Classification of geographic origin of rice by data mining and inductively coupled plasma mass spectrometry

Computers and Electronics in Agriculture

Volumn 121, Issue , 2016, Pages 101-107

  Maione, Camila ^a   Batista, Bruno Lemos ^b   Campiglia, Andres Dobal ^c   Barbosa, Fernando ^d   Barbosa, Rommel Melgaço ^a  

^a FEDERAL UNIVERSITY OF GOIÁS   (Brazil)

^b UNIVERSIDADE FEDERAL DO ABC   (Brazil)

^c UNIVERSITY OF CENTRAL FLORIDA   (United States)

^d UNIVERSITY OF SÃO PAULO   (Brazil)

Author keywords

Classification; Data mining; F score; ICP MS; Rice; Support vector machines

Indexed keywords

CEREAL PRODUCTS; CLASSIFICATION (OF INFORMATION); DECISION TREES; FORECASTING; INDUCTIVELY COUPLED PLASMA MASS SPECTROMETRY; MASS SPECTROMETRY; RUBIDIUM; SUPPORT VECTOR MACHINES;

10-FOLD CROSS-VALIDATION; CLASSIFICATION MODELS; CLASSIFICATION TECHNIQUE; F-SCORE; GEOGRAPHICAL ORIGINS; ICP-MS; RELEVANT COMPONENTS; RICE;

DATA MINING;

AGRICULTURAL TECHNOLOGY; CLASSIFICATION; DATA MINING; MASS SPECTROMETRY; MODEL VALIDATION; NUMERICAL METHOD; PHYTOCHEMISTRY; PREDICTION; RICE; SPATIAL ANALYSIS; SUPPORT VECTOR MACHINE;

BRAZIL; GOIAS; RIO GRANDE DO SUL;

EID: 84951863017     PISSN: 01681699     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.compag.2015.11.009     Document Type: Article

References (34)

