Classification of textile fabrics by use of spectroscopy-based pattern recognition methods

ABSTRACT The combination of near-infrared spectroscopy and pattern recognition methods, including soft independent modeling of class analogy, least squares support machine, and extreme learning machine, was employed for textile fabrics classification. The fabrics of cotton, viscose, acrylic, polyamide, polyester, and blend fabric of cotton-viscose were divided into training and prediction sets (60:60) for developing models and evaluating the classification abilities of the models. The classification accuracy and speed of soft independent modeling of class analogy, least squares support machine, and extreme learning machine were compared. Both least squares support machine and extreme learning machine achieved the classification accuracy of 100% for the prediction set. However, extreme learning machine performed much faster than least squares support machine, which suggested that extreme learning machine may be a promising method for real-time textile fabrics classification with a comparable accuracy based on near-infrared spectroscopy. Moreover, it might have commercial and regulatory potential to avoid time-consuming work, and costly and laborious chemical analysis for textile fabrics classification.

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

[2]  J. Roger,et al.  Application of LS-SVM to non-linear phenomena in NIR spectroscopy: development of a robust and portable sensor for acidity prediction in grapes , 2004 .

[3]  Elke Bach,et al.  Using chemometric methods and NIR spectrophotometry in the textile industry , 2000 .

[4]  Quansheng Chen,et al.  Feasibility study on qualitative and quantitative analysis in tea by near infrared spectroscopy with multivariate calibration. , 2006, Analytica chimica acta.

[5]  James Rodgers,et al.  NIR Characterization and Measurement of the Cotton Content of Dyed Blend Fabrics , 2009 .

[6]  Jacek M. Zurada,et al.  Review and performance comparison of SVM- and ELM-based classifiers , 2014, Neurocomputing.

[7]  Lisbeth G. Thygesen,et al.  NIR Measurement of Moisture Content in Wood under Unstable Temperature Conditions. Part 1. Thermal Effects in near Infrared Spectra of Wood , 2000 .

[8]  Chunyan Miao,et al.  Comparing the learning effectiveness of BP, ELM, I-ELM, and SVM for corporate credit ratings , 2014, Neurocomputing.

[9]  Xiaoli Li,et al.  Non-destructive discrimination of Chinese bayberry varieties using Vis/NIR spectroscopy , 2007 .

[10]  David J. Hewson,et al.  Classifying NIR spectra of textile products with kernel methods , 2007, Eng. Appl. Artif. Intell..

[11]  Yi Zhang,et al.  A preliminary study on time series forecast of fair-weather atmospheric electric field with WT-LSSVM method , 2015 .

[12]  L. C. Kasun,et al.  Representational Learning with Extreme Learning Machine for Big Data Liyanaarachchi , 2022 .

[13]  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.

[14]  J. Coello,et al.  Use of near-infrared spectrometry in control analyses of acrylic fibre manufacturing processes , 1999 .

[15]  Michal Šejnoha,et al.  Qualitative analysis of fiber composite microstructure: Influence of boundary conditions , 2006 .

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

[17]  Ling Lin,et al.  Classification of diabetes and measurement of blood glucose concentration noninvasively using near infrared spectroscopy , 2014 .

[18]  Guang-Bin Huang,et al.  Trends in extreme learning machines: A review , 2015, Neural Networks.

[19]  J. Foulk,et al.  Identification of cotton and cotton trash components by Fourier transform near-infrared spectroscopy , 2011 .

[20]  A. Peirs,et al.  Nondestructive measurement of fruit and vegetable quality by means of NIR spectroscopy: A review , 2007 .

[21]  Dipankar Das,et al.  Enhanced SenticNet with Affective Labels for Concept-Based Opinion Mining , 2013, IEEE Intelligent Systems.

[22]  Dianhui Wang,et al.  Extreme learning machines: a survey , 2011, Int. J. Mach. Learn. Cybern..

[23]  Desire L. Massart,et al.  Artificial neural networks in classification of NIR spectral data: Selection of the input , 1996 .

[24]  Serge Kokot,et al.  Vibrational spectroscopy investigation of Australian cotton cellulose fibres.Part 2. A Fourier transform near-infrared preliminary study† , 1998 .

[25]  Sheng Li,et al.  Classification of gasoline brand and origin by Raman spectroscopy and a novel R-weighted LSSVM algorithm , 2012 .

[26]  Dawei Han,et al.  Assessment of input variables determination on the SVM model performance using PCA, Gamma test, and forward selection techniques for monthly stream flow prediction , 2011 .

[27]  Jordi Coello,et al.  NIR calibration in non-linear systems: different PLS approaches and artificial neural networks , 2000 .

[28]  Bugao Xu,et al.  Verification study on AutoRate fabric smoothness grading , 2002 .

[29]  Yuanyan Tang,et al.  Combination of activation functions in extreme learning machines for multivariate calibration , 2013 .

[30]  Johan A. K. Suykens,et al.  Least Squares Support Vector Machine Classifiers , 1999, Neural Processing Letters.

[31]  Guang-Bin Huang,et al.  What are Extreme Learning Machines? Filling the Gap Between Frank Rosenblatt’s Dream and John von Neumann’s Puzzle , 2015, Cognitive Computation.

[32]  Marcelo Blanco,et al.  NIR spectroscopy: a rapid-response analytical tool , 2002 .