NDLI: Application of Wavelet Packet Transform-Radial Basis Function Neural Network in NIR Spectroscopy for Non-destructive Determination of Tricholoma Matsutake

Content Provider	IEEE Xplore Digital Library
Author	Jia-hui Lu Yi-bo Zhang Qing-fan Meng Qiu-hong Xie Li-rong Teng
Copyright Year	2009
Abstract	A novel calibration model has been proposed for synchronous, rapid and non-destructive determination the content of polysaccharide and protein in medical fungi Tricholoma matsutake by near infrared reflectance (NIR) spectroscopy. This model is a combination of wavelet packet transform (WPT) data disposal with multi-scale analysis and radial basis function neural network (WPT-RBFNN). Via using principal component analysis (PCA) method for analyzing these reconstructed spectra matrix, the anterior 15 PC scores of principal components (PC) were obtained, which were used as input data in RBFNN. The network parameters including number of input nodes, number of hidden layer neurons and spread constant (SC) were investigated. WPT-RBFNN model which reconstructed the spectra data, removed the noise and the interference of background, and reduced the randomness, the capabilities of prediction is well optimized. Both precision of prediction and calculation speed are well improved. The root mean square errors of prediction (RMSEP) for determination of polysaccharide and protein obtained from the optimum WPT-RBFNN model are 0.00956 and 0.00943, which are superior to those that obtained by the optimum RBFNN models with origin spectra. Regression coefficient (R) between NIR predicted values and actual values for polysaccharide and protein are 0.98638 and 0.98292. It is verified that WPT-RBFNN model with multi-scale analysis is a suitable approach to deal with NIR spectroscopy and model this complex non-linearity. The proposed method which is convenient, rapid, no pretreatment and non-destructive for more precise determination of Tricholoma matsutake could be popularized in the in situ measurement and the on-line quality control for medical fungi.
Starting Page	590
Ending Page	596
File Size	1067090
Page Count	7
File Format	PDF
ISBN	9780769537368
DOI	10.1109/ICNC.2009.151
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2009-08-14
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Spectroscopy principal component analysis (PCA) Predictive models Infrared spectra Calibration wavelet packet transform (WPT) radial basis function neural network (RBFNN) near infrared reflectance (NIR) spectroscopy Proteins Fungi Reflectivity Tricholoma matsutake Neural networks Wavelet packets Principal component analysis
Content Type	Text
Resource Type	Article

Sl.	Authority	Responsibilities	Communication Details
1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
5	Website/Portal (Helpdesk)	Queries regarding NDLI and its services	support@ndl.gov.in
6	Contents and Copyright Issues	Queries related to content curation and copyright issues	content@ndl.gov.in
7	National Digital Library of India Club (NDLI Club)	Queries related to NDLI Club formation, support, user awareness program, seminar/symposium, collaboration, social media, promotion, and outreach	clubsupport@ndl.gov.in
8	Digital Preservation Centre (DPC)	Assistance with digitizing and archiving copyright-free printed books	dpc@ndl.gov.in
9	IDR Setup or Support	Queries related to establishment and support of Institutional Digital Repository (IDR) and IDR workshops	idr@ndl.gov.in

Application of Wavelet Packet Transform-Radial Basis Function Neural Network in NIR Spectroscopy for Non-destructive Determination of Coriolus Versicolor

Application of Wavelet Packet Transform-Radial Basis Function Neural Network in NIR Spectroscopy for Non-destructive Determination of Cordyceps Gunnii Mycelia Powder

Soft Sensor Based on Kernel Principal Component Analysis and Radial Basis Function Neural Network for Microbiological Fermentation

Application of principal component-radial basis function neural networks (PC-RBFNN) for the detection of water-adulterated bayberry juice by near-infrared spectroscopy.

Application of principal component-radial basis function neural networks (PC-RBFNN) for the detection of water-adulterated bayberry juice by near-infrared spectroscopy

Application of a Wavelet Packet Transform Based Radial Basis Function Neural Network to Analyze Overlapping Spectra

Nondestructive Discrimination of Peach Varieties Using Near Infrared Spectroscopy

Face Recognition Using Combination of Wavelet Packets, PCA and LDA

Resolve of Overlapped Voltammetric Peaks in Using a Wavelet Packet Transform Based Radial Basis Function Network

Application of Wavelet Packet Transform-Radial Basis Function Neural Network in NIR Spectroscopy for Non-destructive Determination of Tricholoma Matsutake

Similar Documents

Application of Wavelet Packet Transform-Radial Basis Function Neural Network in NIR Spectroscopy for Non-destructive Determination of Coriolus Versicolor

Application of Wavelet Packet Transform-Radial Basis Function Neural Network in NIR Spectroscopy for Non-destructive Determination of Cordyceps Gunnii Mycelia Powder

Soft Sensor Based on Kernel Principal Component Analysis and Radial Basis Function Neural Network for Microbiological Fermentation

Application of principal component-radial basis function neural networks (PC-RBFNN) for the detection of water-adulterated bayberry juice by near-infrared spectroscopy.

Application of principal component-radial basis function neural networks (PC-RBFNN) for the detection of water-adulterated bayberry juice by near-infrared spectroscopy

Application of a Wavelet Packet Transform Based Radial Basis Function Neural Network to Analyze Overlapping Spectra

Nondestructive Discrimination of Peach Varieties Using Near Infrared Spectroscopy

Face Recognition Using Combination of Wavelet Packets, PCA and LDA

Resolve of Overlapped Voltammetric Peaks in Using a Wavelet Packet Transform Based Radial Basis Function Network

Application of Wavelet Packet Transform-Radial Basis Function Neural Network in NIR Spectroscopy for Non-destructive Determination of Tricholoma Matsutake