Similar articles

Abstract: The use of visible-near infrared (NIR) spectroscopy was explored as a tool to discriminate two new tomato plant varieties in China (Zheza205 and Zheza207). In this study, 82 top-canopy leaves of Zheza205 and 86 top-canopy leaves of Zheza207 were measured in visible-NIR reflectance mode. Discriminant models were developed using principal component analysis (PCA), discriminant analysis (DA), and discriminant partial least squares (DPLS) regression methods. After outliers detection, the samples were randomly split into two sets, one used as a calibration set (n=82) and the remaining samples as a validation set (n=82). When predicting the variety of the samples in validation set, the classification correctness of the DPLS model after optimizing spectral pretreatment was up to 93%. The DPLS model with raw spectra after multiplicative scatter correction and Savitzky-Golay filter smoothing pretreatments had the best satisfactory calibration and prediction abilities (correlation coefficient of calibration (R_c)=0.920, root mean square errors of calibration=0.196, and root mean square errors of prediction=0.216). The results show that visible-NIR spectroscopy might be a suitable alternative tool to discriminate tomato plant varieties on-site.

[1] Alsberg, B.K., Kell, D.B., Goodacre, R., 1998. Variable selection in discriminant partial least-squares analysis. Anal. Chem., 70(19):4126-4133.

[2] Ancillo, G., Gadea, J., Forment, J., Guerri, J., Navarro, L., 2007. Class prediction of closely related plant varieties using gene expression profiling. J. Exp. Bot., 58(8): 1927-1933.

[3] Borregaard, T., Nielsen, H., Nørgaard, L., Have, H., 2000. Crop-weed discrimination by line imaging spectroscopy. J. Agric. Eng. Res., 75(4):389-400.

[4] Cervantes-Martínez, J., Flores-Hernández, R., Rodríguez-Garay, B., Santacruz-Ruvalcaba, F., 2002. Detection of bacterial infection of agave plants by laser-induced fluorescence. Appl. Optics., 41(13):2541-2545.

[5] Chen, L.M., Carpita, N.C., Reiter, W.D., Wilson, R.H., Jeffries, C., McCann, M.C., 1998. A rapid method to screen for cell-wall mutants using discriminant analysis of Fourier transform infrared spectra. Plant J., 16(3):385-392.

[6] Chen, Q.S., Zhao, J.W., Fang, C.H., Wang, D.M., 2007. Feasibility study on identification of green, black and oolong teas using near infrared reflectance spectroscopy based on support vector machine. Spectrochim. Acta A, 66(3): 568-574.

[7] Cooke, R.J., 1995a. Gel electrophoresis for the identification of plant varieties. J. Chromatogr. A, 698(1-2):281-299.

[8] Cooke, R.J., 1995b. Introduction: The Reasons for Variety Identification. In: Wrigley, C.W. (Ed.), Identification of Food Grain Varieties. AACC, USA, p.1-18.

[9] Cooke, R.J., 1999. New approaches to potato variety identification. Potato Res., 42(3-4):529-539.

[10] Cozzolino, D., Murray, I., 2004. Identification of animal meat muscles by visible and near infrared reflectance spectroscopy. Lebensm.-Wiss. u.-Technol., 37(4):447-452.

[11] Cozzolino, D., Smyth, H.E., Gishen, M., 2003. Feasibility study on the use of visible and near infrared spectroscopy together with chemometrics to discriminate between commercial white wines of different varietal origins. J. Agric. Food Chem., 51(26):7703-7708.

[12] de Riek, J., Everaert, I., Esselink, D., Calsyn, E., Smulders, M.J.M., Vosman, B., 2007. Assignment tests for variety identification compared to genetic similarity-based methods using experimental datasets from different marker systems in sugar beet. Crop. Sci., 47(5): 1964-1974.

[13] Fu, X.P., Zhou, Y., Ying, Y.B., Lu, H.S., Xu, H.R., 2007. Discrimination of pear varieties using three classification methods based on near infrared spectroscopy. Trans. ASABE, 50(4):1355-1361.

[14] Holmes, M.G., Keiller, D.R., 2002. Effects of pubescence and waxes on the reflectance of leaves in the ultraviolet and photosynthetic wavebands: a comparison of a range of species. Plant Cell Environ., 25(1):85-93.

