Quantitative Determination of the Fiber Components in Textiles by Near-Infrared Spectroscopy and Extreme Learning Machine. (12th April 2020)

• Record Type: Journal Article
• Authors: Chen, Hui; Tan, Chao; Lin, Zan
• Is Part Of: Analytical letters. Volume 53:Number 6(2020)
• Page Start: 844
• Page End: 857
• Publication Date: 2020-04-12
• Subjects: Calibration -- extreme learning machine (ELM) -- near-infrared spectroscopy (NIRS) -- partial least squares (PLS) -- textile; Chemistry, Analytic -- Periodicals; Chemistry, Analytic -- Abstracts; 543
• DOI: 10.1080/00032719.2019.1683742
• Languages: English
• ISSNs: 0003-2719

Abstract

Abstract: The quantitative determination of the components of textiles takes an important position in quality control. A rapid and green method for simultaneously determining four components in blended fabrics was explored by near-infrared (NIR) spectroscopy combined with chemometrics. Two sample sets were designed and used as the training and test sets, respectively. The four components included wool, polyester, polyacrylonitrile, and nylon. A variance sorting-based algorithm was used for variable filtering. Both classic partial least squares (PLS) and extreme learning machine (ELM) were used for multivariate calibration and a systematic comparison was made. This results reveal that ELM is superior to the conventional PLS, especially when there are fewer variables, indicating that NIR spectroscopy combined with ELM and a pertinent variable selection is feasible for NIR-based textile analysis. The developed procedure may have commercial and regulatory potential to avoid laborious, time-consuming, and expensive wet chemical analysis.