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System optimisation quantitative model of on‐line NIR: a case of Glycyrrhiza uralensis Fisch extraction process
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| Zeitschriftentitel: | Phytochemical Analysis |
|---|---|
| Personen und Körperschaften: | , , , , , , , , , , , , |
| In: | Phytochemical Analysis, 32, 2021, 2, S. 165-171 |
| Format: | E-Article |
| Sprache: | Englisch |
| veröffentlicht: |
Wiley
|
| Schlagwörter: |
| author_facet |
Zeng, Jingqi Zhou, Zheng Liao, Yuan Ma, Lijuan Huang, Xingguo Zhang, Jing Lin, Ling Zhu, Jinyuan Lei, Leting Cao, Junjie Shen, Haoran Zheng, Yanfei Wu, Zhisheng Zeng, Jingqi Zhou, Zheng Liao, Yuan Ma, Lijuan Huang, Xingguo Zhang, Jing Lin, Ling Zhu, Jinyuan Lei, Leting Cao, Junjie Shen, Haoran Zheng, Yanfei Wu, Zhisheng |
|---|---|
| author |
Zeng, Jingqi Zhou, Zheng Liao, Yuan Ma, Lijuan Huang, Xingguo Zhang, Jing Lin, Ling Zhu, Jinyuan Lei, Leting Cao, Junjie Shen, Haoran Zheng, Yanfei Wu, Zhisheng |
| spellingShingle |
Zeng, Jingqi Zhou, Zheng Liao, Yuan Ma, Lijuan Huang, Xingguo Zhang, Jing Lin, Ling Zhu, Jinyuan Lei, Leting Cao, Junjie Shen, Haoran Zheng, Yanfei Wu, Zhisheng Phytochemical Analysis System optimisation quantitative model of on‐line NIR: a case of Glycyrrhiza uralensis Fisch extraction process Complementary and alternative medicine Drug Discovery Plant Science Molecular Medicine General Medicine Biochemistry Food Science Analytical Chemistry |
| author_sort |
zeng, jingqi |
| spelling |
Zeng, Jingqi Zhou, Zheng Liao, Yuan Ma, Lijuan Huang, Xingguo Zhang, Jing Lin, Ling Zhu, Jinyuan Lei, Leting Cao, Junjie Shen, Haoran Zheng, Yanfei Wu, Zhisheng 0958-0344 1099-1565 Wiley Complementary and alternative medicine Drug Discovery Plant Science Molecular Medicine General Medicine Biochemistry Food Science Analytical Chemistry http://dx.doi.org/10.1002/pca.2919 <jats:title>Abstract</jats:title><jats:sec><jats:title>Introduction</jats:title><jats:p>The on‐line analysis of active pharmaceutical ingredients (APIs) during the extraction process in herbal medicine is a challenge. Establishing a reliable and robust model is a critical procedure for the industrial application of on‐line near‐infrared (NIR) technology.</jats:p></jats:sec><jats:sec><jats:title>Objective</jats:title><jats:p>To evaluate the advantages of on‐line NIR model development using system optimisation strategy, <jats:italic>Glycyrrhiza uralensis</jats:italic> Fisch was used as a case. The content of liquiritin and glycyrrhizic acid was monitored during pilot scale extraction process of <jats:italic>Glycyrrhiza uralensis</jats:italic> Fisch in three batches.</jats:p></jats:sec><jats:sec><jats:title>Methods</jats:title><jats:p>High‐performance liquid chromatography (HPLC) was used as reference method for content determination of liquiritin and glycyrrhizic acid. The quantitative models of on‐line NIR were developed by system optimisation of processing trajectory. For comparison, the models were simultaneously developed by stepwise optimisation. Moreover, the modelling parameters obtained through system optimisation and stepwise optimisation were reused in three batches. Root mean square error of prediction (RMSEP) and residual predictive deviation (RPD) were used to assess the model quality.</jats:p></jats:sec><jats:sec><jats:title>Results</jats:title><jats:p>The average values of RMSEP and RPD of systematic model for liquiritin in three batches were 0.0361, 4.1525 (first batch), 0.0348, 4.7286 (second batch) and 0.0311, 4.9686 (third batch), respectively. In addition, the modelling parameters of systematic model for glycyrrhizic acid in three batches were same, and the average values of RMSEP and RPD were 0.0665 and 5.2751, respectively. The predictive performance and robustness of systematic models for the three batches were better than the comparison models.</jats:p></jats:sec><jats:sec><jats:title>Conclusion</jats:title><jats:p>The work demonstrated that system optimisation quantitative model of on‐line NIR could be used to determine the contents of liquiritin and glycyrrhizic acid during <jats:italic>Glycyrrhiza uralensis</jats:italic> Fisch extraction process.</jats:p></jats:sec> System optimisation quantitative model of on‐line NIR: a case of <i>Glycyrrhiza uralensis</i> Fisch extraction process Phytochemical Analysis |
