Optical coherence tomography imaging can identify Merino lambs' wool using automatic machine learning vision

Sabuncu M. H., Özdemir H.

TEXTILE RESEARCH JOURNAL, vol.93, no.19-20, pp.4611-4622, 2023 (SCI-Expanded, Scopus) identifier identifier

  • Publication Type: Article / Article
  • Volume: 93 Issue: 19-20
  • Publication Date: 2023
  • Doi Number: 10.1177/00405175231176500
  • Journal Name: TEXTILE RESEARCH JOURNAL
  • Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Agricultural & Environmental Science Database, Applied Science & Technology Source, Chemical Abstracts Core, Compendex, INSPEC
  • Page Numbers: pp.4611-4622
  • Keywords: Optical coherence tomography, Merino wool, coarse wool, fiber analysis, distinguishing, automatic machine learning
  • Dokuz Eylül University Affiliated: Yes

Abstract

Merino lambs' wool fiber has a unique chemical structure that gives the wool many unique properties and technical benefits. For example, the small fiber diameters mean that Merino wool is soft, countering the scratchiness commonly associated with wool. It is also biodegradable, organic, and environmentally friendly, making it a popular choice of sustainable fabric material. Unfortunately, some fiber sellers unduly sell coarse wool tops to wool yarn factories as Merino wool tops. It is, therefore, an important task to identify the actual wool type for quality assurance in the textile manufacturing process. This paper describes applying the spectral-domain optical coherence tomography (OCT) imaging and automatic machine learning (AutoML) techniques for distinguishing Merino wool from coarse wool. We present the results of wool measurements that were performed by the OCT scans and AutoML algorithms. We conclude that OCT imaging and AutoML algorithms can be applied to distinguish Merino wool from coarse wool in a simple, non-destructive, and contactless manner.