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.
Article
en Zh
| WPRIM
| ID: wpr-320029
Biblioteca responsable:
WPRO
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:
1
Índice:
WPRIM
Asunto principal:
Control de Calidad
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Algoritmos
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Calibración
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Medicamentos Herbarios Chinos
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Análisis de los Mínimos Cuadrados
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Química
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Redes Neurales de la Computación
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Espectroscopía Infrarroja Corta
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Cornus
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Máquina de Vectores de Soporte
Tipo de estudio:
Prognostic_studies
Idioma:
Zh
Revista:
Acta Pharmaceutica Sinica
Año:
2015
Tipo del documento:
Article