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首页> 外文期刊>Applied Spectroscopy: Society for Applied Spectroscopy >Fiber-Content Measurement of Wool–Cashmere Blends Using Near-Infrared Spectroscopy
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Fiber-Content Measurement of Wool–Cashmere Blends Using Near-Infrared Spectroscopy

机译:使用近红外光谱法测定羊毛羊绒混合物的纤维含量测量

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>Cashmere and wool are two protein fibers with analogous geometrical attributes, but distinct physical properties. Due to its scarcity and unique features, cashmere is a much more expensive fiber than wool. In the textile production, cashmere is often intentionally blended with fine wool in order to reduce the material cost. To identify the fiber contents of a wool–cashmere blend is important to quality control and product classification. The goal of this study is to develop a reliable method for estimating fiber contents in wool–cashmere blends based on near-infrared (NIR) spectroscopy. In this study, we prepared two sets of cashmere–wool blends by using either whole fibers or fiber snippets in 11 different blend ratios of the two fibers and collected the NIR spectra of all the 22 samples. Of the 11 samples in each set, six were used as a subset for calibration and five as a subset for validation. By referencing the NIR band assignment to chemical bonds in protein, we identified six characteristic wavelength bands where the NIR absorbance powers of the two fibers were significantly different. We then performed the chemometric analysis with two multilinear regression (MLR) equations to predict the cashmere content (CC) in a blended sample. The experiment with these samples demonstrated that the predicted CCs from the MLR models were consistent with the CCs given in the preparations of the two sample sets (whole fiber or snippet), and the errors of the predicted CCs could be limited to 0.5% if the testing was performed over at least 25 locations. The MLR models seem to be reliable and accurate enough for estimating the cashmere content in a wool–cashmere blend and have potential to be used for tackling the cashmere adulteration problem.
机译:>羊绒和羊毛是两种具有类似几何属性的蛋白质纤维,但物理性质不同。由于其稀缺和独特的功能,羊绒比羊毛更昂贵。在纺织品生产中,羊绒通常与细羊毛有意混合,以降低材料成本。为了识别羊毛羊绒混合物的纤维内容对质量控制和产品分类非常重要。本研究的目的是开发一种可靠的方法,可根据近红外(NIR)光谱法估算羊毛羊绒共混物中的纤维内容。在这项研究中,我们通过使用两种纤维的11种不同的混合比中使用整个纤维或纤维片段制备了两套羊绒羊毛混合物,并收集了所有22个样品的NIR光谱。在每个集合中的11个样本中,六个被用作校准子集,五作为验证的子集。通过将NIR带分配参考蛋白质中的化学键,我们鉴定了六个特征波长带,其中两根纤维的NIR吸光度显着不同。然后,我们用两种多线性回归(MLR)方程进行化学计量分析,以预测混合样品中的羊绒含量(CC)。具有这些样品的实验证明来自MLR模型的预测CCS与在两个样品组(整纤维或片段)的制剂中给出的CCS一致,并且预测的CCS的误差可能限于0.5%测试在至少25个位置进行。 MLR模型似乎可靠,准确,足以估算羊毛羊绒混合物中的羊绒内容,并有可能用于解决羊绒掺假问题。

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