Application of near infrared spectroscopy combined with particle swarm optimization based least square support vactor machine to rapid quantitative analysis of Corni Fructus / 药学学报
Acta Pharmaceutica Sinica
;
(12): 1645-1651, 2015.
Artigo
em Chinês
| WPRIM
| ID: wpr-320029
ABSTRACT
A novel method was developed for the rapid determination of multi-indicators in corni fructus by means of near infrared (NIR) spectroscopy. Particle swarm optimization (PSO) based least squares support vector machine was investigated to increase the levels of quality control. The calibration models of moisture, extractum, morroniside and loganin were established using the PSO-LS-SVM algorithm. The performance of PSO-LS-SVM models was compared with partial least squares regression (PLSR) and back propagation artificial neural network (BP-ANN). The calibration and validation results of PSO-LS-SVM were superior to both PLS and BP-ANN. For PSO-LS-SVM models, the correlation coefficients (r) of calibrations were all above 0.942. The optimal prediction results were also achieved by PSO-LS-SVM models with the RMSEP (root mean square error of prediction) and RSEP (relative standard errors of prediction) less than 1.176 and 15.5% respectively. The results suggest that PSO-LS-SVM algorithm has a good model performance and high prediction accuracy. NIR has a potential value for rapid determination of multi-indicators in Corni Fructus.
Texto completo:
DisponíveL
Índice:
WPRIM (Pacífico Ocidental)
Assunto principal:
Controle de Qualidade
/
Algoritmos
/
Calibragem
/
Medicamentos de Ervas Chinesas
/
Análise dos Mínimos Quadrados
/
Química
/
Redes Neurais de Computação
/
Espectroscopia de Luz Próxima ao Infravermelho
/
Cornus
/
Máquina de Vetores de Suporte
Tipo de estudo:
Estudo prognóstico
Idioma:
Chinês
Revista:
Acta Pharmaceutica Sinica
Ano de publicação:
2015
Tipo de documento:
Artigo
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