Geographical classification of Nanfeng mandarin by near infrared spectroscopy coupled with chemometrics methods

Xuan Zhang; Yiping Du; Peijin Tong; Yuanlong Wei; Man Wang

doi:10.1142/s179354581450028x

[1] S. Tsuchikawa, "Near-Infrared Spectroscopy in Food Science and Technology," Y. Ozaki, W. F. McClure, A. A. Christy, Eds., pp. 138–139, John Wiley & Sons, Inc, Hoboken, New Jersey, U.S. (2006).

[2] D. A. Burns, E. W. Ciurczak, "Handbook of Near-Infrared Analysis," Taylor & Francis Group, Boca Raton, U.S. (2008).

[3] T. Caliński, J. Harabasz, "A dendrite method for cluster analysis," Commun. Stat.-Theor. M 3(1), 1–27 (1974).

[4] S. Wold, K. Esbensen, P. Geladi, "Principal component analysis," Chemometr. Intell. Lab. 2(1–3), 37–52 (1987).

[5] K.-L. Hsu, H. V. Gupta, S. Sorooshian, "Artificial neural network modeling of the rainfall-runoff process," Water Resour. Res. 31(10), 2517–2530 (1995).

[6] I. Estebandiez, "An evaluation of orthogonal signal correction methods for the characterisation of arabica and robusta coffee varieties by NIRS," Anal. Chim. Acta. 514(1), 57–67 (2004).

[7] I. Esteban-Diez, J. M. Gonzalez-Saiz, C. Saenz- Gonzalez, C. Pizarro, "Coffee varietal differentiation based on near infrared spectroscopy," Talanta 71 (1), 221–229 (2007).

[8] D. Wu, Y. He, Y. Bao, "Intelligent Computing," pp. 931–936, Springer (2006).

[9] Y. He, X. Li, X. Deng, "Discrimination of varieties of tea using near infrared spectroscopy by principal component analysis and BP model," J. Food Eng. 79(4), 1238–1242 (2007).

[10] Y. Shao, Y. He, Y. Wang, "A new approach to discriminate varieties of tobacco using vis/near infrared spectra," Eur. Food Res. Technol. 224(5), 591–596 (2007).

[11] T. Woodcock, G. Downey, J. D. Kelly, C. O'Donnell, "Geographical classification of honey samples by near-infrared spectroscopy: A feasibility study," J. Agric. Food. Chem. 55(22), 9128–9134 (2007).

[12] G. Downey, P. McIntyre, A. N. Davies, "Geographic classification of extra virgin olive oils from the eastern Mediterranean by chemometric analysis of visible and near-infrared spectroscopic data," Appl. Spectosc. 57(2), 158–163 (2003).

[13] G. Gurdeniz, B. Ozen, "Detection of adulteration of extra-virgin olive oil by chemometric analysis of mid-infrared spectral data," Food Chem. 116(2), 519–525 (2009).

[14] L. Liu, D. Cozzolino, W. Cynkar, M. Gishen, C. B. Colby, "Geographic classification of Spanish and Australian Tempranillo red wines by visible and near-infrared spectroscopy combined with multivariate analysis," J. Agric. Food Chem. 54(18), 6754–6759 (2006).

[15] L. Pillonel, W. Luginbühl, D. Picque, E. Schaller, R. Tabacchi, J. Bosset, "Analytical methods for the determination of the geographic origin of Emmental cheese: Mid and near-infrared spectroscopy," Eur. Food Res. Technol. 216(2), 174–178 (2003).

[16] L. León, J. D. Kelly, G. Downey, "Detection of apple juice adulteration using near-infrared trans- flectance spectroscopy," Appl. Spectosc. 59(5), 593– 599 (2005).

[17] Y. L. Wei, C. H. Yin, G. P. Chen, J. Huang, W. B. Zhang, Y. P. Du, "Identification of Nanfeng Mandarin from different origins by using near infrared spectroscopy coupled with principal components analysis," Spectrosc. Spect. Anal. 33(11), 3024– 3027 (2013).

[18] Y. Du, Y. Liang, J. Jiang, R. Berry, Y. Ozaki, "Spectral regions selection to improve prediction ability of PLS models by changeable size moving window partial least squares and searching combination moving window partial least squares," Anal. Chim. Acta 501(2), 183–191 (2004).

[19] T. Isaksson, T. N s, "The effect of multiplicative scatter correction (MSC) and linearity improvement in NIR Spectroscopy," Appl. Spectosc. 42(7), 1273– 1284 (1988).

[20] I. A. Cowe, J. W. McNicol, D. C. Cuthbertson, "A designed experiment for the examination of techniques used in the analysis of near infrared spectra. Part 1 Analysis of spectral structure," Analyst 110 (10), 1227–1232 (1985).

[21] R. Barnes, M. Dhanoa, S. J. Lister, "Standard normal variate transformation and de-trending of nearinfrared diffuse reflectance spectra," Appl. Spectosc. 43(5), 772–777 (1989).

[22] A. Savitzky, M. J. Golay, "Smoothing and differentiation of data by simplified least squares procedures," Anal. Chem. 36(8), 1627–1639 (1964).

[23] D. Hand, "Discrimination and classification," John Wiley & Sons, Toronto (1981).

[24] H. Abdi, L. J. Williams, "Principal component analysis," WIREs Comput. Stat. 2(4), 433–459 (2010).

[25] C. M. Andersen, R. Bro, "Variable selection in regression— a tutorial," J. Chemometr. 24(11–12), 728–737 (2010).

[26] C. H. Spiegelman, M. J. McShane, M. J. Goetz, M. Motamedi, Q. L. Yue, G. L. Cote, "Theoretical justification of wavelength selection in PLS calibration: Development of a new algorithm," Anal. Chem. 70(1), 35–44 (1998).

[27] A. S. Bangalore, R. E. Shaffer, G. W. Small, M. A. Arnold, "Genetic algorithm-based method for selecting wavelengths and model size for use with partial least-squares regression: Application to nearinfrared spectroscopy," Anal. Chem. 68(23), 4200– 4212 (1996).

[28] L. Norgaard, A. Saudland, J. Wagner, J. P. Nielsen, L. Munck, S. Engelsen, "Interval partial leastsquares regression (iPLS): A comparative chemometric study with an example from near-infrared spectroscopy," Appl. Spectosc. 54(3), 413–419 (2000).

[29] W. Cai, Y. Li, X. Shao, "A variable selection method based on uninformative variable elimination for multivariate calibration of near-infrared spectra," Chemometr. Intell. Lab. 90(2), 188–194 (2008).

[30] H. Li, Y. Liang, Q. Xu, D. Cao, "Key wavelengths screening using competitive adaptive reweighted sampling method for multivariate calibration," Anal. Chim. Acta. 648(1), 77–84 (2009).

[31] K. Zheng, Q. Li, J. Wang, J. Geng, P. Cao, T. Sui, X. Wang, Y. Du, "Stability competitive adaptive reweighted sampling (SCARS) and its applications to multivariate calibration of NIR spectra," Chemometr. Intell. Lab. 112(0), 48–54 (2012).

[32] H. Xu, Z. Liu, W. Cai, X. Shao, "A wavelength selection method based on randomization test for near-infrared spectral analysis," Chemometr. Intell. Lab. 97(2), 189–193 (2009).