TITLE:
Rapid, Non-Destructive, Textile Classification Using SIMCA on Diffuse Near-Infrared Reflectance Spectra
AUTHORS:
Christopher B. Davis, Kenneth W. Busch, Dennis H. Rabbe, Marianna A. Busch, Judith R. Lusk
KEYWORDS:
Diffuse Near-Infrared (NIR) Reflectance Spectroscopy, Chemometrics, Soft Independent Modeling of Class Analogy (SIMCA), Pattern Recognition, Textile Identification, Multivariate Analysis
JOURNAL NAME:
Journal of Modern Physics,
Vol.6 No.6,
May
6,
2015
ABSTRACT: Soft independent modeling of class analogy
(SIMCA) was successful in classifying a large library of 758 commercially
available, non-blended samples of acetate, cotton, polyester, rayon, silk and
wool 89% - 98% of the time at the 95% confidence level (p = 0.05 significance
level). In the present study, cotton and silk had a 62% and 24% chance,
respectively, of being classified with their own group and also with rayon.
SIMCA correctly identified a counterfeit “silk” sample as polyester. When
coupled with diffuse NIR reflectance spectroscopy and a large sample library,
SIMCA shows considerable promise as a quick, non-destructive, multivariate
method for fiber identification. A major advantage is simplicity. No sample
pretreatment of any kind was required, and no adjust-ments were made for fiber
origin, manufacturing process residues, topical finishes, weave pattern, or dye
content. Increasing the sample library should make the models more robust and
improve identification rates over those reported in this paper.