Fast Nondestructive Identification of Endothelium Corneum Gigeriae Galli Using Visible/Near-Infrared Spectroscopy

Autor:	Xiao-Yan Zhang, Wen-Juan Ou, Yao-Yong Meng, Hao Zhang, Song-Hao Liu
Rok vydání:	2011
Předmět:	Mean squared error Artificial neural network business.industry Near-infrared spectroscopy General Physics and Astronomy Pattern recognition Cross-validation Identification (information) Principal component analysis Multiple correlation Artificial intelligence Spectroscopy business Mathematics
Zdroj:	Chinese Physics Letters. 28:120701
ISSN:	1741-3540 0256-307X
Popis:	Vis/NIR spectroscopy, in combination with partial least square (PLS) analysis and a back-propagation neural network, is investigated to identify endothelium corneum gigeriae galli (ECGG). The spectral features of ECGGs and their counterfeits are reasonably differentiated in vis/NIR region, which provides enough qualitative information to establish the relationship between the spectra and samples for identification. After pretreatment of the spectral data, cross validation is implemented for extracting the best number of principal components. Then the calibration and validation set are performed well. The PLS and back propagation neural network (BPNN) model gives the BPNN to be 0.9941 and the root mean square residual (RMSR) to be 0.0775 for the calibration set, and the multiple correlation coefficient (MCC) to 0.9874 and the RMSE to 0.1134 for the validation set. Thus the PLS and BPNN model is reliable and practicable. Through testing, a recognition accuracy of 100% is achieved. The present study could offer a new approach for fast and nondestructive discrimination of ECGG and its counterfeit.
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_________::d641651c56907ee116f49f0286a2101e https://doi.org/10.1088/0256-307x/28/12/120701 Zobrazit plný text záznamu