Yuan Gao, Bin Qiao, Zarmina Gul, Mengfei Tian, Jiabo Cheng, Chunguo Xu, Chunjian Zhao, Chunying Li
{"title":"Python辅助优化指纹图谱结合单标记多组分定量分析评价地榆的质量","authors":"Yuan Gao, Bin Qiao, Zarmina Gul, Mengfei Tian, Jiabo Cheng, Chunguo Xu, Chunjian Zhao, Chunying Li","doi":"10.1007/s10337-023-04284-x","DOIUrl":null,"url":null,"abstract":"<div><p>A simple method was proposed to assess the quality of <i>Sanguisorbae Radix</i> based on Python-aided optimization fingerprint chromatography (FC) combined with quantitative analysis of multi-components by single marker (PA-FC/QAMS). The Python program quickly anticipated the mobile phase conditions for fingerprints, and the result yielded a chromatogram with good peak and resolution. The estimated chromatogram was similar to that obtained from the actual experiment. Furthermore, we used the mobile phase conditions predicted by Python to establish a fingerprint of 15 batches of <i>Sanguisorbae Radix</i>. and optimized it. Compared with the traditional trial-and-error method used to optimize the mobile phase conditions during the experiment, the efficient Python prediction method substantially reduced the number of experiments needed for optimum mobile phase conditions. In addition, exclusive mobile phase composition can be simulated according to different experimental instruments and chromatographic columns. Moreover, 15 batches of samples from different regions were classified by similarity analysis, cluster analysis, and factor analysis. This study shows that PA-FC/QAMS method can provide a simple and efficient approach for the quality evaluation of <i>Sanguisorbae Radix</i>.</p></div>","PeriodicalId":518,"journal":{"name":"Chromatographia","volume":"86 11-12","pages":"717 - 727"},"PeriodicalIF":1.2000,"publicationDate":"2023-10-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Quality Evaluation of Sanguisorbae Radix via Python Aided Optimization Fingerprint Chromatography Combined with Quantitative Analysis of Multi-components by Single Marker\",\"authors\":\"Yuan Gao, Bin Qiao, Zarmina Gul, Mengfei Tian, Jiabo Cheng, Chunguo Xu, Chunjian Zhao, Chunying Li\",\"doi\":\"10.1007/s10337-023-04284-x\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><p>A simple method was proposed to assess the quality of <i>Sanguisorbae Radix</i> based on Python-aided optimization fingerprint chromatography (FC) combined with quantitative analysis of multi-components by single marker (PA-FC/QAMS). The Python program quickly anticipated the mobile phase conditions for fingerprints, and the result yielded a chromatogram with good peak and resolution. The estimated chromatogram was similar to that obtained from the actual experiment. Furthermore, we used the mobile phase conditions predicted by Python to establish a fingerprint of 15 batches of <i>Sanguisorbae Radix</i>. and optimized it. Compared with the traditional trial-and-error method used to optimize the mobile phase conditions during the experiment, the efficient Python prediction method substantially reduced the number of experiments needed for optimum mobile phase conditions. In addition, exclusive mobile phase composition can be simulated according to different experimental instruments and chromatographic columns. Moreover, 15 batches of samples from different regions were classified by similarity analysis, cluster analysis, and factor analysis. This study shows that PA-FC/QAMS method can provide a simple and efficient approach for the quality evaluation of <i>Sanguisorbae Radix</i>.</p></div>\",\"PeriodicalId\":518,\"journal\":{\"name\":\"Chromatographia\",\"volume\":\"86 11-12\",\"pages\":\"717 - 727\"},\"PeriodicalIF\":1.2000,\"publicationDate\":\"2023-10-26\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Chromatographia\",\"FirstCategoryId\":\"92\",\"ListUrlMain\":\"https://link.springer.com/article/10.1007/s10337-023-04284-x\",\"RegionNum\":4,\"RegionCategory\":\"化学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q4\",\"JCRName\":\"BIOCHEMICAL RESEARCH METHODS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Chromatographia","FirstCategoryId":"92","ListUrlMain":"https://link.springer.com/article/10.1007/s10337-023-04284-x","RegionNum":4,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q4","JCRName":"BIOCHEMICAL RESEARCH METHODS","Score":null,"Total":0}
Quality Evaluation of Sanguisorbae Radix via Python Aided Optimization Fingerprint Chromatography Combined with Quantitative Analysis of Multi-components by Single Marker
A simple method was proposed to assess the quality of Sanguisorbae Radix based on Python-aided optimization fingerprint chromatography (FC) combined with quantitative analysis of multi-components by single marker (PA-FC/QAMS). The Python program quickly anticipated the mobile phase conditions for fingerprints, and the result yielded a chromatogram with good peak and resolution. The estimated chromatogram was similar to that obtained from the actual experiment. Furthermore, we used the mobile phase conditions predicted by Python to establish a fingerprint of 15 batches of Sanguisorbae Radix. and optimized it. Compared with the traditional trial-and-error method used to optimize the mobile phase conditions during the experiment, the efficient Python prediction method substantially reduced the number of experiments needed for optimum mobile phase conditions. In addition, exclusive mobile phase composition can be simulated according to different experimental instruments and chromatographic columns. Moreover, 15 batches of samples from different regions were classified by similarity analysis, cluster analysis, and factor analysis. This study shows that PA-FC/QAMS method can provide a simple and efficient approach for the quality evaluation of Sanguisorbae Radix.
期刊介绍：
Separation sciences, in all their various forms such as chromatography, field-flow fractionation, and electrophoresis, provide some of the most powerful techniques in analytical chemistry and are applied within a number of important application areas, including archaeology, biotechnology, clinical, environmental, food, medical, petroleum, pharmaceutical, polymer and biopolymer research. Beyond serving analytical purposes, separation techniques are also used for preparative and process-scale applications. The scope and power of separation sciences is significantly extended by combination with spectroscopic detection methods (e.g., laser-based approaches, nuclear-magnetic resonance, Raman, chemiluminescence) and particularly, mass spectrometry, to create hyphenated techniques. In addition to exciting new developments in chromatography, such as ultra high-pressure systems, multidimensional separations, and high-temperature approaches, there have also been great advances in hybrid methods combining chromatography and electro-based separations, especially on the micro- and nanoscale. Integrated biological procedures (e.g., enzymatic, immunological, receptor-based assays) can also be part of the overall analytical process.
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