A New Method for Qualitative Analysis of Near Infrared Spectra of Textiles
近红外光谱分析技术可用于对样本的快速无损检测, 在人们的生产和生活中发挥着越来越重要的作用。 支持向量机是建立定性分析模型的常用方法, 可通过寻找最优分类超平面将两类样本分开。 在小样本情况下, 支持向量机方法有其独特的优势。 主成分分析是常用的数据降维方法, 可将数据降维之后作为支持向量机方法的输入变量, 简化模型并提高模型识别的准确性。 因此, 基于主成分分析的支持向量机(简称PCA-SVM)适合用于建立近红外光谱定性分析模型。 多模型方法是人们使用较少的建模方法, 用该方法建立的模型一般具有较好的稳定性。 将多模型方法与PCA-SVM方法成功结合形成了新方法。 以棉锦混合、 棉涤混合纺织品为例, 用新方法建立了这两类纺织品样本的近红外光谱定性分析模型。 建模时将光谱数据按照波长分为4组, 用每组光谱数据建立一个子模型, 将子模型的输出值进行加权平均便得到最终的预测结果。 这样可以更充分地使用光谱数据中所包含的信息。 为了便于对比不同的方法, 仍使用上述校正集和验证集, 又用PCA-SVM方法建立了这两类纺织品样本的近红外光谱定性分析模型。 对预测结果做交叉验证, 用新方法所建模型判别的正确率的平均值为85.49%, 正确率的标准差为0.066 7, 用PCA-SVM方法所建模型判别的正确率的平均值为83.34%, 正确率的标准差为0.109 6。 研究结果表明用新方法所建模型的分类效果好于用PCA-SVM方法所建模型的分类效果; 用新方法建立的模型的稳定性明显高于用PCA-SVM方法建立的模型的稳定性。 用PCA-SVM方法所建模型的预测效果受校正集构成情况的影响较大, 而用新方法所建模型的预测效果则相对稳定。 对废旧纺织品进行分类回收可大量节约纺织原材料, 但采用人工分拣方式效率低且成本高。 采用近红外光谱分析方法对纺织品进行分类, 为废旧纺织品的大规模精细分拣和分级奠定了一定的基础。 该新方法有望用于某些其他类型样本的分类。
Near infrared spectral analysis technique can be used to detect samples quickly and nondestructively, which is playing an increasingly important role in people’s production and life. The support vector machine is a commonly used method for building qualitative analysis models. It separates two kinds of samples by finding the optimal classification hyperplane. In the case of small samples, the support vector machine method has its unique advantages. The principal component analysis is a commonly used method to reduce the dimension of data. After the dimension is reduced by this method, the data is used as input variables of the support vector machine method. The model can be simplified and the accuracy of discriminating by the model can be improved in this way. So the support vector machine based on the principal component analysis (PCA-SVM for short) is suitable for establishing the qualitative analysis model of near infrared spectroscopy. The multi-model method is a modeling method seldom used by people. The model established by this method usually has good stability. The multi-model method is successfully combined with the PCA-SVM method to form a new method in this paper. With cotton and nylon blended, cotton and polyester blended textiles being taken as an example, a qualitative analysis model of near infrared spectra of these two types of textile samples is established by the new method. In modeling, the spectral data are divided into 4 groups according to the wavelengths. A sub model is established with each group of spectral data. The final prediction results are obtained by weighted average of the output values of the sub models. The information contained in the spectral data can be used more fully in this way. In order to facilitate the comparison of different methods, the aforementioned calibration set and validation set are used. A qualitative analysis model of near infrared spectra of these two types of textile samples is also established by using the PCA-SVM method in the paper. The cross validation of the prediction results show that the mean value of the correct rate of discrimination by the model built with the new method is 85.49%, the standard deviation of the correct rate of it is 0.066 7, and the mean value of the correct rate of discrimination by the model built with the PCA-SVM method is 83.34%, the standard deviation of the correct rate of it is 0.109 6. Since the mean value 85.49% is higher than the mean value 83.34%, the classification effect of the model built by the new method is better than that built by the PCA-SVM method. Since the standard deviation 0.066 7 is much smaller than the standard deviation 0.109 6, the stability of the model built by the new method is obviously higher than that built by the PCA-SVM method. The prediction effect of the model built by the PCA-SVM method is greatly influenced by the composition of the calibration set. But the prediction effect of the model built by the new method is relatively stable. Sorting and recycling waste textiles can save a lot of textile raw materials. However, manual sorting is inefficient and costly. Classification of textiles by using the method of near infrared spectra analysis is proposed in this paper, which lays a certain foundation for large-scale fine sorting and grading of waste textiles. The new method put forward in the paper is also expected to be used for classification of some other types of samples.
刘 胜：北京林业大学理学院, 北京 100083
备注：李海洋, 女, 1993年生, 北京林业大学理学院硕士研究生
【1】Hu Changqin, Feng Yanchun, Yin Lihui. J. Near Infrared Spectrosc., 2015, 23(5): 271.
【2】LI Zheng-feng, XU Guang-jin, WANG Jia-jun, et al(李正风, 徐广晋, 王家俊, 等). Chinese J. Anal. Chem.(分析化学), 2016, 44(2): 305.
【4】Chalermpun Thamasopinkul, Pitiporn Ritthiruangdej, Sumaporn Kasemsumran, et al. J. Near Infrared Spectrosc., 2017, 25(1): 36.
【5】YANG Xiao-wei, HAO Zhi-feng(杨晓伟, 郝志峰). Algorithm Design and Analysis of Support Vector Machine(支持向量机的算法设计与分析). Beijing: Science Press(北京: 科学出版社), 2013. 15.
【6】Mu Weilei, Gao Jianmin, Jiang Hongquan, et al. Insight: Non-Destructive Testing and Condition Monitoring, 2013, 55(10): 535.
【7】Kuang Fangjun, Zhang Siyang, Jin Zhong, et al. Soft Computing, 2015, 19(5): 1187.
【8】Villa-Manríquez J F, Castro-Ramos J, Gutiérrez-Delgado F, et al. Journal of Biophotonics, 2017, 10(8): 1074.
【9】Saeed Bashiri, Abbas Akbarzadeh, Mansur Zarrabi, et al. Environmental Engineering & Management Journal, 2017, 16(9): 2139.
【10】WEI Wei-wei, WANG Wei-wei, SONG Xiang-gang, et al(魏伟伟, 王伟伟, 宋向岗, 等). Journal of Analytical Science(分析科学学报), 2015, 31 (2): 257.
LI Hai-yang,LIU Sheng. A New Method for Qualitative Analysis of Near Infrared Spectra of Textiles[J]. Spectroscopy and Spectral Analysis, 2019, 39(7): 2142-2146
李海洋,刘 胜. 纺织品近红外光谱定性分析的一种新方法[J]. 光谱学与光谱分析, 2019, 39(7): 2142-2146