Ruckebusch C, Orhan F, Durand A, Boubellouta T, Huvenne J P
Laboratoire de Spectrochimie Infrarouge et Raman (LASIR) UMR CNRS 8516, Bât.C5, Université des Sciences et Technologies de Lille (USTL) 59655 Villeneuve d'Ascq, France.
Appl Spectrosc. 2006 May;60(5):539-44. doi: 10.1366/000370206777412194.
Quantitative analysis of textile blends and textile fabrics is currently of particular interest in the industrial context. In this frame, this work investigates whether the use of Fourier transform (FT) near-infrared (NIR) spectroscopy and chemometrics is powerful for rapid and accurate quantitative analysis of cotton-polyester content in blend products. As samples of the same composition have many sources of variability that affect NIR spectra, indirect prediction is particularly challenging and a large sample population is required to design robust calibration models. Thus, a total of more than three-hundred cotton-polyester samples were selected covering the range from the 0% to 100% cotton and the corresponding NIR reflectance spectra were measured on raw fabrics. The data set obtained was used to develop multivariate models for quantitative prediction from reference measurements. A successful approach was found to rely on partial least squares (PLS) regression combined with genetic algorithms (GAs) for wavelength selection. It involved evaluating a set of calibration models considering different spectral regions. The results obtained considering 27.5% of the original variables yielded a prediction error (RMSEP) of 2.3 in percent cotton content. It demonstrates that FT-NIR spectroscopy has the potential to be used in the textile industry for the prediction of the composition of cotton-polyester blends. As a further consequence, it was observed that the spectral preprocessing and the complexity of the model are simplified compared to the full-spectrum approach. Also, the relevancy of the spectral intervals retained after variable selection can be discussed.
在工业背景下，对纺织混纺物和纺织面料进行定量分析目前备受关注。在此框架下，本研究探讨了傅里叶变换（FT）近红外（NIR）光谱法和化学计量学在快速、准确地定量分析混纺产品中棉-聚酯含量方面是否有效。由于相同组成的样品存在许多影响近红外光谱的变异性来源，间接预测极具挑战性，需要大量样本群体来设计稳健的校准模型。因此，总共选择了三百多个棉-聚酯样品，涵盖0%至100%棉的范围，并对原始面料测量了相应的近红外反射光谱。所获得的数据集用于从参考测量值开发多变量定量预测模型。发现一种成功的方法是依靠偏最小二乘法（PLS）回归结合遗传算法（GAs）进行波长选择。这涉及评估一组考虑不同光谱区域的校准模型。考虑27.5%的原始变量所获得的结果在棉含量百分比方面产生了2.3的预测误差（RMSEP）。这表明傅里叶变换近红外光谱法有潜力在纺织工业中用于预测棉-聚酯混纺物的组成。此外，还观察到与全光谱方法相比，光谱预处理和模型的复杂性得到了简化。而且，变量选择后保留的光谱区间的相关性也值得探讨。