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10.1002/pca.2919 |
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Online |
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Medizin Chemie und Pharmazie Biologie Land- und Forstwirtschaft, Gartenbau, Fischereiwirtschaft, Hauswirtschaft |
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Wiley, 2021 |
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Wiley, 2021 |
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0958-0344 1099-1565 |
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Phytochemical Analysis |
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| title |
System optimisation quantitative model of on‐line NIR: a case of Glycyrrhiza uralensis Fisch extraction process |
| title_unstemmed |
System optimisation quantitative model of on‐line NIR: a case of Glycyrrhiza uralensis Fisch extraction process |
| title_full |
System optimisation quantitative model of on‐line NIR: a case of Glycyrrhiza uralensis Fisch extraction process |
| title_fullStr |
System optimisation quantitative model of on‐line NIR: a case of Glycyrrhiza uralensis Fisch extraction process |
| title_full_unstemmed |
System optimisation quantitative model of on‐line NIR: a case of Glycyrrhiza uralensis Fisch extraction process |
| title_short |
System optimisation quantitative model of on‐line NIR: a case of Glycyrrhiza uralensis Fisch extraction process |
| title_sort |
system optimisation quantitative model of on‐line nir: a case of <i>glycyrrhiza uralensis</i> fisch extraction process |
| topic |
Complementary and alternative medicine Drug Discovery Plant Science Molecular Medicine General Medicine Biochemistry Food Science Analytical Chemistry |
| url |
http://dx.doi.org/10.1002/pca.2919 |
| publishDate |
2021 |
| physical |
165-171 |
| description |
<jats:title>Abstract</jats:title><jats:sec><jats:title>Introduction</jats:title><jats:p>The on‐line analysis of active pharmaceutical ingredients (APIs) during the extraction process in herbal medicine is a challenge. Establishing a reliable and robust model is a critical procedure for the industrial application of on‐line near‐infrared (NIR) technology.</jats:p></jats:sec><jats:sec><jats:title>Objective</jats:title><jats:p>To evaluate the advantages of on‐line NIR model development using system optimisation strategy, <jats:italic>Glycyrrhiza uralensis</jats:italic> Fisch was used as a case. The content of liquiritin and glycyrrhizic acid was monitored during pilot scale extraction process of <jats:italic>Glycyrrhiza uralensis</jats:italic> Fisch in three batches.</jats:p></jats:sec><jats:sec><jats:title>Methods</jats:title><jats:p>High‐performance liquid chromatography (HPLC) was used as reference method for content determination of liquiritin and glycyrrhizic acid. The quantitative models of on‐line NIR were developed by system optimisation of processing trajectory. For comparison, the models were simultaneously developed by stepwise optimisation. Moreover, the modelling parameters obtained through system optimisation and stepwise optimisation were reused in three batches. Root mean square error of prediction (RMSEP) and residual predictive deviation (RPD) were used to assess the model quality.</jats:p></jats:sec><jats:sec><jats:title>Results</jats:title><jats:p>The average values of RMSEP and RPD of systematic model for liquiritin in three batches were 0.0361, 4.1525 (first batch), 0.0348, 4.7286 (second batch) and 0.0311, 4.9686 (third batch), respectively. In addition, the modelling parameters of systematic model for glycyrrhizic acid in three batches were same, and the average values of RMSEP and RPD were 0.0665 and 5.2751, respectively. The predictive performance and robustness of systematic models for the three batches were better than the comparison models.</jats:p></jats:sec><jats:sec><jats:title>Conclusion</jats:title><jats:p>The work demonstrated that system optimisation quantitative model of on‐line NIR could be used to determine the contents of liquiritin and glycyrrhizic acid during <jats:italic>Glycyrrhiza uralensis</jats:italic> Fisch extraction process.</jats:p></jats:sec> |
