Rapid, Non-Destructive, Textile Classification Using SIMCA on Diffuse Near-Infrared Reflectance Spectra

Christopher B. Davis¹, Kenneth W. Busch^1*, Dennis H. Rabbe¹, Marianna A. Busch¹, Judith R. Lusk²

¹Department of Chemistry and Biochemistry, Baylor University, Waco, USA.
²Department of Family and Consumer Sciences, Baylor University, Waco, USA.

DOI: 10.4236/jmp.2015.66076                

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.

Keywords

Diffuse Near-Infrared (NIR) Reflectance Spectroscopy, Chemometrics, Soft Independent Modeling of Class Analogy (SIMCA), Pattern Recognition, Textile Identification, Multivariate Analysis

Conflicts of Interest

The authors declare no conflicts of interest.

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