Advanced Modeling of Food Convective Drying: A Comparison Between Artificial Neural Networks and Hybrid Approaches

Food and Bioprocess Technology

Volumn 5, Issue 5, 2012, Pages 1694-1705

  Saraceno, Alessandra ^a   Aversa, Maria ^a   Curcio, Stefano ^a  

^a UNIVERSITY OF CALABRIA   (Italy)

Author keywords

Computational tools; Models formulation; Vegetables drying

Indexed keywords

ACCURATE PREDICTION; ADVANCED MODELING; AVERAGE RELATIVE ERROR; COMPUTATIONAL TOOLS; CONVECTIVE DRYING; DRYING PROCESS; FITTING PROCEDURE; FOOD DRYING; HYBRID APPROACH; MODELING TECHNIQUE; NEURAL MODELS; NEURAL NETWORK MODEL; OPERATING CONDITION; POOR PERFORMANCE; SYSTEM BEHAVIORS; THIN LAYERS; THIN-LAYER MODELS;

FOOD PRESERVATIVES; FOOD PROCESSING; FORECASTING; HEAT CONVECTION; NEURAL NETWORKS; OPTIMIZATION; VEGETABLES;

DRYING;

EID: 84862148132     PISSN: 19355130     EISSN: 19355149     Source Type: Journal    
DOI: 10.1007/s11947-010-0477-3     Document Type: Article

References (51)

