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International Journal of Chemical Engineering
Volume 2019, Article ID 8256817, 8 pages
https://doi.org/10.1155/2019/8256817
Research Article

Simultaneous Determination of Several Fiber Contents in Blended Fabrics by Near-Infrared Spectroscopy and Multivariate Calibration

Hui Chen,1,2 Zan Lin,1,3 and Chao Tan1

1Key Lab of Process Analysis and Control of Sichuan Universities, Yibin University, Yibin, Sichuan 644000, China
2Hospital, Yibin University, Yibin, Sichuan 644000, China
3The First Affiliated Hospital, Chongqing Medical University, Chongqing 400016, China

Correspondence should be addressed to Chao Tan; moc.361@2111natoahc

Received 12 November 2018; Accepted 18 December 2018; Published 3 January 2019

Academic Editor: Bhaskar Kulkarni

Copyright © 2019 Hui Chen et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Linked References

  1. X. Sun, M. Zhou, and Y. Sun, “Classification of textile fabrics by use of spectroscopy-based pattern recognition methods,” Spectroscopy Letters, vol. 49, no. 2, pp. 96–102, 2015. View at Publisher · View at Google Scholar · View at Scopus
  2. H. Chen, C. Tan, and Z. Lin, “Quantitative determination of wool in textile by near-infrared spectroscopy and multivariate models,” Spectrochimica Acta Part A: Molecular and Biomolecular Spectroscopy, vol. 201, pp. 229–235, 2018. View at Publisher · View at Google Scholar · View at Scopus
  3. C. Ruckebusch, F. Orhan, A. Durand, T. Boubellouta, and J. P. Huvenne, “Quantitative analysis of cotton-polyester textile blends from near-infrared spectra,” Applied Spectroscopy, vol. 60, no. 5, pp. 539–544, 2016. View at Publisher · View at Google Scholar · View at Scopus
  4. E. Cleve, E. Bach, and E. Schollmeyer, “Using chemometric methods and NIR spectrophotometry in the textile industry,” Analytica Chimica Acta, vol. 420, no. 2, pp. 163–167, 2000. View at Publisher · View at Google Scholar · View at Scopus
  5. C. A. Fortier, J. E. Rodgers, M. S. Cintrón, X. Xiaoliang Cui, and J. A. Foulk, “Identification of cotton and cotton trash components by Fourier transform near-infrared spectroscopy,” Textile Research Journal, vol. 81, no. 3, pp. 230–238, 2010. View at Publisher · View at Google Scholar · View at Scopus
  6. M. Zoccola, N. Lu, R. Mossotti, R. Innocenti, and A. Montarsolo, “Identification of wool, cashmere, yak, and angora rabbit fibers and quantitative determination of wool and cashmere in blend: a near infrared spectroscopy study,” Fibers and Polymers, vol. 14, no. 8, pp. 1283–1289, 2013. View at Publisher · View at Google Scholar · View at Scopus
  7. M. Bassbasi, M. De Luca, G. Ioele, A. Oussama, and G. Ragno, “Prediction of the geographical origin of butters by partial least square discriminant analysis (PLS-DA) applied to infrared spectroscopy (FTIR) data,” Journal of Food Composition and Analysis, vol. 33, no. 2, pp. 210–215, 2014. View at Publisher · View at Google Scholar · View at Scopus
  8. J. A. Cayuela and J. F. García, “Nondestructive measurement of squalene in olive oil by near infrared spectroscopy,” LWT, vol. 88, pp. 103–108, 2018. View at Publisher · View at Google Scholar · View at Scopus
  9. R. Lapchareonsuk and P. Sirisomboon, “Sensory quality evaluation of rice using visible and shortwave near-infrared spectroscopy,” International Journal of Food Properties, vol. 18, no. 5, pp. 1128–1138, 2014. View at Publisher · View at Google Scholar · View at Scopus
  10. J. Luypaert, D. L. Massart, and Y. Vander Heyden, “Near-infrared spectroscopy applications in pharmaceutical analysis,” Talanta, vol. 72, no. 3, pp. 865–883, 2007. View at Publisher · View at Google Scholar · View at Scopus
  11. B. Igne, A. d. Juan, J. Jaumot et al., “Modeling strategies for pharmaceutical blend monitoring and end-point determination by near-infrared spectroscopy,” International Journal of Pharmaceutics, vol. 473, no. 1-2, pp. 219–231, 2014. View at Publisher · View at Google Scholar · View at Scopus