[15] Jacquemoud, S., Ustin, S.L., 2001. Leaf Optical Properties: A State of the Art. Proceedings of the 8th International Symposium on Physical Measurements and Signatures in Remote Sensing. Aussois, France, p.223-232.

[16] Kim, J., Mowat, A., Poole, P., Kasabov, N., 2000. Linear and non-linear pattern recognition models for classification of fruit from visible-near infrared spectra. Chemometr. Intell. Lab. Syst., 51(2):201-216.

[17] Lee, D., Reeves, J.C., Cooke, R.J., 2005. DNA profiling and plant variety registration: 1. the use of random amplified DNA polymorphisms to discriminate between varieties of oilseed rape. Electrophoresis, 17(1):261-265.

[18] Lee, K.H., Zhang, N.Q., Das, S., 2003. Comparing Adaptive Neuro-fuzzy Inference System (ANFIS) to Partial Least-squares (PLS) Method for Simultaneous Prediction of Multiple Soil Properties. ASAE Paper No. 033144. Las Vegas, Nevada, USA.

[19] Mouazen, A.M., Karoui, R., Baerdemaeker, J.D., Ramon, H., 2006. Classification of Soils into Different Moisture Content Levels Based on VIS-NIR Spectra. ASABE Paper No. 061067. Portland, USA.

[20] Noble, S.D., Crowe, T.G., 2001. Plant Discrimination Based on Leaf Reflectance. ASABE Paper No. 011150. Sacramento, California, USA.

[21] Perez, D.P., Sanchez, M.T., Cano, G., Garrido, A., 2001. Authentication of green asparagus varieties by near infrared reflectance spectroscopy. J. Food Sci., 66(2): 323-327.

[22] Riedell, W.E., Blackmer, T.M., 1999. Leaf reflectance spectra of cereal aphid-damaged wheat. Crop Sci., 39(6):1835-1840.

[23] Steuer, B., Schulz, H., Lager, E., 2001. Classification and analysis of citrus oils by NIR spectroscopy. Food Chem., 72(1):113-117.

[24] Vogelmann, T.C., 1993. Plant tissue optics. Annu. Rev. Plant Physiol. Plant Mol. Biol., 44(1):231-251.

[25] Wang, C.Y., Chen, C.T., Chiang, C.P., Young, S.T., Chow, S.N., Chiang, H.K., 1998. Partial least-squares discriminant analysis on autofluorescence spectra of oral carcinogenesis. Appl. Spectrosc., 52(9):1190-1195.

[26] Wang, L., Frank Lee, S.C., Wang, X.R., He, Y., 2006. Feasibility study of quantifying and discriminating soybean oil adulteration in camellia oils by attenuated total reflectance MIR and fiber optic diffuse reflectance NIR. Food Chem., 95(3):529-536.

[27] Wang, W., Paliwal, J., 2006. Spectral data compression and analyses techniques to discriminate wheat classes. Trans. ASABE, 49(5):1607-1612.

[28] Workman, J., 2001. NIR Spectroscopy Calibration Basics. In: Burns, D.A., Ciurczak, E.W. (Eds.), Handbook of near Infrared Analysis, 2nd Ed. Marcfl Dfkker, Inc., New York, p.90-129.

[29] Xie, L.J., Ying, Y.B., Ying, T.J., 2007a. Combination and comparison of chemometrics methods for identification of transgenic tomatoes using visible and near-infrared diffuse transmittance technique. J. Food Eng., 82(3): 395-401.

[30] Xie, L.J., Ying, Y.B., Ying, T.J., Yu, H.Y., 2007b. Discrimination of transgenic tomatoes based on visible/ near-infrared spectra. Anal. Chim. Acta, 584(2):379-384.

[31] Xie, L.J., Ying, Y.B., Ying, T.J., 2007c. Quantification of chlorophyll content and classification of nontransgenic and transgenic tomato leaves using visible/near-infrared diffuse reflectance spectroscopy. J. Agric. Food Chem., 55(12):4645-4650.

[32] Xu, H.R., Ying, Y.B., Fu, X.P., Zhu, S.P., 2007. Near infrared spectroscopy in detecting leaf miner damage on tomato leaf. Biosyst. Eng., 96(4):447-454.

[33] Yee, N., Bussell, W.T., Coghill, G.G., 2006. Use of near infrared spectra to identify cultivar in potato (Solanum tuberosum) crisps. New Zeal J. Crop Hort., 34(2):177-181.