In:
Journal of Near Infrared Spectroscopy, SAGE Publications, Vol. 15, No. 5 ( 2007-10), p. 291-297
Abstract:
Moving window partial least-squares (MWPLS) regression was coupled with near infrared (NIR) spectra as an interval selection method to improve the performance of partial least squares discriminant analysis (PLSDA) models. This method was applied to the identification of artificial bezoar, natural bezoar and artificial bezoar in natural bezoar and compared with some traditional pattern recognition methods, such as principal component analysis (PCA), linear discriminant analysis (LDA) and PLSDA. The introduction of MWPLS enhanced the performance of PLSDA model. The results obtained showed that moving window partial least-squares discriminant analysis (MWPLSDA) can extract wavelength intervals with useful information and build simple yet effective classification models that can significantly improve the classification accuracy. Then MWPLSDA was used to identify natural bezoar by geographical origin; a promising result was achieved. The work showed that MWPLSDA could be a promising method for quality analysis and discrimination of chinese medical herbs according to geographical origin.
Type of Medium:
Online Resource
ISSN:
0967-0335
,
1751-6552
Language:
English
Publisher:
SAGE Publications
Publication Date:
2007
detail.hit.zdb_id:
2021280-X
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