Discrimination and Quantification of Cotton and Polyester Textile Samples Using Near-Infrared and Mid-Infrared Spectroscopies

Abstract

In the textile industry, cotton and polyester (PES) are among the most used fibres to produce clothes. The correct identification and accurate composition estimate of fibres are mandatory, and environmentally friendly and precise techniques are welcome. In this context, the use of near-infrared (NIR) and mid-infrared (MIR) spectroscopies to distinguish between cotton and PES samples and further estimate the cotton content of blended samples were evaluated. Infrared spectra were acquired and modelled through diverse chemometric models: principal component analysis; partial least squares discriminant analysis; and partial least squares (PLS) regression. Both techniques (NIR and MIR) presented good potential for cotton and PES sample discrimination, although the results obtained with NIR spectroscopy were slightly better. Regarding cotton content estimates, the calibration errors of the PLS models were 3.3% and 6.5% for NIR and MIR spectroscopy, respectively. The PLS models were validated with two different sets of samples: prediction set 1, containing blended cotton + PES samples (like those used in the calibration step), and prediction set 2, containing cotton + PES + distinct fibre samples. Prediction set 2 was included to address one of the biggest known drawbacks of such chemometric models, which is the prediction of sample types that are not used in the calibration. Despite the poorer results obtained for prediction set 2, all the errors were lower than 8%, proving the suitability of the techniques for cotton content estimation. It should be stressed that the textile samples used in this work came from different geographic origins (cotton) and were of distinct presentations (raw, yarn, knitted/woven fabric), which strengthens our findings.

Keywords: PLS models; chemometrics; cotton; model validation; polyester.

Figure 1

(A) Near-infrared and (B) mid-infrared spectra (mean spectra of all the samples) of 100% cotton and 100% polyester samples. Legend: red lines—mean spectrum of cotton samples; green lines—mean spectrum of PES samples.

Figure 2

Score plots of the PLSDA regression models obtained for 100% cotton and 100% PES samples. (A1) Near-infrared (spectral region: 9000–4000 cm⁻¹); (B1) mid-infrared (spectral region: 1800–700 cm⁻¹) spectra and corresponding model loadings (A2,B2). Legend: red circles—cotton samples; green squares—PES samples.

Figure 3

Score plots of the PCA models developed with 100% cotton, 100% PES, and blended samples of cotton and polyester: (A) near-infrared (spectral region: 9000–4000 cm⁻¹) and (B) mid-infrared (spectral region: 1800–700 cm⁻¹). Legend: red circles—cotton samples; green squares—PES samples; blue triangles—blended samples.

Figure 4

PLS regression results obtained with NIR (A1—prediction set 1 and A2—prediction set 2) and FTIR spectra (B1—prediction set 1 and B2—prediction set 2). Legend: grey circles—calibration spectra; red rhombi—prediction spectra.

Figure 5

Pictures of 100% cotton (A1—raw; A2—yarn; A3—fabric) and 100% polyester (B1—raw; B2—yarn; B3—fabric) samples.