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BioMed Research International
Volume 2013 (2013), Article ID 813519, 8 pages
Pesticide Residue Screening Using a Novel Artificial Neural Network Combined with a Bioelectric Cellular Biosensor
1Laboratory of Informatics, School of Food Science, Biotechnology and Development, Agricultural University of Athens, Iera Odos 75, 11855 Athens, Greece
2Laboratory of Enzyme Technology, School of Food Science, Biotechnology and Development, Agricultural University of Athens, Iera Odos 75, 11855 Athens, Greece
Received 9 April 2013; Revised 12 June 2013; Accepted 3 July 2013
Academic Editor: Eldon R. Rene
Copyright © 2013 Konstantinos P. Ferentinos et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
- Eurostat, Agricultural Statistics: Main Results 2009-2010, Luxemburg, 2011.
- FiBL/IFOAM, Data Tables on Organic Horticulture World-wide 2006–2010: Development, crop categories, country data, Frick, 2012.
- S. Mavrikou, K. Flampouri, G. Moschopoulou, O. Mangana, A. Michaelides, and S. Kintzios, “Assessment of organophosphate and carbamate pesticide residues in cigarette tobacco with a novel cell biosensor,” Sensors, vol. 8, no. 4, pp. 2818–2832, 2008.
- K. Flampouri, S. Mavrikou, S. Kintzios, G. Miliadis, and P. Aplada-Sarlis, “Development and validation of a cellular biosensor detecting pesticide residues in tomatoes,” Talanta, vol. 80, no. 5, pp. 1799–1804, 2010.
- S. I. Gallant, Neural Network Learning and Expert Systems, The MIT Press, Cambridge, Mass, USA, 1993.
- D. E. Rumelhart, G. E. Hinton, and R. J. Williams, “Learning representations by back-propagating errors,” Nature, vol. 323, no. 6088, pp. 533–536, 1986.
- I. Seginer, T. Boulard, and B. J. Bailey, “Neural network models of the greenhouse climate,” Journal of Agricultural Engineering Research, vol. 59, no. 3, pp. 203–216, 1994.
- R. Lacroix, F. Salehi, X. Z. Yang, and K. M. Wade, “Effects of data preprocessing on the performance of artificial neural networks for dairy yield prediction and cow culling classification,” Transactions of the American Society of Agricultural Engineers, vol. 40, no. 3, pp. 839–846, 1997.
- K. P. Ferentinos and L. D. Albright, “Predictive neural network modeling of pH and electrical conductivity in deep-trough hydroponics,” Transactions of the American Society of Agricultural Engineers, vol. 45, no. 6, pp. 2007–2015, 2002.
- K. P. Ferentinos, “Biological engineering applications of feedforward neural networks designed and parameterized by genetic algorithms,” Neural Networks, vol. 18, no. 7, pp. 934–950, 2005.
- T. L. Fine, Feedforward Neural Network Methodology, Springer, New York, NY, USA, 1999.
- M. Marinovich, B. Viviani, V. Capra et al., “Facilitation of acetylcholine signaling by the dithiocarbamate fungicide propineb,” Chemical Research in Toxicology, vol. 15, no. 1, pp. 26–32, 2002.
- P. A. Davies, W. Wang, T. G. Hales, and E. F. Kirkness, “A novel class of ligand-gated ion channel is activated by Zn2+,” The Journal of Biological Chemistry, vol. 278, no. 2, pp. 712–717, 2003.
- T. Houtani, Y. Munemoto, M. Kase, S. Sakuma, T. Tsutsumi, and T. Sugimoto, “Cloning and expression of ligand-gated ion-channel receptor L2 in central nervous system,” Biochemical and Biophysical Research Communications, vol. 335, no. 2, pp. 277–285, 2005.
- T. J. Glezakos, T. A. Tsiligiridis, and C. P. Yialouris, “Piecewise evolutionary segmentation for feature extraction in time series models,” Neural Computing and Applications, 2012.
- D. Frossyniotis, Y. Anthopoulos, S. Kintzios, A. Perdikaris, and C. P. Yialouris, “A multisensor fusion system for the detection of plant viruses by combining artificial neural networks,” in Artificial Neural Networks, vol. 4132 of Lectures notes in Computer Science, pp. 401–409, 2006.
- T. J. Glezakos, G. Moschopoulou, T. A. Tsiligiridis, S. Kintzios, and C. P. Yialouris, “Plant virus identification based on neural networks with evolutionary preprocessing,” Computers and Electronics in Agriculture, vol. 70, no. 2, pp. 263–275, 2010.
- L. S. Ferreira, M. B. de Souza Jr., and R. O. M. Folly, “Development of an alcohol fermentation control system based on biosensor measurements interpreted by neural networks,” Sensors and Actuators B, vol. 75, no. 3, pp. 166–171, 2001.
- A. Gutés, F. Céspedes, S. Alegret, and M. del Valle, “Determination of phenolic compounds by a polyphenol oxidase amperometric biosensor and artificial neural network analysis,” Biosensors and Bioelectronics, vol. 20, no. 8, pp. 1668–1673, 2005.
- J. Abdullah, M. Ahmad, L. Yook Heng, N. Karuppiah, and H. Sidek, “Evaluation of an optical phenolic biosensor signal employing artificial neural networks,” Sensors and Actuators B, vol. 134, no. 2, pp. 959–965, 2008.
- J. S. Torrecilla, M. L. Mena, P. Yáñez-Sedeño, and J. García, “Field determination of phenolic compounds in olive oil mill wastewater by artificial neural network,” Biochemical Engineering Journal, vol. 38, no. 2, pp. 171–179, 2008.
- C. Ziegler, A. Harsch, and W. Göpel, “Natural neural networks for quantitative sensing of neurochemicals: an artificial neural network analysis,” Sensors and Actuators B, vol. 65, no. 1, pp. 160–162, 2000.
- T. T. Bachmann and R. D. Schmid, “A disposable multielectrode biosensor for rapid simultaneous detection of the insecticides paraoxon and carbofuran at high resolution,” Analytica Chimica Acta, vol. 401, no. 1-2, pp. 95–103, 1999.
- G. A. Alonso, G. Istamboulie, T. Noguer, J.-L. Marty, and R. Muñoz, “Rapid determination of pesticide mixtures using disposable biosensors based on genetically modified enzymes and artificial neural networks,” Sensors and Actuators B, vol. 164, no. 1, pp. 22–28, 2012.
- G. A. Alonso, G. Istamboulie, A. Ramirez-Garcia, T. Noguer, J.-L. Marty, and R. Muñoz, “Artificial neural network implementation in single low-cost chip for the detection of insecticides by modeling of screen-printed enzymatic sensors response,” Computers and Electronics in Agriculture, vol. 74, no. 2, pp. 223–229, 2010.
- G. Istamboulie, M. Cortina-Puig, J.-L. Marty, and T. Noguer, “The use of artificial neural networks for the selective detection of two organophosphate insecticides: chlorpyrifos and chlorfenvinfos,” Talanta, vol. 79, no. 2, pp. 507–511, 2009.
- B. Li, Y. He, and C. Xu, “Simultaneous determination of three organophosphorus pesticides residues in vegetables using continuous-flow chemiluminescence with artificial neural network calibration,” Talanta, vol. 72, no. 1, pp. 223–230, 2007.