Cargando…

Support Vector Regression Approach to Predict the Design Space for the Extraction Process of Pueraria lobata

A support vector regression (SVR) method was introduced to improve the robustness and predictability of the design space in the implementation of quality by design (QbD), taking the extraction process of Pueraria lobata as a case study. In this paper, extraction time, number of extraction cycles, an...

Descripción completa

Detalles Bibliográficos
Autores principales: Wang, Yaqi, Yang, Yuanzhen, Jiao, Jiaojiao, Wu, Zhenfeng, Yang, Ming
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2018
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6222814/
https://www.ncbi.nlm.nih.gov/pubmed/30241281
http://dx.doi.org/10.3390/molecules23102405
_version_ 1783369294677016576
author Wang, Yaqi
Yang, Yuanzhen
Jiao, Jiaojiao
Wu, Zhenfeng
Yang, Ming
author_facet Wang, Yaqi
Yang, Yuanzhen
Jiao, Jiaojiao
Wu, Zhenfeng
Yang, Ming
author_sort Wang, Yaqi
collection PubMed
description A support vector regression (SVR) method was introduced to improve the robustness and predictability of the design space in the implementation of quality by design (QbD), taking the extraction process of Pueraria lobata as a case study. In this paper, extraction time, number of extraction cycles, and liquid–solid ratio were identified as critical process parameters (CPPs), and the yield of puerarin, total isoflavonoids, and extracta sicca were the critical quality attributes (CQAs). Models between CQAs and CPPs were constructed using both a conventional quadratic polynomial model (QPM) and the SVR algorithm. The results of the two models indicated that the SVR model had better performance, with a higher R(2) and lower root-mean-square error (RMSE) and mean absolute deviation (MAD) than those of the QPM. Furthermore, the design space was predicted using a grid search technique. The operational range was extraction time, 24–51 min; number of extraction cycles, 3; and liquid–solid ratio, 14–18 mL/g. This study is the first reported work optimizing the design space of the extraction process of P. lobata based on an SVR model. SVR modeling, with its better prediction accuracy and generalization ability, could be a reliable tool for predicting the design space and shows great potential for the quality control of QbD.
format Online
Article
Text
id pubmed-6222814
institution National Center for Biotechnology Information
language English
publishDate 2018
publisher MDPI
record_format MEDLINE/PubMed
spelling pubmed-62228142018-11-13 Support Vector Regression Approach to Predict the Design Space for the Extraction Process of Pueraria lobata Wang, Yaqi Yang, Yuanzhen Jiao, Jiaojiao Wu, Zhenfeng Yang, Ming Molecules Article A support vector regression (SVR) method was introduced to improve the robustness and predictability of the design space in the implementation of quality by design (QbD), taking the extraction process of Pueraria lobata as a case study. In this paper, extraction time, number of extraction cycles, and liquid–solid ratio were identified as critical process parameters (CPPs), and the yield of puerarin, total isoflavonoids, and extracta sicca were the critical quality attributes (CQAs). Models between CQAs and CPPs were constructed using both a conventional quadratic polynomial model (QPM) and the SVR algorithm. The results of the two models indicated that the SVR model had better performance, with a higher R(2) and lower root-mean-square error (RMSE) and mean absolute deviation (MAD) than those of the QPM. Furthermore, the design space was predicted using a grid search technique. The operational range was extraction time, 24–51 min; number of extraction cycles, 3; and liquid–solid ratio, 14–18 mL/g. This study is the first reported work optimizing the design space of the extraction process of P. lobata based on an SVR model. SVR modeling, with its better prediction accuracy and generalization ability, could be a reliable tool for predicting the design space and shows great potential for the quality control of QbD. MDPI 2018-09-20 /pmc/articles/PMC6222814/ /pubmed/30241281 http://dx.doi.org/10.3390/molecules23102405 Text en © 2018 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Wang, Yaqi
Yang, Yuanzhen
Jiao, Jiaojiao
Wu, Zhenfeng
Yang, Ming
Support Vector Regression Approach to Predict the Design Space for the Extraction Process of Pueraria lobata
title Support Vector Regression Approach to Predict the Design Space for the Extraction Process of Pueraria lobata
title_full Support Vector Regression Approach to Predict the Design Space for the Extraction Process of Pueraria lobata
title_fullStr Support Vector Regression Approach to Predict the Design Space for the Extraction Process of Pueraria lobata
title_full_unstemmed Support Vector Regression Approach to Predict the Design Space for the Extraction Process of Pueraria lobata
title_short Support Vector Regression Approach to Predict the Design Space for the Extraction Process of Pueraria lobata
title_sort support vector regression approach to predict the design space for the extraction process of pueraria lobata
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6222814/
https://www.ncbi.nlm.nih.gov/pubmed/30241281
http://dx.doi.org/10.3390/molecules23102405
work_keys_str_mv AT wangyaqi supportvectorregressionapproachtopredictthedesignspacefortheextractionprocessofpuerarialobata
AT yangyuanzhen supportvectorregressionapproachtopredictthedesignspacefortheextractionprocessofpuerarialobata
AT jiaojiaojiao supportvectorregressionapproachtopredictthedesignspacefortheextractionprocessofpuerarialobata
AT wuzhenfeng supportvectorregressionapproachtopredictthedesignspacefortheextractionprocessofpuerarialobata
AT yangming supportvectorregressionapproachtopredictthedesignspacefortheextractionprocessofpuerarialobata