1. Agarwal, M. (1997). Combining neural and conventional paradigms for modelling, prediction and control. International Journal of Systems Science, 28, 65-81.
2. Akpinar, E. K., Bicer, Y., & Yildiz, C. (2003). Thin layer drying of red pepper. Journal of Food Engineering, 59, 99-104.
3. Arrieche, L. S., & Sartori, D. J. M. (2008). Fluid flow effect and mechanical interactions during drying of a deformable food model. Drying Technology, 26, 54-63.
4. Bird, R. B., Stewart, W., & Lightfoot, E. N. (1979). Fenomeni di trasporto. Milano: Ambrosiana.
5. Bon, J., Rossello, C., Femenia, A., Eim, V., & Simal, S. (2007). Mathematical modeling of drying kinetics for apricots: Influence of the external resistance to mass transfer. Drying Technology, 25, 1829-1835.
6. Boyaci, I. H., Sumnu, G., & Sakiyan, O. (2009). Estimation of dielectric properties of cakes based on porosity, moisture content, and formulations using statistical methods and artificial neural networks. Food and Bioprocess Technology, 2, 353-360.
7. Chin, S. K., Law, C. L., Supramaniam, C. V., & Cheng, P. G. (2009). Thin-layer drying characteristics and quality evaluation of air-dried Ganoderma tsugae Murrill. Drying Technology, 27, 975-984.
8. Curcio, S. (2010). A multiphase model to analyze transport phenomena in food drying processes. Drying Technology, 28(6), 773-785.
9. Curcio, S., Scilingo, G., Calabrò, V., & Iorio, G. (2005). Ultrafiltration of BSA in pulsating conditions: An artificial neural networks approach. Journal of Membrane Science, 246(2), 235-247.
10. Curcio, S., Calabrò, V., & Iorio, G. (2006). Reduction and control of flux decline in cross-flow membrane processes modeled by artificial neural networks. Journal of Membrane Science, 286(1-2), 125-132.
11. Curcio, S., Aversa, M., Calabro', V., & Iorio, G. (2008). Simulation of food drying: FEM analysis and experimental validation. Journal of Food Engineering, 87, 541-553.
12. Curcio, S., Calabrò, V., & Iorio, G. (2009). Reduction and control of flux decline in cross-flow membrane processes modeled by artificial neural networks and hybrid systems. Desalination, 236(1-3), 234-243.
13. Curcio, S., Aversa, M., & Saraceno, A. (2010). Advanced modeling of food convective drying: A comparative study among fundamental, artificial neural networks and hybrid approaches. In B. C. Siegler (Ed.), Food Engineering. New York: Nova Publishers.
14. Datta, A. K. (2007a). Porous media approaches to studying simultaneous heat and mass transfer in food processes. I: Problem formulations. Journal of Food Engineering, 80, 80-95.
15. Datta, A. K. (2007b). Porous media approaches to studying simultaneous heat and mass transfer in food processes. II: Property data representative results. Journal of Food Engineering, 80, 96-110.
16. Dehghani, A. A., Mohammadi, Z. B., Maghsoudlou, Y., & Mahoonak, A. S. (2009). Intelligent estimation of the canola oil stability using artificial neural network. Food and Bioprocess Technology doi: 10. 1007/s11947-009-0314-8.
17. Demuth, H., & Beale, M. (2000). Neural Network Toolbox User's Guide. Natick: The MathWorks.
18. Doymaz, İ. (2004). Convective air drying characteristics of thin layer carrots. Journal of Food Engineering, 61, 359-364.
19. Erenturk, S., & Erenturk, K. (2007). Comparison of genetic algorithm and neural network approaches for the drying process of carrots. Journal of Food Engineering, 78, 905-912.
20. Erenturk, K., Erenturk, S., & Tabil, L. (2005). A comparative study for the estimation of dynamical drying behavior of Echinacea angustifolia: regression analysis and neural network. Computers and Electronics in Agriculture, 45, 71-90.
21. Ertekin, C., & Yaldiz, O. (2004). Drying of eggplant and selection of a suitable thin layer drying model. Journal of Food Engineering, 63, 349-359.
22. Fathi, M., Mohebbi, M., & Razavi, S. M. A. (2009). Application of image analysis and artificial neural network to predict mass transfer kinetics and colour changes of osmotically dehydrated kiwifruit. Food and Bioprocess Technology doi: 10. 1007/s11947-009-0222-y.
23. Hernàndez-Pèrez, J. A., Garcia-Alvarado, M. A., Trystram, G., & Heyd, B. (2004). Neural networks for the heat and mass transfer prediction during drying of cassava and mango. Innovative Food Science & Emerging Technologies, 5, 57-64.
24. Kahrs, O., & Marquardt, W. (2007). The validity domain of hybrid models and its application in process optimization. Chemical Engineering Processes, 46, 1054-1066.
25. Klaypradit, W., Kerdpiboon, S., & Singh, R. K. (2010). Application of artificial neural network to predict the oxidation of Menhaden fish oil obtained from Fourier transform infrared spectroscopy method. Food and Bioprocess Technology doi: 10. 1007/s11947-010-0386-s.
26. Kondjoyan, A., & Boisson, H. C. (1997). Comparison of calculated and experimental heat transfer coefficients at the surface of circular cylinders placed in a turbulent cross-flow of air. Journal of Food Engineering, 34, 123-143.
