Discernment of textile fibers by polarization-sensitive Digital Holographic microscope and machine learning

Valentino, M.; Behal, J.; Tonetti, C.; Carletto, R.A.; Itri, S.; Memmolo, P.; Stella, E.; Miccio, L.; Bianco, V.; Ferraro, P.

doi:10.1016/j.optlaseng.2024.108395

Optics and Lasers in Engineering

2024

DOI: 10.1016/j.optlaseng.2024.108395

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Discernment of textile fibers by polarization-sensitive Digital Holographic microscope and machine learning

M. Valentino,

J. Behal,

C. Tonetti

et al.

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Attenuated Total Reflection Fourier Transform Infrared Spectroscopy and Chemometrics for the Discrimination of Animal Hair Fibers for the Textile Sector

Chrimatopoulos,

Tummino,

Iliadis

et al. 2024

Appl Spectrosc

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Analyzing the composition of animal hair fibers in textiles is crucial for ensuring the quality of yarns and fabrics made from animal hair. Among others, Fourier transform infrared (FT-IR) spectroscopy is a technique that identifies vibrations associated with chemical bonds, including those found in amino acid groups. Cashmere, mohair, yak, camel, alpaca, vicuña, llama, and sheep hair fibers were analyzed via attenuated total reflection FT-IR (ATR FT-IR) spectroscopy and scanning electron microscopy techniques aiming at the discrimination among them to identify possible commercial frauds. ATR FT-IR, being a novel approach, was coupled with chemometric tools (partial least squares discriminant analysis, PLS-DA), building classification/prediction models, which were cross-validated. PLS-DA models provided an excellent differentiation among animal hair of both camelids and eight animal species. In addition, the combination of ATR FT-IR and PLS-DA was used to discriminate the cashmere hair from different origins (Afghanistan, Australia, China, Iran, and Mongolia). The model showed very good discrimination ability (accuracy 87%), with variance expression of 94.88% and mean squared error of cross-validation of 0.1525.

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Attenuated Total Reflection Fourier Transform Infrared Spectroscopy and Chemometrics for the Discrimination of Animal Hair Fibers for the Textile Sector

Chrimatopoulos,

Tummino,

Iliadis

et al. 2024

Appl Spectrosc

View full text Add to dashboard Cite

show abstract

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Discernment of textile fibers by polarization-sensitive Digital Holographic microscope and machine learning

Cited by 1 publication

References 36 publications

Attenuated Total Reflection Fourier Transform Infrared Spectroscopy and Chemometrics for the Discrimination of Animal Hair Fibers for the Textile Sector

Attenuated Total Reflection Fourier Transform Infrared Spectroscopy and Chemometrics for the Discrimination of Animal Hair Fibers for the Textile Sector

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