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Spectral dimensionality reduction for quantitative analysis of cotton content of blend fabrics

Spectral dimensionality reduction for quantitative analysis of cotton content of blend fabrics PurposeThe purpose of this paper is to initiate investigations to develop near infrared (NIR) spectroscopy coupled with spectral dimensionality reduction and multivariate calibration methods to rapidly measure cotton content in blend fabrics.Design/methodology/approachIn total, 124 and 41 samples were used to calibrate models and assess the performance of the models, respectively. The raw spectra are transformed into wavelet coefficients. Multivariate calibration methods of partial least square (PLS), extreme learning machine (ELM) and least square support vector machine (LS-SVM) were employed to develop the models using 100 wavelet coefficients. Through comparing the performance of PLS, ELM and LS-SVM models with new samples, the optimal model of cotton content was obtained with the LS-SVM model.FindingsThe correlation coefficient of prediction (rp) and root mean square errors of prediction were 0.99 and 4.37 percent, respectively. The results suggest that NIR spectroscopy, combining with the LS-SVM method, has significant potential to quantitatively analyze cotton content in blend fabrics.Originality/valueIt may have commercial and regulatory potential to avoid time-consuming work, costly and laborious chemical analysis for cotton content in blend fabrics. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png International Journal of Clothing Science and Technology Emerald Publishing

Spectral dimensionality reduction for quantitative analysis of cotton content of blend fabrics

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References (26)

Publisher
Emerald Publishing
Copyright
Copyright © Emerald Group Publishing Limited
ISSN
0955-6222
DOI
10.1108/IJCST-07-2018-0091
Publisher site
See Article on Publisher Site

Abstract

PurposeThe purpose of this paper is to initiate investigations to develop near infrared (NIR) spectroscopy coupled with spectral dimensionality reduction and multivariate calibration methods to rapidly measure cotton content in blend fabrics.Design/methodology/approachIn total, 124 and 41 samples were used to calibrate models and assess the performance of the models, respectively. The raw spectra are transformed into wavelet coefficients. Multivariate calibration methods of partial least square (PLS), extreme learning machine (ELM) and least square support vector machine (LS-SVM) were employed to develop the models using 100 wavelet coefficients. Through comparing the performance of PLS, ELM and LS-SVM models with new samples, the optimal model of cotton content was obtained with the LS-SVM model.FindingsThe correlation coefficient of prediction (rp) and root mean square errors of prediction were 0.99 and 4.37 percent, respectively. The results suggest that NIR spectroscopy, combining with the LS-SVM method, has significant potential to quantitatively analyze cotton content in blend fabrics.Originality/valueIt may have commercial and regulatory potential to avoid time-consuming work, costly and laborious chemical analysis for cotton content in blend fabrics.

Journal

International Journal of Clothing Science and TechnologyEmerald Publishing

Published: Jun 3, 2019

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