Chrimatopoulos Christoforos, Tummino Maria Laura, Iliadis Eleftherios, Tonetti Cinzia, Sakkas Vasilios
Department of Chemistry, School of Sciences, University of Ioannina, Ioannina, Greece.
Institute of Intelligent Industrial Technologies and Systems for Advanced Manufacturing, National Research Council of Italy (CNR-STIIMA), Biella, Italy.
Appl Spectrosc. 2025 Aug;79(8):1173-1184. doi: 10.1177/00037028241292372. Epub 2024 Nov 8.
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.
分析纺织品中动物毛发纤维的成分对于确保由动物毛发制成的纱线和织物的质量至关重要。其中,傅里叶变换红外(FT-IR)光谱法是一种能够识别与化学键相关振动的技术,包括在氨基酸基团中发现的那些振动。通过衰减全反射傅里叶变换红外(ATR FT-IR)光谱法和扫描电子显微镜技术对羊绒、马海毛、牦牛毛、骆驼毛、羊驼毛、小羊驼毛、原驼毛和羊毛纤维进行了分析,旨在区分它们,以识别可能的商业欺诈行为。ATR FT-IR作为一种新方法,与化学计量工具(偏最小二乘判别分析,PLS-DA)相结合,构建了经过交叉验证的分类/预测模型。PLS-DA模型能够很好地区分骆驼科动物毛发和八种动物毛发。此外,ATR FT-IR和PLS-DA的组合被用于区分不同产地(阿富汗、澳大利亚、中国、伊朗和蒙古)的羊绒。该模型显示出非常好的判别能力(准确率87%),方差解释率为94.88%,交叉验证的均方误差为0.1525。