近红外光谱结合BP神经网络测定棉涤混纺面料的纤维含量
Detection of Fiber Contents of Cotton and Terylene Mixture Textile by Near Infrared Spectroscopy Combined with BP Neural Network

作者: 刘 莉 * , 颜丽 , 谢尧城 , 李颂战 , 许杰 , 徐卫林 :武汉纺织大学材料科学与工程学院;

关键词: 混纺面料近红外光谱纤维含量BP神经网络小波变换Textile Mixture Near Infrared Spectroscopy Fiber Content BP Neural Network Wavelet Transform

摘要: 通过近红外光谱结合误差反向传播的人工神经网络来检测棉涤混纺面料中纤维含量。测量了4000 cm–1~10,000 cm–1范围内棉涤混纺面料样品的近红外吸收光谱。利用小波变换滤波技术对吸收光谱数据进行压缩和去噪处理,结合滤波后重构光谱信号建立了棉涤混纺面料中棉和涤纶含量的BP神经网络校正模型。优化了隐含层神经元的节点数、学习率、动量因子和学习次数。对小波变换中的小波基和压缩尺度进行了详细的讨论。棉涤混纺样品的近红外光谱经过小波压缩,可以大大降低数据运算量。在小波尺度为3、隐含层神经元节点数为17时,模型的预测精度最高。所建立的棉和涤纶含量校正模型的预测集相关系数(RP)均为0.998,预测均方根误差为1.260%和1.860%。实验结果表明,应用傅里叶变换近红外光谱和BP神经网络技术来预测棉涤混纺面料纤维含量,可以满足定量分析的要求,该方法也适合于其他混纺面料纤维含量的快速测定。

Abstract: The prediction of fiber contents of mixture textile by near infrared spectroscopy (NIR) combined with back propagation (BP) neural network was investigated. The near infrared spectrum of samples with different cotton and terylene contents were obtained in the range of 4000 cm–1 - 10,000 cm–1. Wavelet transform (WT) was used for spectra data de-noise and compression. The correction model of cotton and terylene content based on BP neural network and reconstruction spectral signals was established. The number of hidden neurons, learning rate, momentum and epochs were optimized and decomposition levels of WT was discussed. Data procession was greatly reduced after the spectra signals were compressed by WT. When the compression level and the number of hidden neurons are 3 and 17 respec-tively, the prediction accuracy is the best. Correlation coefficients (RP) of prediction set for the correction model of cot-ton and terylene content both are 0.998, and the root-mean-square error (RMSE) is 1.260% and 1.860% correspondingly. Experimental results have shown that this approach by Fourier transform NIR based on the BP neural network to predict the cotton and terylene content of textile mixture can satisfy the requirement of quantitative analysis and is also suitable to other fiber contents measurement of textile mixture.

文章引用: 刘 莉 , 颜丽 , 谢尧城 , 李颂战 , 许杰 , 徐卫林 (2012) 近红外光谱结合BP神经网络测定棉涤混纺面料的纤维含量。 现代物理, 2, 82-87. doi: 10.12677/MP.2012.24014

参考文献

[1] 周莹, 徐惠荣, 应义斌. 近红外技术在自然纺织纤维品种鉴别及成分预测上的应用[J]. 光谱学与光谱分析, 2008, 28(12): 2804-2807.

[2] 王徽蓉, 李卫军, 刘扬阳等. 基于遗传算法与线性鉴别的近红外光谱玉米品种鉴别研究[J]. 光谱学与光谱分析, 2011, 31(3): 669-672.

[3] 孙德勇, 李云梅, 王桥等. 利用高光谱数据估算太湖水体CDOM浓度的神经网络模型[J]. 武汉大学学报•信息科学版, 2009, 4(7): 851-855.

[4] T. A. Lestander, P. Geladi. NIR spec-tral information used to predict water content o f pine seeds from mul-tivariate calibration. Canadian Journal of Forest Research, 2005, 35(5): 1139-1248.

[5] V. A. Saptari, T. K. Youcef and J. Zhang. NIR meas-urement of glucose in synthetic biological solutions using high-throughput angle tuned filter spectrometer. The International Society for Optical Engineering, 2004, 5325(1): 1-10.

[6] 单扬, 朱向荣, 许青松等. 近红外光谱结合小波变换——径向基神经网络用于奶粉蛋白质与脂肪含量的测定[J]. 红外与毫米波学报, 2010, 29(2): 128-131.

[7] 刘炜, 常庆瑞, 郭曼, 邢东兴等. 夏玉米可见/近红外小波主成分提取与氮素含量神经网络检测[J]. 红外与毫米波学报, 2011, 30(1): 48-54.

[8] 张愿, 张录达, 白琪林等. 近红外光谱法快速无损识别普通、高油、超高油玉米籽粒[J]. 光谱学与光谱分析, 2009, 29(3): 686-689.

[9] 吴桂芳, 朱登胜, 何勇. 可见–近红外光谱用于鉴别山羊绒与细支绵羊毛的研究[J]. 光谱学与光谱分析, 2008, 8(6): 1260-1263.

[10] 陈斌, 崔广, 金尚忠等. 近红外光谱在快速检测棉制品中含棉量的应用[J]. 江苏大学学报(自然科学版), 2007, 28(3): 185- 188.

[11] 冯红年, 甘彬, 金尚忠. 棉涤混合纺织面料含量的近红外光谱检测[J]. 激光与红外, 2005, 35(10): 768-770.

[12] 陈斌, 王小天, 倪凯. 相关分析法在NIR快速检测纺织原料真丝含量中的应用[J]. 光谱仪器与分析, 2006, Z1: 52-56.

[13] 飞思科技产品研发中心. 小波分析理论与MATLAB R2007实现[M]. 北京: 电子工业出版社, 2005: 81.

[14] 葛哲学, 孙志强. 神经网络理论与MATLAB R2007实现[M]. 北京: 电子工业出版社, 2007: 46.

分享
Top