Category identification of textile fibers based on near-infrared spectroscopy combined with data description algorithms

Abstract Cashmere is a kind of luxury fiber produced by goats and has high economic value. The temptation of huge profits makes it a common phenomenon to fake cashmere with cheap materials. There is increasing demand to develop simple methods for distinguishing cashmere with other animal fibers. The feasibility of combining near-infrared (NIR) spectroscopy and three kind of data descriptions, i.e., support vector data description(SVDD), k-nearest neighbor data description (KNNDD) and GAUSS methods, for this goal is explored. The Relieff algorithm is used for variable selection and principal component analysis (PCA) is used as an exploratory tool and feature extraction. A total of 395 samples belonging to four categories were collected for the experiment. The number of samples used for model construction are 69, 71, 61 and 50 for A, B, C and D as the target class, respectively. Based on the selected 67 variables and only two principal components (PCs), three types of data descriptions are obtained. The SVDD model exhibits the most flexible and tightest boundary and also achieves 100% sensitivity on the independent test set. It indicates that NIR combined with SVDD and Relieff is feasible for category identification of different animal fibers.

[1]  Marko Robnik-Sikonja,et al.  Theoretical and Empirical Analysis of ReliefF and RReliefF , 2003, Machine Learning.

[2]  Yibin Ying,et al.  Spectroscopy-based food classification with extreme learning machine , 2014 .

[3]  Zhaohong Deng,et al.  Robust Relief-Feature Weighting, Margin Maximization, and Fuzzy Optimization , 2010, IEEE Transactions on Fuzzy Systems.

[4]  Paolo Oliveri,et al.  Confirmation of brand identity in foods by near infrared transflectance spectroscopy using classification and class-modelling chemometric techniques — The example of a Belgian beer , 2011 .

[5]  Shien Li,et al.  Measurement of Diameter and Scale of Cashmere Fibers by Computer Images Analysis , 2012 .

[6]  Satoru Tsuchikawa,et al.  A review of recent application of near infrared spectroscopy to wood science and technology , 2015, Journal of Wood Science.

[7]  Hyung-Min Choi,et al.  Qualitative Identification of Cashmere and Yak Fibers by Protein Fingerprint Analysis Using Matrix-Assisted Laser Desorption/Ionization Time-of-Flight Mass Spectrometry , 2013 .

[8]  Lingzhi Zhao,et al.  Radial basis function neural networks in non-destructive determination of compound aspirin tablets on NIR spectroscopy. , 2006, Spectrochimica acta. Part A, Molecular and biomolecular spectroscopy.

[9]  Guohai Liu,et al.  Measurement of process variables in solid-state fermentation of wheat straw using FT-NIR spectroscopy and synergy interval PLS algorithm. , 2012, Spectrochimica acta. Part A, Molecular and biomolecular spectroscopy.

[10]  Wan Ji,et al.  A method for quantifying mixed goat cashmere and sheep wool. , 2011, Forensic science international.

[11]  Zou Xiaobo,et al.  Variables selection methods in near-infrared spectroscopy. , 2010, Analytica chimica acta.

[12]  Y. Roggo,et al.  A review of near infrared spectroscopy and chemometrics in pharmaceutical technologies. , 2007, Journal of pharmaceutical and biomedical analysis.

[13]  I-Chang Yang,et al.  Integration of SIMCA and near-infrared spectroscopy for rapid and precise identification of herbal medicines , 2013 .

[14]  R. Brereton.  One‐class classifiers , 2011 .

[15]  Lixiang Duan,et al.  A new support vector data description method for machinery fault diagnosis with unbalanced datasets , 2016, Expert Syst. Appl..

[16]  C. B. Davis,et al.  Rapid, Non-Destructive, Textile Classification Using SIMCA on Diffuse Near-Infrared Reflectance Spectra , 2015 .

[17]  M. Forina,et al.  Class-modeling techniques, classic and new, for old and new problems , 2008 .

[18]  Robert P. W. Duin,et al.  Support vector domain description , 1999, Pattern Recognit. Lett..

[19]  Chao Tan,et al.  Quantitative determination of wool in textile by near-infrared spectroscopy and multivariate models. , 2018, Spectrochimica acta. Part A, Molecular and biomolecular spectroscopy.

[20]  L. A. Stone,et al.  Computer Aided Design of Experiments , 1969 .

[21]  Annalisa Aluigi,et al.  Immunological method for the identification of animal hair fibres , 2012 .

[22]  C. Ruckebusch,et al.  Quantitative Analysis of Cotton—Polyester Textile Blends from Near-Infrared Spectra , 2006, Applied spectroscopy.

[23]  Oxana Ye. Rodionova,et al.  Discriminant analysis is an inappropriate method of authentication , 2016 .

[24]  Larry A. Rendell,et al.  A Practical Approach to Feature Selection , 1992, ML.

[25]  Tong Wu,et al.  Classification and quantitation of milk powder by near-infrared spectroscopy and mutual information-based variable selection and partial least squares. , 2018, Spectrochimica acta. Part A, Molecular and biomolecular spectroscopy.

[26]  R. Innocenti,et al.  Identification of wool, cashmere, yak, and angora rabbit fibers and quantitative determination of wool and cashmere in blend: a near infrared spectroscopy study , 2013, Fibers and Polymers.

[27]  P. Oliveri.  Class-modelling in food analytical chemistry: Development, sampling, optimisation and validation issues - A tutorial. , 2017, Analytica chimica acta.

[28]  C. Huck,et al.  Near-infrared reflection spectroscopy (NIRS) as a successful tool for simultaneous identification and particle size determination of amoxicillin trihydrate. , 2011, Journal of pharmaceutical and biomedical analysis.

[29]  Isabelle Guyon,et al.  An Introduction to Variable and Feature Selection , 2003, J. Mach. Learn. Res..

[30]  Naoshi Kondo,et al.  Quality Assessment of Poultry Egg Based on Visible–Near Infrared Spectroscopy and Radial Basis Function Networks , 2016 .

[31]  Yong He,et al.  [Identification of fine wool and cashmere by using Vis/NIR spectroscopy technology]. , 2008, Guang pu xue yu guang pu fen xi = Guang pu.

[32]  Yize Sun,et al.  Classification of textile fabrics by use of spectroscopy-based pattern recognition methods , 2016 .