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SPECTROSCOPY

Characterization of Tobacco Leaves by Near-Infrared Reflectance Spectroscopy and Electronic Nose with Support Vector Machine

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Pages 1935-1943 | Received 27 Aug 2017, Accepted 17 Oct 2017, Published online: 28 Mar 2018

References

  • Baker, R. R., J. R. Pereira da Silva, and G. Smith. 2004. The effect of tobacco ingredients on smoke chemistry. Part I: Flavourings and additives. Food Chem. Toxicol. 42 (Suppl.):3–37.
  • Berrueta, L. A., R. M. Alonso-Salces, and K. Heberger. 2007. Supervised pattern recognition in food analysis. J. Chromatogr. A 1158:196–214.
  • Brezmes, J., M. L. L. Fructuoso, E. Llobet, X. Vilanova, I. Recasens, J. Orts, G. Saiz, and X. Correig. 2005. Evaluation of an electronic nose to assess fruit ripeness. IEEE Sens. J. 5:97–108.
  • Brudzewski, K., S. Osowski, and A. Golembiecka. 2012. Differential electronic nose and support vector machine for fast recognition of tobacco. Expert Syst. Appl. 39:9886–91.
  • Burbidge, R., M. Trotter, B. Buxton, and S. Holden. 2001. Drug design by machine learning: Support vector machines for pharmaceutical data analysis. Comput. Chem. 26:5–14.
  • Chao, T., H. Chen, T. Wu, Z. H. Xu, W. Y. Li, and X. Qin. 2013. Determination of total sugar in tobacco by near-infrared spectroscopy and wavelet transformation-based calibration. Anal. Lett. 46:171–183.
  • Chen, N. Y., W. C. Lu, J. Yang, and G. Z. Li. 2004. Support vector machine in chemistry Singapore. Singapore: World Scientific Publishing Co. Pte. Ltd.
  • Cheng, Z. J., G. Warwick, D. H. Yates, and P. S. Thomas. 2009. An electronic nose in the discrimination of breath from smokers and non-smokers: A model for toxin exposure. J. Breath Res. 3:5.
  • Dong, W., Y. S. Ding, Z. H. Guo, and S. G. Min. 2014. The application of near-infrared spectra micro-image in the imaging analysis of biology samples. J. Innov. Opt. Heal. Sci. 7:1350062(10 pages).
  • Falasconi, M., M. Pardo, G. Sberveglieri, F. Battistutta, M. Piloni, and R. Zironi. 2005. Study of white truffle aging with SPME-GC-MS and the Pico2-electronic nose. Sens. Actuators, B 106:88–94.
  • Gomez, A. H., G. X. Hu, J. Wang, and A. G. Pereira. 2006. Evaluation of tomato maturity by electronic nose. Comput. Electron. Agric. 54:44–52.
  • Guo, Y. B., W. R. Cai, K. Tu, S. C. Tu, S. M. Wang, X. L. Zhu, and W. Zhang. 2013. Infrared and Raman spectroscopic characterization of structural changes in albumin, globulin, glutelin, and prolamin during rice aging. J. Agric. Food. Chem. 61:185–192.
  • Heshka, N. E., and D. B. Hager. 2015. Measurement of H2S in crude oil and crude oil headspace using multidimensional gas chromatography, deans switching and sulfur-selective detection. J. Vis. Exp. 106:e53416.
  • Jenkins, T. J., M. Kaplan, G. Davidson, M. A. Healy, and M. Poliakoff. 1992. Dramatic advances in on-line Fourier transform IR detection for capillary supercritical fluid chromatography. J. Chromatogr. A 626:53–58.
  • Kai, Y., J. Y. Cai, Z. Y. Yang, R. X. Shu, M. Liang, L. L. Zhao, L. D. Zhang, Y. H. Zhang, and J. H. Li. 2014. Analysis of tobacco site features using near-infrared spectroscopy and projection model. Spectrosc. Spect. Anal. 34 (12):3277–3280.
  • Ködderitzsch, P., R. Bischoff, P. Veitenhansl, W. Lorenz, and G. Bischoff. 2005. Sensor array based measurement technique for fast-responding cigarette smoke analysis. Sens. Actuators, B 107:479–89.
  • Lu, W. C., N. Y. Chen, Z. C. Ye, and G. Z. Li. 2002. Introduction to the algorithm of support vector machine and the software ChemSVM. Comput. Appl. Chem. 19:697–702.
  • Norris, K. H., R. F. Barnes, J. E. Moore, and J. S. Shenk. 1976. Predicting forage quality by infrared reflectance spectroscopy. J. Anim. Sci. 43:889–897.
  • Rajamaki, T., H. L. Alakomi, T. Ritvanen, E. S. Skytta, M. Smolander, and R. Ahvenainen. 2006. Application of an electronic nose for quality assessment of modified atmosphere packaged poultry meat. Food Control 17:5–13.
  • Rambla-Alegre, M., B. Tienpont, K. Mitsui, E. Masugi, Y. Yoshimura, H. Nagata, F. David, and P. Sandra. 2014. Coupling gas chromatography and electronic nose detection for detailed cigarette smoke aroma characterization. J. Chromatogr. A 1365:191–203.
  • Shenk, J. S., I. Landa, M. R. Hoover, and M. O. Westerhaus. 1981. Description and evaluation of a near infrared reflectance spectro-computer for forage and grain analysis. Crop Sci. 21:355–358.
  • Shu, R. X., J. Y. Cai, Z. Y. Yang, Z. Y. Yang, L. L. Zhao, L. D. Zhang, Y. H. Zhang, and J. H. Li. 2014. Analysis of tobacco style features using near-infrared spectroscopy and projection model. Spectrosc. Spect. Anal. 34 (10):2764–2768.
  • Tian, K. D., K. X. Qiu, Z. H. Li, Y. Q. Lu, Q. J. Zhang, Y. M. Xiong, and S. G. Min. 2014. Determination of calcium and magnesium in tobacco by near-infrared spectroscopy and least squares-support vector machine. Spect. Anal. 34:3262–3266.
  • Vapnik, V. 1998. Statistical learning theory. New York: Wiley-Interscience.
  • Wang, D. F., X. C. Wang, T. A. Liu, and Y. Liu. 2012. Prediction of total viable counts on chilled pork using an electronic nose combined with support vector machine. Meat Sci. 90:373–7.

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