27. Krokida, M. K., Karathanos, V. T., Maroulis, Z. B., & Marinos-Kouris, D. (2003). Drying kinetics of burdens vegetables. Journal of Food Engineering, 59, 391-403.
28. Lewiki, P. P. (2000). Raoult's law based food water sorption isotherm. Journal of Food Engineering, 43, 31-40.
29. Liu, X., Chen, X., Wu, W., & Peng, G. (2007). A neural network for predicting moisture content of grain drying process using genetic algorithm. Food Control, 18, 928-933.
30. Lopez, J., Uribe, E., Vega-Galvez, M. M., Vergara, J., Gonzales, E., & Di Scala, K. (2010). Effect of air temperature on drying kinetics, vitamine c, antioxidant activity, total phenolic content, non-enzymatic browning and firmness of blueberries variety O'Neill. Food and Bioprocess Technology, 3(5), 772-777.
31. Marquez, C. A., & de Michelis, A. (2009). Comparison of drying kinetics for small fruits with and without particle shrinkage considerations, Food and Bioprocess Technology doi: 10. 1007/s11947-009-0218-7.
32. Martynenko, A. I., & Yang, S. X. (2006). Biologically inspired neural computation for ginseng drying rate. Biosystems Engineering, 5(3), 385-396.
33. Mitra, P., Barman, P. C., & Chang, K. S. (2008). Coumarin Extraction from Cuscuta Reflexa using supercritical fluid carbon dioxide and development of an artificial neural network model to predict the coumarin yield. Food and Bioprocess Technology doi: 10. 1007/s11947-008-0179-2.
34. Movagharnejad, K., & Nikzad, M. (2007). Modeling of tomato drying using artificial neural network. Computers and Electronics in Agriculture, 59, 78-85.
35. Ni, H., & Datta, A. K. (1999). Heat and moisture transfer in baking of potato slabs. Drying Technology, 17(10), 2069-2092.
36. Patnaik, P. R. (2010). Design consideration in hybrid neural optimization of fed-batch fermentation for PHB production by Ralstonia eutropha. Food and Bioprocess Technology, 3, 213-225.
37. Pilatowski, I., Mounir, S., Haddad, J., Thai Cong, D., & Allaf, K. (2010). The instant controlled pressure drop process as a new post-harvesting treatment of paddy rice: impacts on drying kinetics and end product attributes. Food and Bioprocess Technology doi: 10. 1007/s11947-010-0332-6.
38. Psichogios, D. D., & Ungar, L. H. (1992). A hybrid neural network-first principle approach to process modeling. AIChE Journal, 38(10), 1499-1511.
39. Rahman, S. M. A., Islam, M. R., & Mujumdar, A. S. (2007). A study of coupled heat and mass transfer in composite food products during convective drying. Drying Technology, 25, 1359-1368.
40. Reilly, D. L., & Cooper, L. N. (1990). An overview of neural networks: early models to real world systems. In S. F. Zornetzer, J. L. Davis, & C. Lau (Eds.), An introduction to neural and electronic networks (pp. 227-248). New York: Academic.
41. Saraceno, A., Curcio, S., Calabrò, V., & Iorio, G. (2010). A hybrid neural approach to model batch fermentation of "ricotta cheese whey" to ethanol. Computers & Chemical Engineering, 34, 1590-1596.
42. Shittu, T. A., & Raji, A. O. (2008). Thin Layer Drying of African Breadfruits (Treculia Africana) Seeds: Modeling and Rehydration capacity. Food and Bioprocess Technology doi: 10. 1007/s11947-008-0161-z.
43. Uribe, E., Vega-Galvez, A., Di Scala, K., Oyanadel, R., Torrico, J. S., & Miranda, M. (2009). Characteristics of convective drying of Pepino fruits (Solanum Muricatum Ait.): Application of Weibull distribution. Food and Bioprocess Technology doi: 10. 1007-009-0230-y.
44. Uribe, E., Miranda, M., Vega-Galvez, A., Quispe, I., Claveria, R., & Di Scala, K. (2010). Mass transport modelling during the osmotic dehydration of Jumbo Squid (Dosidicus gigas): influence of temperature on diffusion coefficients and kinetic parameters. Food and Bioprocess Technology doi: 10. 1007/s-010-0336-2.
45. van Can, H. J. L., HAB, Te brake, Dubbelman, S., Hellinga, C., Luyben, K. C. A. M., & Heijnen, J. (1998). Understanding and applying the extrapolation properties of serial grey-box models. AIChE Journal, 44, 1071-1089.
46. Verbdryer, P., Nicolaï, B. M., Scheerlinck, N., & De Baerdemaeker, J. (1997). The local surface heat transfer coefficient in thermal food process calculations: A CFD approach. Journal of Food Engineering, 33, 15-35.
47. Zhang, J., & Datta, A. K. (2004). Some considerations in modeling of moisture transport in heating of hygroscopic materials. Drying Technology, 22(8), 1983-2008.
48. Zhang, G., Patuwo, B. E., & Hu, M. J. (1998). Forecasting with artificial neural network: The state of Art. International Journal of Forecasting, 14, 35-62.
49. Zhang, Q., Yang, S., Mittal, G., & Yi, S. (2002). Prediction of performance indices and optimal parameters of rough rice drying using neural networks. Biosystems Engineering, 83(3), 281-290.
50. Zhang, J., Datta, A. K., & Mukherjee, S. (2005). Transport processes and large deformation during baking of bread. AIChE Journal, 51(9), 2569-2580.
51. Zuniga, R., Rouaud, O., Boillereaux, L., & Havet, M. (2007). Conjugate heat and moisture transfer during a dynamic thermal treatment of food. Drying Technology, 25, 1411-1419.