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| author | Zeng, Jingqi, Zhou, Zheng, Liao, Yuan, Ma, Lijuan, Huang, Xingguo, Zhang, Jing, Lin, Ling, Zhu, Jinyuan, Lei, Leting, Cao, Junjie, Shen, Haoran, Zheng, Yanfei, Wu, Zhisheng |
| author_facet | Zeng, Jingqi, Zhou, Zheng, Liao, Yuan, Ma, Lijuan, Huang, Xingguo, Zhang, Jing, Lin, Ling, Zhu, Jinyuan, Lei, Leting, Cao, Junjie, Shen, Haoran, Zheng, Yanfei, Wu, Zhisheng, Zeng, Jingqi, Zhou, Zheng, Liao, Yuan, Ma, Lijuan, Huang, Xingguo, Zhang, Jing, Lin, Ling, Zhu, Jinyuan, Lei, Leting, Cao, Junjie, Shen, Haoran, Zheng, Yanfei, Wu, Zhisheng |
| author_sort | zeng, jingqi |
| container_issue | 2 |
| container_start_page | 165 |
| container_title | Phytochemical Analysis |
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| description | <jats:title>Abstract</jats:title><jats:sec><jats:title>Introduction</jats:title><jats:p>The on‐line analysis of active pharmaceutical ingredients (APIs) during the extraction process in herbal medicine is a challenge. Establishing a reliable and robust model is a critical procedure for the industrial application of on‐line near‐infrared (NIR) technology.</jats:p></jats:sec><jats:sec><jats:title>Objective</jats:title><jats:p>To evaluate the advantages of on‐line NIR model development using system optimisation strategy, <jats:italic>Glycyrrhiza uralensis</jats:italic> Fisch was used as a case. The content of liquiritin and glycyrrhizic acid was monitored during pilot scale extraction process of <jats:italic>Glycyrrhiza uralensis</jats:italic> Fisch in three batches.</jats:p></jats:sec><jats:sec><jats:title>Methods</jats:title><jats:p>High‐performance liquid chromatography (HPLC) was used as reference method for content determination of liquiritin and glycyrrhizic acid. The quantitative models of on‐line NIR were developed by system optimisation of processing trajectory. For comparison, the models were simultaneously developed by stepwise optimisation. Moreover, the modelling parameters obtained through system optimisation and stepwise optimisation were reused in three batches. Root mean square error of prediction (RMSEP) and residual predictive deviation (RPD) were used to assess the model quality.</jats:p></jats:sec><jats:sec><jats:title>Results</jats:title><jats:p>The average values of RMSEP and RPD of systematic model for liquiritin in three batches were 0.0361, 4.1525 (first batch), 0.0348, 4.7286 (second batch) and 0.0311, 4.9686 (third batch), respectively. In addition, the modelling parameters of systematic model for glycyrrhizic acid in three batches were same, and the average values of RMSEP and RPD were 0.0665 and 5.2751, respectively. The predictive performance and robustness of systematic models for the three batches were better than the comparison models.</jats:p></jats:sec><jats:sec><jats:title>Conclusion</jats:title><jats:p>The work demonstrated that system optimisation quantitative model of on‐line NIR could be used to determine the contents of liquiritin and glycyrrhizic acid during <jats:italic>Glycyrrhiza uralensis</jats:italic> Fisch extraction process.</jats:p></jats:sec> |
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| finc_class_facet | Medizin, Chemie und Pharmazie, Biologie, Land- und Forstwirtschaft, Gartenbau, Fischereiwirtschaft, Hauswirtschaft |
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| imprint | Wiley, 2021 |
| imprint_str_mv | Wiley, 2021 |
| institution | DE-Gla1, DE-Zi4, DE-15, DE-Pl11, DE-Rs1, DE-105, DE-14, DE-Ch1, DE-L229, DE-D275, DE-Bn3, DE-Brt1, DE-D161 |
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| physical | 165-171 |
| publishDate | 2021 |
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| series | Phytochemical Analysis |
| source_id | 49 |
