基于红外光谱PCA-LDA统计分析的麻纤维鉴别研究

Discrimination research of bast fibers by PCA-LDA statistical analysis on infrared spectra

亚麻、汉麻与苎麻纤维的成分组成和物化性质高度相似,三者间的分类鉴别是纺织品检验检测领域的难点。本文对不同种类麻纤维的傅里叶变换衰减全反射红外光谱(ATR-FTIR)作主成分分析(PCA)和线性判别分析(LDA),创建麻纤维分类判别模型以鉴别三种易混麻纤维。选取亚麻、汉麻和苎麻纤维各60组作为样品集进行脱胶清洗处理并采集ATR-FTIR光谱。光谱归一化后对800~2000 cm-1波长的光谱作主成分分析,分析结果显示:随着主成分个数增加,主成分分数依据麻纤维类别逐渐显现聚类趋势,同时前12个主成分对归一化红外光谱数据的累计贡献率超过99.5%。以训练集前12主成分数为自变量,以麻纤维种类为因变量,通过线性判别分析构建了分类判别模型(典型判别函数和分类函数)。模型验证结果显示:典型判别函数可使前12个主成分分数矩阵根据麻纤维样品类型形成良好的聚类,分类函数对训练集和测试集中所有纤维样品的分类准确率达到100%。此外,PCA-LDA分类判别模型留一交叉验证的分类准确率仍能达到99.6%。结果表明,不同类别麻纤维的ATR-FTIR光谱存在差异,基于麻纤维ATR-FTIR光谱的PCA-LDA统计分析可实现亚麻、汉麻和苎麻三种易混麻纤维的快速无损鉴别。

Flax,hemp and ramie fibers have been important raw materials for textile products since ancient times.Due to the difference in use and output,there has been a significant gap in the market price of these three different species of bast fibers in recent years,and the illegal phenomenon of low-price bast fibers posing as high-price bast fibers often occurs.The composition,physical and chemical properties of flax,hemp and ramie fibers are very close.At present,the standard method for identifying bast fibers in China is FZ/T 01057.3-2007 The Method for Identification of Textile Fibers-Part 3:Microscopy.However,the microscopic morphology of flax,hemp and ramie is highly similar,and the accuracy of identifying bast fibers according to this method is insufficient.The identification of these three species of easily confused bast fibers has always been a difficult task in the field of textile inspection and testing,so it is necessary to improve the identification technology of bast fibers to maintain market stability and strengthen quality supervision.Although the predecessors have found that there are relatively slight infrared spectral differences between different kinds of bast fibers,no rapid and accurate classification method for bast fibers based on the differences in infrared spectral data has been reported.In our study,the principal component analysis(PCA)and linear discriminant analysis(LDA)on attenuated total reflection Flourier transformed infrared spectroscopy(ATR-FTIR)of different kinds of bast fibers were proposed to establish a discriminant model to identify these three kinds of easily confused bast fibers.In this study,60 groups of flax,hemp and ramie fibers were selected as sample sets for degumming cleaning treatment and their ATR-FTIR spectra were collected.After spectral normalization,infrared spectra in the range of 800-2000 cm-1 were compressed to scores of sets of principal components(PCs)by PCA.The PCA results show that:with the increase of the number of PCs,the PC scores gradually display a clustering trend according to the species of bast fibers,and the cumulative variance contribution rate of the first 12 PCs to the normalized spectral data reaches 99.5%.With the first 12 PC scores as the independent variable and the species of fiber samples as the dependent variable,a discriminant model of the bast fibers including typical discriminant functions and classification functions was established by LDA.The discriminant model verification results show that:the typical discriminant functions can make the first 12 PC scores achieve a excellent cluster according to the species of fiber samples,and the classification functions can reach a classification accuracy of 100%for all fiber samples in the training set and the test set.In addition,the classification accuracy of the PCA-LDA model can still reach 99.6%by leave-one-out cross-validation.These results prove that the differences between ATR-FTIR spectra of flax,hemp,and ramie can be discerned and utilized for the rapid and accurate identification of these easily confused textile bast fibers by PCA-LDA statistic analysis.In the follow-up study,we will introduce more laboratories and samples to participate in the construction and verification of the PCA-LDA model,so as to further improve the applicability of the bast fiber classification and identification model.