Expand this Topic clickable element to expand a topic
Skip to content
Optica Publishing Group
  • Applied Spectroscopy
  • Vol. 52,
  • Issue 8,
  • pp. 1039-1046
  • (1998)

Detection of Fibrilated Polymeric Contaminants in Wool and Cotton Yarns

Not Accessible

Your library or personal account may give you access

Abstract

The contamination of natural fiber yarns with polymeric fibrils is a common problem in the textile industry. This work demonstrates that near-infrared spectroscopy in the 2250 to 2400 nm region is a viable technique for the detection of hydrocarbon-based polymeric contamination in both wool and cotton yarn samples. Both highdensity polyethylene and polypropylene fibrils typical in size to those found in industry could be detected irrespective of polymer and yarn color. A principal component analysis model based on first-order derivative spectra was developed in which yarns contaminated with polymeric fibrils could easily be separated from pure wool yarns. The spectra obtained from the contaminated regions of the yarns were found to cluster well with spectra obtained from the pure polymeric materials. With the use of soft independent modeling for class analogy (SIMCA), a single class model for wool was developed on the basis of the raw data. This model easily discriminated between pure wool and polymer-containing samples, but the distinction between the different polymer types themselves was poor. This separation was enhanced, however, when the model was based on first-derivative spectra.

PDF Article
More Like This
Empirical model for matching spectrophotometric reflectance of yarn windings and multispectral imaging reflectance of single strands of yarns

Lin Luo, Hui-Liang Shen, Si-Jie Shao, and John Xin
J. Opt. Soc. Am. A 32(8) 1459-1467 (2015)

Computer vision for yarn microtension measurement

Qing Wang, Changhou Lu, Ran Huang, Wei Pan, and Xueyong Li
Appl. Opt. 55(9) 2393-2398 (2016)

Construction of an integrated multi-layer textile for solar-driven steam generation

Mengyu Xie, Jiaqi Qian, Yao Li, Hanmei Yang, Jiangang Qu, Xiaolin Hu, and Qinghui Mao
Appl. Opt. 60(16) 4930-4937 (2021)

Cited By

You do not have subscription access to this journal. Cited by links are available to subscribers only. You may subscribe either as an Optica member, or as an authorized user of your institution.

Contact your librarian or system administrator
or
Login to access Optica Member Subscription

Select as filters


Select Topics Cancel
© Copyright 2024 | Optica Publishing Group. All rights reserved, including rights for text and data mining and training of artificial technologies or similar technologies.