  12. O. Y. Rodionova and A. L. Pomerantsev, “NIR-based approach to counterfeit-drug detection,” TrAC Trends in Analytical Chemistry, vol. 29, no. 8, pp. 795–803, 2010. View at Publisher · View at Google Scholar · View at Scopus
  13. C. Tan, X. Qin, and M. L. Li, “Comparison of chemometric methods for brand classification of cigarettes by near-infrared spectroscopy,” Vibrational Spectroscopy, vol. 51, no. 2, pp. 276–282, 2008. View at Publisher · View at Google Scholar · View at Scopus
  14. X.-L. Chu, Y.-P. Xu, S.-B. Tian, J. Wang, and W.-Z. Lu, “Rapid identification and assay of crude oils based on moving-window correlation coefficient and near infrared spectral library,” Chemometrics and Intelligent Laboratory Systems, vol. 107, no. 1, pp. 44–49, 2011. View at Publisher · View at Google Scholar · View at Scopus
  15. S. Tsuchikawa and H. Kobori, “A review of recent application of near infrared spectroscopy to wood science and technology,” Journal of Wood Science, vol. 61, no. 3, pp. 213–220, 2015. View at Publisher · View at Google Scholar · View at Scopus
  16. M. Khanmohammadi and A. B. Garmarudi, “Infrared spectroscopy provides a green analytical chemistry tool for direct diagnosis of cancer,” TrAC Trends in Analytical Chemistry, vol. 30, no. 6, pp. 864–874, 2011. View at Publisher · View at Google Scholar · View at Scopus
  17. C. Tan, X. Qin, and M. Li, “Subspace regression ensemble method based on variable clustering for near-infrared spectroscopic calibration,” Analytical Letters, vol. 42, no. 11, pp. 1693–1710, 2009. View at Publisher · View at Google Scholar · View at Scopus
  18. C. H. Spiegelman, M. J. McShane, M. J. Goetz, M. Motamedi, Q. L. Yue, and G. L. Coté, “Theoretical justification of wavelength selection in PLS calibration: development of a new algorithm,” Analytical Chemistry, vol. 70, no. 1, pp. 35–44, 1998. View at Publisher · View at Google Scholar · View at Scopus
  19. A. Borin and R. J. Poppi, “Application of mid infrared spectroscopy and iPLS for the quantification of contaminants in lubricating oil,” Vibrational Spectroscopy, vol. 37, no. 1, pp. 27–32, 2005. View at Publisher · View at Google Scholar · View at Scopus
  20. V. Center, D. L. Massart, O. E. Noord, S. Jong, and B. M. Vandeginste, “Elimination of uninformative variables for multivariate calibration,” Analytical Chemistry, vol. 68, no. 21, pp. 3851–3858, 1996. View at Publisher · View at Google Scholar · View at Scopus
  21. R. M. Balabin and S. V. Smirnov, “Variable selection in near-infrared spectroscopy: benchmarking of feature selection methods on biodiesel data,” Analytica Chimica Acta, vol. 692, no. 1-2, pp. 63–72, 2011. View at Publisher · View at Google Scholar · View at Scopus
  22. S. Wold, M. Sjöström, and L. Eriksson, “PLS-regression: a basic tool of chemometrics,” Chemometrics and Intelligent Laboratory Systems, vol. 58, no. 2, pp. 109–130, 2001. View at Publisher · View at Google Scholar · View at Scopus
  23. P. Geladi and B. R. Kowalski, “Partial least-squares regression: a tutorial,” Analytica Chimica Acta, vol. 185, pp. 1–17, 1986. View at Publisher · View at Google Scholar · View at Scopus
  24. C. Tan, T. Wu, Z. Xu, W. Li, and K. Zhang, “A simple ensemble strategy of uninformative variable elimination and partial least-squares for near-infrared spectroscopic calibration of pharmaceutical products,” Vibrational Spectroscopy, vol. 58, pp. 44–49, 2012. View at Publisher · View at Google Scholar · View at Scopus
  25. S. Ye, D. Wang, and S. Min, “Successive projections algorithm combined with uninformative variable elimination for spectral variable selection,” Chemometrics and Intelligent Laboratory Systems, vol. 91, no. 2, pp. 194–199, 2008. View at Publisher · View at Google Scholar · View at Scopus
  26. A. Candolfi, R. De Maesschalck, D. Jouan-Rimbaud, P. A. Hailey, and D. L. Massart, “The influence of data pre-processing in the pattern recognition of excipients near-infrared spectra,” Journal of Pharmaceutical and Biomedical Analysis, vol. 21, no. 1, pp. 115–132, 1999. View at Publisher · View at Google Scholar · View at Scopus