NDLI: Application of Principal Component Analysis-Artificial Neural Network in Near Infrared Spectroscopy for Non-destructive Determination of Coriolus Versicolor

Content Provider	IEEE Xplore Digital Library
Author	Li-yan Wu Dong-sheng Yang Ming-zhi Zhao Fan-xin Meng
Copyright Year	2012
Abstract	We have applied principal component analysis-artificial neural network (PCA-ANN) in near infrared (NIR) spectroscopy to synchronous and rapid determining the contents of polysaccharide and protein in the Coriolus versicolor Powders. Back-Propagation (BP) Networks which adopt Levenberg-Marquardt training algorithm have been developed. Via analyzing the NIR spectra matrix by principal component analysis (PCA) method, we have obtained the principal components (PC) scores. The original NIR spectra and PC scores were respectively used as input data. These developed BP Networks have been optimized by selecting suitable topologic parameters and the best numbers of training. Compare with original NIR spectra, using the PC scores as input data, the capabilities of BP networks were much better. Using these optimized BP Networks for predicting the contents of polysaccharide and protein in prediction set, the root mean square error of prediction (RMSEP) are 0.0141 and 0.0138. These results are so satisfied and NIR spectroscopy technology is convenient, rapid, no pretreatment and no pollution that this method could be popularized in the in situ measurement and the on-line quality control for fermentation.
Starting Page	106
Ending Page	109
File Size	561099
Page Count	4
File Format	PDF
ISBN	9781467304702
DOI	10.1109/ICICTA.2012.33
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2012-01-12
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Proteins Training Spectroscopy Coriolus Versicolor Artificial Neural Network Artificial neural networks Predictive models Calibration Near Infrared Spectroscopy Principal Component Analysis Principal component analysis
Content Type	Text
Resource Type	Article

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