光谱学与光谱分析, 2019, 39 (2): 634, 网络出版: 2019-03-06  

涤棉混纺织物近红外定量分析模型的建立及相关问题探讨

NIR Quantitative Model Establishment of Cotton-Polyester Blend Fabrics and Related Problem Exploration
作者单位
1 北京服装学院材料科学与工程学院, 北京 100029
2 京环纺织品再利用邯郸有限公司, 河北 邯郸 056800
3 东华大学材料科学与工程学院, 上海 201620
4 浙江绿宇环保股份有限公司, 浙江 绍兴 312000
摘要
在废旧纺织品回收再利用中, 纤维类型和含量的快速、准确测定是回收方案的关键部分。以598个废旧涤棉混纺织物为研究对象, 采用便携式近红外(NIR) 光谱仪测试了样品的原始近红外光谱。在1 400~1 700和1 900~2 200 nm光谱区域, 100%棉和100%聚酯样品的光谱存在明显差异, 并且这些光谱差异存在于各种颜色纤维上。同时探讨了斜线光谱产生的原因可能是由于织物表面效果、着色方法及粘附在纤维表面的细小颗粒造成的。深色样品易造成其光谱基线在短波区发生漂移, 经导数预处理后, 基线漂移基本消除, 斜线光谱呈现出正常光谱的特征。 利用偏最小二乘(PLS) 法结合一阶导数、 S-G平滑、 均值中心化和正交信号校正法, 建立了废旧棉-涤混纺织物定量分析模型。 为了验证模型的可靠性, 选取346个样本采用内部交叉验证均方根误差(RMSECV) 和预测样品集外部检验法对模型进行检验, 模型的RMSECV值0.002、 校正集相关系数RC=0.998、 预测相关系数RP=0.997、 预测标准差SEP=1.121, 模型预测正确率可达97%。 对模型进行匹配样本t检验结果显示, NIR方法与国家标准方法无显着性差异。 NIR预测值与重量法测定值误差在±3%以内时, 二者的一致性在90%以上, 当误差在±5%以内, 二者的一致性在95%以上, 分析时间小于10 s。 因此, 利用近红外技术结合所建模型可以快速、 准确地预测废旧棉/涤混纺织物纤维成分的含量。
Abstract
In waste textile recycling, the rapid and accurate determination of fiber type and content is a key part of the recovery program. In this paper, 598 waste cotton-polyester blended fabrics were used as the research object, and the raw near infrared spectra (NIRS) of the samples were tested by portable NIR spectrometer. In the 1 400~1 700 and 1 900~2 200 nm NIR regions, there was a clear difference between the spectra of 100% cotton and 100% polyester samples, and these spectral differences were reflected in the various color fibers. At the same time, the reason why the slant spectrum was produced might be surface effect of the fabric, the coloring method and the fine particles adhered to the fiber surface. Dark samples tended to drift their spectral baselines in the shortwave region. After the derivative pretreatment, the baseline drift was basically eliminated, and the oblique spectrum showed normal spectral characteristics. The quantitative analysis model of waste cotton-polyester blend fabric was established by partial least squares (PLS) method combined with 1st derivative, Savitzky-Golay (S-G) smoothing, mean centering and orthogonal signal correction (OSC) method. In order to verify the reliability of the model, root mean standard error of cross validation (RMSECV) was calculated and 346 external samples were selected to test the model. The RMSECV of the model was 0.002, and the relational coefficient of calibration ( RC ) was 0.998, and the relational coefficient of prediction ( RP ) was 0.997, and the standard error of prediction (SEP) was 1.121, and the prediction accuracy of the model was up to 97%. The error of NIR predictive value and gravimetric determination was within ±3%, and the consistency between the two is more than 90% , while the error was within ±5%, and the consistency was above 95%, and the analysis time of each sample was less than 10 seconds. Therefore, the waste cotton-polyester blend fabric fiber content could be quickly and accurately predicted by using NIR technology combined with the model.

时瑶, 李文霞, 赵国樑, 李书润, 王华平, 张朔. 涤棉混纺织物近红外定量分析模型的建立及相关问题探讨[J]. 光谱学与光谱分析, 2019, 39(2): 634. SHI Yao, LI Wen-xia, ZHAO Guo-liang, LI Shu-run, WANG Hua-ping, ZHANG Shuo. NIR Quantitative Model Establishment of Cotton-Polyester Blend Fabrics and Related Problem Exploration[J]. Spectroscopy and Spectral Analysis, 2019, 39(2): 634.

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