Support vector machine and artificial neural network models for the classification of grapevine varieties using a portable NIR spectrophotometer

Autor: Gutiérrez S.; Tardáguila Laso, Javier; Fernández-Novales J.; Diago Santamaría, María Paz

Tipo de documento: Artículo de revista

Revista: PLoS ONE. ISSN: 1932-6203. Año: 2015. Número: 11. Volumen: 10.

doi 10.1371/journal.pone.0143197Texto completo open access 

SCIMAGO (datos correspondientes al año 2014):
1,3  SNIP: 1,034 



  • Galet, P., (1979) A Practical Ampelography, , Cornell University Press
  • Altube, H., Cabello, F., Ortiz, J.M., Caracterización de variedades y portainjertos de vid mediante isoenzimas de los sarmientos (1991) Vitis., 30 (4), pp. 203-212
  • Sefc, K.M., Lefort, F., Grando, M.S., Scott, K.D., Steinkellner, H., Thomas, M.R., Microsatellite markers for grapevine: A state of the art (2001) Molecular Biology &
  • Biotechnology of the Grapevine, pp. 433-463
  • Borrego, J., De Andrés, M.T., Gómez, J.L., Ibáñez, J., Genetic study of Malvasia and Torrontes groups through molecular markers (2002) American Journal of Enology and Viticulture., 53 (2), pp. 125-130
  • Pelsy, F., Hocquigny, S., Moncada, X., Barbeau, G., Forget, D., Hinrichsen, P., An extensive study of the genetic diversity within seven French wine grape variety collections (2010) Theoretical and Applied Genetics., 120 (6), pp. 1219-1231. , 20062965
  • Fernández-Novales, J., López, M.I., Sánchez, M.T., García-Mesa, J.A., González-Caballero, V., Assessment of quality parameters in grapes during ripening using a miniature fiber-optic near-infrared spectrometer (2009) International Journal of Food Sciences and Nutrition., 60, pp. 265-277. , 19626519
  • Pérez-Marín, D., Paz, P., Guerrero, J.E., Garrido-Varo, A., Sánchez, M.T., Miniature handheld NIR sensor for the on-site non-destructive assessment of post-harvest quality and refrigerated storage behavior in plums (2010) Journal of Food Engineering., 99 (3), pp. 294-302
  • Wang, W., Paliwal, J., Spectral data compression and analyses techniques to discriminate wheat classes (2006) Transactions of the ASABE., 49 (5), pp. 1607-1612
  • Li, X., He, Y., Fang, H., Non-destructive discrimination of Chinese bayberry varieties using Vis/NIR spectroscopy (2007) Journal of Food Engineering., 81 (2), pp. 357-363
  • Fu, X., Zhou, Y., Ying, Y., Lu, H., Xu, H., Discrimination of pear varieties using three classification methods based on near-infrared spectroscopy (2007) Transactions of the ASABE., 50 (4), pp. 1355-1361
  • Xu, H.R., Yu, P., Fu, X.P., Ying, Y.B., On-site variety discrimination of tomato plant using visible-near infrared reflectance spectroscopy (2009) Journal of Zhejiang University Science B., 10 (2), pp. 126-132. , 19235271
  • Sánchez, M.T., De La Haba, M.J., Benítez-López, M., Fernández-Novales, J., Garrido-Varo, A., Pérez-Marín, D., Non-destructive characterization and quality control of intact strawberries based on NIR spectral data (2012) Journal of Food Engineering., 110 (1), pp. 102-108
  • Diago, M.P., Fernandes, A.M., Millan, B., Tardaguila, J., Melo-Pinto, P., Identification of grapevine varieties using leaf spectroscopy and partial least squares (2013) Computers and Electronics in Agriculture., 99, pp. 7-13
  • Fernandes, A.M., Melo-Pinto, P., Millan, B., Tardaguila, J., Diago, M.P., Automatic discrimination of grapevine (Vitis vinifera L.) clones using leaf hyperspectral imaging and partial least squares (2015) The Journal of Agricultural Science., 153 (3), pp. 455-465
  • Cortes, C., Vapnik, V., Support-vector networks (1995) Machine Learning., 20 (3), pp. 273-297
  • Xie, C., Wang, Q., He, Y., Identification of different varieties of sesame oil using near-infrared hyperspectral imaging and chemometrics algorithms (2014) Plos One, 9 (5). , 24879306
  • Yang, X., Hong, H., You, Z., Cheng, F., Spectral and image integrated analysis of hyperspectral data for waxy corn seed variety classification (2015) Sensors., 15 (7), pp. 15578-15594. , 26140347
  • Kong, W., Zhang, C., Liu, F., Nie, P., He, Y., Rice seed cultivar identification using near-infrared hyperspectral imaging and multivariate data analysis (2013) Sensors., 13 (7), pp. 8916-8927. , 23857260
  • McCulloch, W.S., Pitts, W., A logical calculus of the ideas immanent in nervous activity (1943) The Bulletin of Mathematical Biophysics., 5 (4), pp. 115-133