| spelling | Zeng, Jingqi Zhou, Zheng Liao, Yuan Ma, Lijuan Huang, Xingguo Zhang, Jing Lin, Ling Zhu, Jinyuan Lei, Leting Cao, Junjie Shen, Haoran Zheng, Yanfei Wu, Zhisheng 0958-0344 1099-1565 Wiley Complementary and alternative medicine Drug Discovery Plant Science Molecular Medicine General Medicine Biochemistry Food Science Analytical Chemistry http://dx.doi.org/10.1002/pca.2919 <jats:title>Abstract</jats:title><jats:sec><jats:title>Introduction</jats:title><jats:p>The on‐line analysis of active pharmaceutical ingredients (APIs) during the extraction process in herbal medicine is a challenge. Establishing a reliable and robust model is a critical procedure for the industrial application of on‐line near‐infrared (NIR) technology.</jats:p></jats:sec><jats:sec><jats:title>Objective</jats:title><jats:p>To evaluate the advantages of on‐line NIR model development using system optimisation strategy, <jats:italic>Glycyrrhiza uralensis</jats:italic> Fisch was used as a case. The content of liquiritin and glycyrrhizic acid was monitored during pilot scale extraction process of <jats:italic>Glycyrrhiza uralensis</jats:italic> Fisch in three batches.</jats:p></jats:sec><jats:sec><jats:title>Methods</jats:title><jats:p>High‐performance liquid chromatography (HPLC) was used as reference method for content determination of liquiritin and glycyrrhizic acid. The quantitative models of on‐line NIR were developed by system optimisation of processing trajectory. For comparison, the models were simultaneously developed by stepwise optimisation. Moreover, the modelling parameters obtained through system optimisation and stepwise optimisation were reused in three batches. Root mean square error of prediction (RMSEP) and residual predictive deviation (RPD) were used to assess the model quality.</jats:p></jats:sec><jats:sec><jats:title>Results</jats:title><jats:p>The average values of RMSEP and RPD of systematic model for liquiritin in three batches were 0.0361, 4.1525 (first batch), 0.0348, 4.7286 (second batch) and 0.0311, 4.9686 (third batch), respectively. In addition, the modelling parameters of systematic model for glycyrrhizic acid in three batches were same, and the average values of RMSEP and RPD were 0.0665 and 5.2751, respectively. The predictive performance and robustness of systematic models for the three batches were better than the comparison models.</jats:p></jats:sec><jats:sec><jats:title>Conclusion</jats:title><jats:p>The work demonstrated that system optimisation quantitative model of on‐line NIR could be used to determine the contents of liquiritin and glycyrrhizic acid during <jats:italic>Glycyrrhiza uralensis</jats:italic> Fisch extraction process.</jats:p></jats:sec> System optimisation quantitative model of on‐line NIR: a case of <i>Glycyrrhiza uralensis</i> Fisch extraction process Phytochemical Analysis |
| spellingShingle | Zeng, Jingqi, Zhou, Zheng, Liao, Yuan, Ma, Lijuan, Huang, Xingguo, Zhang, Jing, Lin, Ling, Zhu, Jinyuan, Lei, Leting, Cao, Junjie, Shen, Haoran, Zheng, Yanfei, Wu, Zhisheng, Phytochemical Analysis, System optimisation quantitative model of on‐line NIR: a case of Glycyrrhiza uralensis Fisch extraction process, Complementary and alternative medicine, Drug Discovery, Plant Science, Molecular Medicine, General Medicine, Biochemistry, Food Science, Analytical Chemistry |
| title | System optimisation quantitative model of on‐line NIR: a case of Glycyrrhiza uralensis Fisch extraction process |
| title_full | System optimisation quantitative model of on‐line NIR: a case of Glycyrrhiza uralensis Fisch extraction process |
| title_fullStr | System optimisation quantitative model of on‐line NIR: a case of Glycyrrhiza uralensis Fisch extraction process |
| title_full_unstemmed | System optimisation quantitative model of on‐line NIR: a case of Glycyrrhiza uralensis Fisch extraction process |
| title_short | System optimisation quantitative model of on‐line NIR: a case of Glycyrrhiza uralensis Fisch extraction process |
| title_sort | system optimisation quantitative model of on‐line nir: a case of <i>glycyrrhiza uralensis</i> fisch extraction process |
| title_unstemmed | System optimisation quantitative model of on‐line NIR: a case of Glycyrrhiza uralensis Fisch extraction process |
| topic | Complementary and alternative medicine, Drug Discovery, Plant Science, Molecular Medicine, General Medicine, Biochemistry, Food Science, Analytical Chemistry |
| url | http://dx.doi.org/10.1002/pca.2919 |