NDLI: Identification of varieties of cashmere by Vis/NIR spectroscopy technology based on PCA-SVM

Content Provider	IEEE Xplore Digital Library
Author	Guifang Wu Yong He
Copyright Year	2008
Description	Author affiliation: Coll. of Biosystems Eng. & Food Sci., Univ. of Zhejiang, Hangzhou (Guifang Wu; Yong He)
Abstract	One mixed algorithm was presented to discriminate cashmere varieties with principal component analysis (PCA) and support vector machine (SVM). Cashmere fiber has such characteristics as threadlike, softness, glossy and high tensile strength. The quality characters and economic value of each breed of cashmere are very different. In order to safeguard the consumerpsilas rights and guarantee the quality of cashmere product, quickly, efficiently and correctly identify cashmere has significant meaning to the production and transaction of cashmere material. This research adopts Vis/NIRS spectroscopy diffuse techniques to collect the spectral data of cashmere. The near infrared fingerprint of cashmere were acquired by principal component analysis (PCA), followed which support vector machine (SVM) methods were used to further identify the cashmere material. The result of principal component analysis (PCA) indicated that the score map made by the scores of PC1, PC2 and PC3. 10 principal components (PCs) were selected as the input of support vector machine (SVM) based on the reliabilities of PCs of 99.99%. 100 cashmere samples were used for calibration and the remaining 75 cashmere samples were used for validation. A one-against-all multi-class SVM model has been built, the capabilities of SVM with different kernel function were comparative analyzed, and the result showed that SVM possessing the Gaussian kernel function has the best identified capabilities with the accuracy of 100%. This research indicated that the data mining method of PCA-SVM has a good identification effect, and can work as a new method on rapid identification of cashmere material varieties.
Starting Page	1548
Ending Page	1552
File Size	133566
Page Count	5
File Format	PDF
ISBN	9781424421138
DOI	10.1109/WCICA.2008.4594459
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2008-06-25
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Support vector machines Spectroscopy Principal component analysis Reflectivity Kernel Materials Wool Support Vector Machine (SVM) Vis/NIR Spectroscopy Cashmere material Principal Component Analysis (PCA)
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