Geographical classification of Chilean wines by an electronic nose

References

• Eschnauer H, Neeb R. Microelement analysis in wine and grapes. In: Linskens HF, Jackson JF, editors. Wine Analysis. Berlin: Springer-Verlag; 1988.
   Google Scholar
• Eschnauer H, Jakob L, Melerer H, Neeb R. Use and limitations of ICP-OES in wine analysis. Mikrochimica Acta. 1989;3:291–298.
   Google Scholar
• Estévez-Pérez D. Caracterización y tipificación de los vinos de las Islas Canarias con denominación de Origen por su contenido en iones metálicos. Doctoral thesis. Tenerife: Universidad de La Laguna; 2002.
   Google Scholar
• Fournier JB. Contribution à L’étude de L’évolution des teneurs en éléments traces dans les produits de la vigne durant le cycle reproductif et la vinification. Nantes: Thèse de L’Université de Nantes, Chimie Biologie-Sciences Agroalimentaires; 1998.
   Google Scholar
• Gonzales-Larraina M, Gonzales A, Medina B. Les ions métalliques dans la différenciation des vins rouges, des trois régions d’appellation d’origine Rioja. Connaissance Vigne Vin. 1987;21(2):126–140.
   Google Scholar
• Greenough JD, Longerich HP, Jackson SE. Element fingerprint of Okanagan Valley wines using ICP-MS. Relationships between wine composition, vineyard and wine colour. Aust J Grape Wine Res. 1997; 3:75–83.
   Google Scholar
• Smolle A. Terroir: A study of the relationship between the vine and its environment. Berkeley, CA: Class notes of MCB 14 Enology Course; University of California, Berkeley; 1993.
   Google Scholar
• Kallithraka S, Arvanitoyannis IS, Kefalas P, Eip-Zajouli A, Soufleros E, Psarra E. Instrumental and sensory analysis of Greek wines; implementation of principal component analysis (PCA) for classification according to geographical origin. Food Chem. 2001;73:501–514.
   Web of Science ®Google Scholar
• Kwan WO, Kowalski BR, Skogerboe RK. Pattern recognition analysis of elemental data. Wines of Vitis vinifera cv. Pinot Noir from France and the United States. J Agric Food Chem. 1979;27(6): 1321–1326.
   Web of Science ®Google Scholar
• Latorre MJ, Herrero C, Medina B. Utilisation de quelques éléments minéraux dans la différenciation des vins de Galice. Journal  International Sciences Vigne Vin. 1992;26(3):185–193.
   Google Scholar
• Latorre MJ, Garcia-Jares C, Medina B, Herrero C. Pattern recognition analysis applied to classification of wines from Galicia (Northwestern Spain) with certified brand of origin. J Agric Food Chem. 1994;42: 1451–1455.
   Web of Science ®Google Scholar
• Maarse H, Slump AC, Schaefer J. Classification of wines according to type and region based on their composition. Z Lebensm Unters Forsch. 1987;184:198–203.
   Google Scholar
• Martin GJ, Mazure M, Jouitteau C, Martin YL, Aguile L, Allain P. Characterization of the geographic origin of Bordeaux wines by a combined use of isotopic and trace element measurements.  Am J Enol  Vitic. 1999;50(4):409–417.
   Web of Science ®Google Scholar
• Lozano J, Santos JP, Horrillo MC. Enrichment sampling methods for wine discrimination with gas sensors. J Food Compost Anal. 2008;21(8):716–723.
   Web of Science ®Google Scholar
• García M, Aleixandre M, Gutiérrez J, Horrillo MC. Electronic nose for wine discrimination. Sens Actuators B Chem. 2006;113(2): 911–916.
   Web of Science ®Google Scholar
• Lozano J, Arroyo T, Santos JP, Cabellos JM, Horrillo MC. Electronic nose for wine ageing detection. Sens Actuators B Chem. 2008;133(1):180–186.
   Web of Science ®Google Scholar
• Aleixandre M, Lozano J, Gutiérrez J, Sayago I, Fernández MJ, Horrillo MC. Portable e-nose to classify different kinds of wine. Sens  Actuators B Chem. 2008;131(1):71–76.
   Web of Science ®Google Scholar
• Fernández MJ, Fontecha JL, Sayago I, et al. Discrimination of volatile compounds through an electronic nose based on ZnO SAW sensors. Sens Actuators B Chem. 2007;127(1):277–283.
   Web of Science ®Google Scholar
• Beltrán NH, Duarte-Mermoud MA, Soto VA, Salah SA, Bustos MA. Chilean wines classification using volatile organic compounds data obtained with a fast GC analyzer. IEEE Transactions on Instrumentation  and Measurement. 2008;57(11):2421–2436.
   Web of Science ®Google Scholar
• Electronic Sensor Technology. 7100 Fast GC analyzer: Operation  manual. Newbury Park, CA: Electronic Sensor Technology; 1999.
   Google Scholar
• Shannon C. Communication in the presence of noise. Proc Institute of  Radio Engineers. 1949;37(1):10–21.
   Google Scholar
• Middleton RH, Goodwin GC. Digital control and estimation. A unified  approach. Upper Saddle River, NJ: Prentice Hall International; 1990.
   Google Scholar
• Fleiss JL. Statistical methods for rates and proportions. 2nd Ed. New York, NY: John Wiley; 1981.
   Google Scholar
• Fukunaga K. Introduction to statistical pattern recognition. 2nd Ed. New York, NY: Academic Press; 1990.
   Google Scholar
• Jollife LT. Principal component analysis: A beginner’s guide. New York, NY: Springer-Verlag; 1990.
   Google Scholar
• Theodoridis S, Koutroumbas K. Pattern recognition. New York, NY: Academic Press; 1999.
   Google Scholar
• Antonini M, Barlaud M, Mathieu P, Daubechies I. Image coding using wavelet transform. IEEE Trans Image Process. 1992;1(2):205–220.
   PubMed Web of Science ®Google Scholar
• Beltrán NH, Duarte-Mermoud MA, Bustos MA, et al. Feature extraction and classification of Chilean wines. J Food Eng. 2006;75(1):1–10.
   Web of Science ®Google Scholar
• Hastie T, Tibshirani R, Friedman J. The Elements of Statistical Learning,  Data Mining, Interference and Prediction. New York, NY: Springer; 2001.
   Google Scholar
• Vapnik V. Statistical Learning Theory. New York, NY: John Wiley and Sons Inc; 1998.
   Google Scholar
• Cristianini N, Shawe-Taylor J. An introduction to support vector machines and other kernel-based learning methods. Cambridge, MA: Cambridge University Press; 2000.
   Google Scholar
• Angulo C. Aprendizaje con máquinas de núcleo en entornos de multiclasificación. Tesis Doctoral, Doctor en Ciencias, especialidad Matemáticas. Departament d’Enginyeria de Sisteme, Automàtica i Informàtica Industrial, Universitat Politècnica de Catalunya; 2001.
   Google Scholar
• Hastie T, Tibshirani R. Classification by pairwise coupling. Ann Stat. 1998;26(2):451–471.
   Web of Science ®Google Scholar
• Dietterich T, Bakiri G. Solving multiclass learning problems via error-correcting output codes. Journal of Artificial Intelligence  Research. 1995;2:263–286.
   Web of Science ®Google Scholar
• Allwein E, Shapire R, Singer Y. Reducing multiclass to binary: A unifying approach for margin classifiers. Proceedings of the Seventeenth International Conference on Machine Learning. 2000:9–16.
   Google Scholar