  • Werbos, P., (1974) Beyond Regression: New Tools for Prediction and Analysis in the Behavioral Sciences, , PhD Thesis
  • Rumelhart, D.E., Hinton, G.E., Williams, R.J., Learning representations by back-propagating errors (1986) Nature., 323 (6088), pp. 533-536
  • Li, X., He, Y., Discriminating varieties of tea plant based on Vis/NIR spectral characteristics and using artificial neural networks (2008) Biosystems Engineering., 99 (3), pp. 313-321
  • Yang, C.W., Chen, S., Ouyang, F., Yang, I.C., Tsai, C.Y., A robust identification model for herbal medicine using near infrared spectroscopy and artificial neural network (2011) Journal of Food and Drug Analysis., 19 (1)
  • Barrs, H.D., Weatherley, P.E., A re-examination of the relative turgidity technique for estimating water deficits in leaves (1962) Australian Journal of Biological Sciences., 15 (3), pp. 413-428
  • Barnes, R.J., Dhanoa, M.S., Lister, S.J., Standard normal variate transformation and de-trending of near-infrared diffuse reflectance spectra (1989) Applied Spectroscopy., 43 (5), pp. 772-777
  • Dhanoa, M.S., Lister, S.J., Barnes, R.J., On the scales associated with near-infrared reflectance difference spectra (1995) Applied Spectroscopy., 49 (6), pp. 765-772
  • Savitzky, A., Golay, M.J.E., Smoothing and differentiation of data by simplified least squares procedures (1964) Analytical Chemistry., 36 (8), pp. 1627-1639
  • Platt, J., Sequential minimal optimization: A fast algorithm for training support vector machines (1998) Technical Report MSR-TR-98-14, Microsoft Research.
  • Hornik, K., Stinchcombe, M., White, H., Multilayer feedforward networks are universal approximators (1989) Neural Networks., 2 (5), pp. 359-366
  • Talens, P., Mora, L., Morsy, N., Barbin, D.F., ElMasry, G., Sun, D.W., Prediction of water and protein contents and quality classification of Spanish cooked ham using NIR hyperspectral imaging (2013) Journal of Food Engineering., 117 (3), pp. 272-280
  • Ivorra, E., Girón, J., Sánchez, A.J., Verdú, S., Barat, J.M., Grau, R., Detection of expired vacuum-packed smoked salmon based on PLS-DA method using hyperspectral images (2013) Journal of Food Engineering., 117 (3), pp. 342-349
  • Canaza-Cayo, A.W., Cozzolino, D., Alomar, D., Quispe, E., A feasibility study of the classification of Alpaca (Lama pacos) wool samples from different ages, sex and color by means of visible and near infrared reflectance spectroscopy (2012) Computers and Electronics in Agriculture., 88, pp. 141-147
  • Vanloot, P., Bertrand, D., Pinatel, C., Artaud, J., Dupuy, N., Artificial vision and chemometrics analyses of olive stones for varietal identification of five French cultivars (2014) Computers and Electronics in Agriculture., 102, pp. 98-105
  • Stevens, A., Ramirez-Lopez, L., An introduction to the prospectr package (2013) R Package Version 0.1.3
  • Borchers, H.W., Pracma: Practical numerical math functions (2015) R Package Version 1.8.3
  • Hall, M., Frank, E., Holmes, G., Pfahringer, B., Reutemann, P., Witten, I.H., The WEKA data mining software: An update (2009) SIGKDD Explorations., 11 (1), pp. 10-18
  • Jacquemoud, S., Ustin, S.L., Leaf optical properties: A state of the art (2001) 8th International Symposium of Physical Measurements &
  • Signatures in Remote Sensing, pp. 223-332
  • Fernández-Cabanás, V.M., Garrido-Varo, A., Pérez-Marín, D., Dardenne, P., Evaluation of pretreatment strategies for near-infrared spectroscopy calibration development of unground and ground compound feedingstuffs (2006) Applied Spectroscopy., 60 (1), pp. 17-23. , 16454905
  • Delwiche, S.R., Reeves, J.B., The effect of spectral pre-treatments on the partial least squares modelling of agricultural products (2004) Journal of Near Infrared Spectroscopy., 12, pp. 177-182
  • Sultan, S.E., Phenotypic plasticity for plant development, function and life history (2000) Trends in Plant Science., 5 (12), pp. 537-542. , 11120476
  • Nicotra, A.B., Atkin, O.K., Bonser, S.P., Davidson, A.M., Finnegan, E.J., Mathesius, U., Plant phenotypic plasticity in a changing climate (2010) Trends in Plant Science., 15 (12), pp. 684-692. , 20970368
  • Pélabon, Osler, N.C., Diekmann, M., Graae, B.J., Decoupled phenotypic variation between floral and vegetative traits: Distinguishing between developmental and environmental correlations (2013) Annals of Botany., 111 (5), pp. 935-944. , 23471008