
参考文献
引证文献
Zhao J, Tian G, Qu H B. Research on comprehensive quality consistency evaluation strategy for TCM granules: A case study with sugar-free Yangwei Granules produced by fluid-bed granulation [J]. Chin Herb Med , 2025, 17(3): 575-583.et al . Identification of crude drugs and processed drugs in Chinese herbal slices using hyperspectral imaging [J]. J Food Compos Anal , 2025, 148: 108669.et al . Rapid identification of Astragalus membranaceus processing with rice water based on intelligent color recognition and multi-source information fusion technology [J]. Chin Herb Med , 2025, 17(4): 724-733.2 含量[J]. 中国中药杂志, 2022, 47(7): 1864-1870.et al . Integrating multimodal data fusion for comprehensive characterization, antioxidant marker discovery, and geographical origin tracing of Platycodonis Radix [J]. Food Chem , 2026, 507: 148231.et al . Authenticity identification of Pinellia ternata based on hyperspectral imaging combined with spectral-texture multimodal data fusion strategy [J]. Microchem J , 2025, 218: 115133.et al . Hyperspectral BRDF based on UAV measurements can characterize optical properties of flat desert surfaces: A comprehensive comparison with laboratory and satellite data [J]. Remote Sens Environ , 2026, 334: 115228.et al . Strategies for analysing hyperspectral imaging data for food quality and safety issues-A critical review of the last 5 years [J]. Microchem J , 2025, 214: 113994.et al . Hyperspectral image compression with deep learning: A review [J]. Signal Process , 2026, 239: 110270.et al . Exploring Kolmogorov-Arnold networks for hyperspectral crop classification-An evaluative study [J]. Adv Space Res , 2026, 77(10): 10175-10193.et al . Rapid discrimination of Platycodonis Radix geographical origins using hyperspectral imaging and deep learning [J]. Optics , 2025, 6(4): 52.et al . Enhanced Raman hyperspectral imaging using RS-NMF: A novel regularized sparse non-negative matrix factorization for spectral unmixing [J]. Chemom Intell Lab Syst , 2026, 269: 105602.Spectrochim Acta A Mol Biomol Spectrosc , 2024, 323: 124868.et al . Research on predicting photosynthetic pigments in tomato seedling leaves based on near-infrared hyperspectral imaging and machine learning [J]. Microchem J , 2024, 204: 111076.et al . A two-step iterative algorithm for sparse hyperspectral unmixing via total variation [J]. Appl Math Comput , 2021, 401: 126059.
2026年第57卷第19期
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doi: 10.7501j.issn.0253-2670.2026.19.007
接收时间：2026-04-02
首发时间：2026-10-29
https://castjournals.cast.org.cn/joweb/zcy/CN/10.7501j.issn.0253-2670.2026.19.007
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2种不同金属材料的力学参数
科 属数 种数 占总种数比例 属 种数 占总种数比例 鹅膏菌科Amanitaceae 2 11 5.26 鹅膏菌属 Amanita 10 4.78 小菇科 Mycenaceae 2 12 5.74 丝盖伞属 Inocybe 5 2.39 多孔菌科 Polyporaceae 8 14 6.70 蜡蘑属 Laccaria 5 2.39 红菇科 Russulaceae 3 23 11.00 小皮伞属 Marasmius 6 2.87 小菇属 Mycena 11 5.26 光柄菇属 Pluteus 5 2.39 红菇属 Russula 17 8.13 栓菌属 Trametes 5 2.39
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