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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.
摘要：
在纺织业,棉和聚酯（PES）是生产衣服最常用的纤维之一。纤维的正确识别和准确的成分估计是强制性的，环保和精确的技术是受欢迎的。在这种情况下，使用近红外（NIR）和中红外（MIR）光谱来区分棉花和PES样品，并进一步估计混合样品的棉花含量进行了评估。通过各种化学计量模型获取和建模红外光谱：主成分分析；偏最小二乘判别分析；和偏最小二乘（PLS）回归。这两种技术(NIR和MIR)都为棉花和PES样品的鉴别提供了良好的潜力，尽管用近红外光谱获得的结果略好。关于棉花含量估计，对于NIR和MIR光谱，PLS模型的校准误差分别为3.3%和6.5%，分别。PLS模型用两组不同的样本进行了验证：预测集1，包含混纺棉+PES样本（如校准步骤中使用的样本），和预测集2，包含棉花+PES+不同的纤维样品。包括预测集2是为了解决这种化学计量学模型的最大已知缺点之一，这是对校准中未使用的样品类型的预测。尽管预测集2获得的结果较差，但所有误差均低于8%，证明了棉花含量估算技术的适用性。应该强调的是，这项工作中使用的纺织品样品来自不同的地理来源(棉花)，并且具有不同的表现(生，纱,针织/机织织物)，这加强了我们的发现。