1. AL-Bakary A.M., Ali S.H. Genetic programming data construction method to handle data scarcity problem. IJACT Int. J. Adv. Comput. Technol. 2010.
2. Barbosa R.M., Batista B.L., Barião C.V., Varrique R.M., Coelho V.A., Campiglia A.D., Barbosa F. A simple and practical control of the authenticity of organic sugarcane samples based on the use of machine-learning algorithms and trace elements determination by inductively coupled plasma mass spectrometry. Food Chem. 2015, 184:154-159.
3. Barbosa R.M., Nacano L.R., Freitas R., Batista B.L., Barbosa F. The use of decision trees and naïve bayes algorithms and trace element patterns for controlling the authenticity of free-range-pastured hens' eggs. J. Food Sci. 2014, 79:C1672-C1677.
4. Batista B., Souza V.d.O., Silva F.d., Barbosa F.J. Survey of 13 trace elements of toxic and nutritional significance in rice from brazil and exposure assessment. Food Additives Contam.: Part B 2010, 3:253-262.
5. Bingham F., Page A., Mahler R., Ganje T. Cadmium availability to rice in sludge-amended soil under "flood" and "nonflood" culture. Soil Sci. Soc. Am. J. 1976, 50:715-719.
6. Breiman L. Random forests. Machine Learning 2001, 45:5-32.
7. Burges C.J.C. A tutorial on support vector machines for pattern recognition. Data Mining Knowl. Discovery 1998, 2:121-167.
8. Cheajesadagul P., Arnaudguilhem C., Shiowatana J., Siripinyanond A., Szpunar J. Discrimination of geographical origin of rice based on multi-element fingerprinting by high resolution inductively coupled plasma mass spectrometry. Food Chem. 2013, 141:3504-3509.
9. Chen Y.-W., Lin C.-J. Combining svms with various feature selection strategies. Feature Extraction 2006, vol. 207:315-324. Springer, Berlin Heidelberg.
10. Cortes C., Vapnik V. Support-vector networks. Machine Learning 1995, 20:273-297.
11. Cutler A., Cutler D.R., Stevens J.R. Random forests. Ensemble Machine Learning 2012, 157-175. Springer, US. C. Zhang, Y. Ma (Eds.).
12. Dash M., Liu H. Feature selection for classification. Intell. Data Anal. 1997, 1:131-156.
13. Duda R.O., Hart P.E., Stork D.G. Pattern Classification 2000, Wiley-Interscience. second ed.
14. Embrapa, 2005. Sistema de cultivo de arroz irrigado no brasil.http://sistemasdeproducao.cnptia.embrapa.br/FontesHTML/Arroz/ArrozIrrigadoBrasil/index.htm.
15. Fawcett T. An introduction to roc analysis. Pattern Recogn. Lett. 2006, 27.
16. Gardner M., Dorling S. Artificial neural networks (the multilayer perceptron)-a review of applications in the atmospheric sciences. Atmos. Environ. 1998, 32:2627-2636.
17. Guyon I., Elisseeff A. An introduction to variable and feature selection. J. Machine Learning Res. 2003, 3:1157-1182.
18. Kohavi, R., 1995. A study of cross-validation and bootstrap for accuracy estimation and model selection. In: Proceedings of the 14th International Joint Conference on Artificial Intelligence - vol. 2 (IJCAI'95), pp. 1137-1143.
19. Kuhn, M., 2015. caret: Classification and Regression Training. r package version 6.0-47. http://CRAN.R-project.org/package=caret.
20. Liaw A., Wiener M. Classification and regression by randomforest. R News 2002, 2:18-22.
21. Marini F., Zupan J., Magrí A.L. On the use of counterpropagation artificial neural networks to characterize italian rice varieties. Anal. Chim. Acta 2004, 510:231-240.
22. Maroco J., Silva D., Rodrigues A., Guerreiro M., Santana I., de Mendonça A. Data mining methods in the prediction of dementia: a real-data comparison of the accuracy, sensitivity and specificity of linear discriminant analysis, logistic regression, neural networks, support vector machines, classification trees and random forests. BMC Res. Notes 2011, 4.
23. Miranda, S.H.G., Silva, G.S., Motta, M.A.S.B., Espósito, H., 2007. Osistema agroindustrial do arroz no rio grande do sul. In: XLV Congresso da Sociedade Brasileira de Economia, Agricultura e Sociologia Rural.
24. R Core Team, 2014. R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing Vienna, Austria. http://www.R-project.org/.
25. Saruta K., Hirai Y., Tanaka K., Inoue E., Okayasu T., Mitsuoka M. Predictive models for yield and protein content of brown rice using support vector machine. Comput. Electron. Agric. 2013, 99:93-100.
26. Sáez J.A., Luengo J., Stefanowski J., Herrera F. Smote-ipf: addressing the noisy and borderline examples problem in imbalanced classification by a re-sampling method with filtering. Inform. Sci. 2015, 291:184-203.
27. Sing T., Sander O., Beerenwinkel N., Lengauer T. Rocr: visualizing classifier performance in r. Bioinformatics 2005, 21:78-81.
28. Sun L.-X., Danzer K., Thiel G. Classification of wine samples by means of artificial neural networks and discrimination analytical methods. Fresenius' J. Anal. Chem. 1997, 359(2):143-149.
29. Suzuki Y., Chikaraishi Y., Ogawa N.O., Ohkouchi N., Korenaga T. Geographical origin of polished rice based on multiple element and stable isotope analyses. Food Chem. 2008, 109:470-475.
30. Tan P.-N., Steinbach M., Kumar V. Introduction to Data Mining 2005, Addison-Wesley Longman Publishing Co., Inc., Boston, MA, USA. first ed.
31. Tong S., Koller D. Support vector machine active learning with applications to text classification. J. Machine Learning Res. 2002, 2:45-66.
32. Varma S., Simon R. Bias in error estimation when using cross-validation for model selection. BMC Bioinform. 2006, 7.
33. Vert J.-P., Tsuda K., Schölkopf B. A primer on kernel methods. Kernel Methods Comput. Biol. 2004, 35-70.
34. Zhang G. Neural networks for classification: a survey. IEEE Trans. Syst. Man Cybernet. Part C: Appl. Rev. 2000, 30:451-462.