Moisture prediction of sweet potato-quinoa-kiwicha flakes dried by rotary drum dryer using artificial intelligence
DOI:
https://doi.org/10.17268/sci.agropecu.2018.01.09Palavras-chave:
Artificial Neural Networks, Fuzzy Logic, Response Surface, Genetic Algorithms, Rotary Drum DryingResumo
A bi-factorial experimental design was considered to assess moisture variation of sweet potato-quinoa-kiwicha flakes (SP-Q-K) caused by the changes in the rotational speed and steam pressure of a rotary drum dryer (RDD). As it is a design with discrete variables, there is a limitation in the modeling and optimization thus techniques of Artificial Intelligence (AI): Artificial Neural Networks (ANN), Fuzzy Logic (FL) and Genetic Algorithms (GA), were applied, and their prediction ability evaluated. Due to the limitation of data for proper training, the ANN did not allow a correct prediction of the experimental data. Response Surface Methodology (RSM) was employed to obtain the relational equation among the experimental variables, which was used as the objective function with GA, and this allowed moisture optimization. Because of this, it is recommended to integrate RSM and GA into optimization studies. In this research the use of FL among variables, enabled us to get the best prediction adjustment of experimental values (R2 = 0.99), with a mean absolute error of 0.6±0.66 %, setting a pressure value of 5 atm and a speed value of 6 rpm for flakes at 4.99 % humidity.
Referências
Amelia, L. 2012. Modelling of palm oil production using fuzzy expert system. Universitas Esa Unggul. Pp. 4. Available in: http://www.esaunggul.ac.id/article/modelling-of-palm-oil-production-using-fuzzy-expert-system/
Azadbakht, M.; Aghili, H.; Ziaratban, A.; Torshizi, M. 2017. Application of artificial neural network method to exergy and energy analyses of fluidized bed dryer for potato cubes. Energy 120: 947-958.
Benković, M.; Jurinjak, A.; Belščak-Cvitanović, A.; Lenart, A.; Domian, E.; Komes, D.; Bauman, I. 2015. Artificial neural network modelling of changes in physical and chemical properties of cocoa powder mixtures during agglomeration. LWT - Food Science and Technology 64(1): 140-148.
Carvalho, A.; Mattietto, R.; Bassinello, P.; Koakuzu, S.; Rios, A.; Maciel, R.; Carvalho, R. 2012. Processing and characterization of extruded breakfast meal formulated with broken rice and bean flour. Ciênc. Tecnol. Aliment., Campinas 32(3): 515-524.
Cheng, J.; Wang, X.; Si, T.; Zhou, F.; Zhou, J.; Cen, K. 2016. Ignition temperature and activation energy of power coal blends predicted with back-propagation neural network models. Fuel 173: 230-238.
Dao, S.; Abhary, K.; Marian, R. 2017. An innovative framework for designing genetic algorithm structures. Expert Systems with Applications 90: 196-208.
Devi, A.; Eyles, H.; Rayner, M.; Mhurchu, C.; Swinburn, B.; Lonsdale-Cooper, E.; Vandevijvere, S. 2014. Nutritional quality, labelling and promotion of breakfast cereals on the New Zealand market. Appetite 81:253-260.
Díaz-Cortés, M.; Cuevas, E.; Gálvez, J.; Camarena, O. 2017. A new metaheuristic optimization methodology based on fuzzy logic. Applied Soft Computing 61: 549-569.
Galaz, P.; Valdenegro, M.; Ramírez, C.; Nuñez, H.; Almonacid, C.; Simpson, R. 2017. Effect of drum drying temperature on drying kinetic and polyphenol contents in pomegranate peel. Journal of Food Engineering 208: 19-27.
Guiné, R.; Barroca, M.; Gonçalves, F.; Alves, M.; Oliveira, S.; Mendes, M. 2015. Artificial neural network modelling of the antioxidant activity and phenolic compounds of bananas submitted to different drying treatments. Food Chemistry 168: 454-459.
Jafari, S.; Ganje, M.; Dehnad, D.; Ghanbari, V. 2015. Mathematical, fuzzy logic and artificial neural network modeling techniques to predict drying kinetics of onion. Journal of Food Processing and Preservation, 1-11.
Kant, G.; Singh, K. 2015. Predictive Modelling and Optimization of Machining Parameters to Minimize Surface Roughness using Artificial Neural Network Coupled with Genetic Algorithm. Procedia CIRP 31: 453-458.
Maran, J.; Sivakumar, V.; Thirugnanasambandham, K.; Sridhar, R. 2013. Artificial neural network and response surface methodology modeling in mass transfer parameters predictions during osmotic dehydration of Carica papaya L. Alexandria Engineering Journal 52: 507-516.
Martinez-Conesa, E.; Egea, J.; Valentin, M.; Toledo, C.; Meseguer-Valdenebro, J. 2017. Optimization of geometric parameters in a welded joint through response surface methodology. Construction and Building Materials 154: 105-114.
Milczarek, R.; Ferry, J.; Alleyne, F.; Olsen, C.; Olson, D.; Winston, R. 2017. Solar thermal drum drying performance of prune and tomato pomaces. Food and Bioproducts Processing 106: 53-64.
Montgomery, D.; Runger, G. 2012. Probabilidad y estadística aplicadas a la ingeniería. 2da Edición Edit. Limusa S.A. México. D.F., 817 pp.
Muddle, J.; Kirton, S.; Parisini, I.; Muddle, A.; Murnane, D.; Ali, J.; Brown, M.; Page, C.; Forbes, B.; 2017. Predicting the Fine Particle Fraction of Dry Powder Inhalers Using Artificial Neural Networks. Journal of Pharmaceutical Sciences 106(1): 313-321.
Nadian, M.; Rafiee, F.; Aghbashlo, M.; Hosseinpour, S.; Mohtasebi, S. 2015. Continuous real-time monitoring and neural network modeling of apple slices color changes during hot air drying. Food and Bioproducts Processing 94: 263-274.
Navruz-Varli, S; Sanlier, N. 2016. Nutritional and health benefits of quinoa (Chenopodium quinoa Willd.). Journal of Cereal Science 69: 371-376.
Nasiri, S.; Khosravani, M.; Weinberg, K. 2017. Fracture mechanics and mechanical fault detection by artificial intelligence methods: A review. Engineering Failure Analysis 81: 270-293.
Nawi, N.; Khan, A.; Rehman, M. 2013. A New Levenberg Marquardt Based Back Propagation Algorithm Trained with Cuckoo Search. Procedia Technology 11: 18-23.
Rhaman, M.; Mustayen, A.; Mekhilef, S.; Saidur, R. 2015. The Optimization of Solar Drying of Grain by Using a Genetic Algorithm. International Journal of Green Energy 12(12): 1222-1231.
Paucar-Menacho, L.; Peñas, E.; Dueñas, M.; Frias, J.; Martínez-Villaluenga, C. 2017. Optimizing germination conditions to enhance the accumulation of bioactive compounds and the antioxidant activity of kiwicha (Amaranthus caudatus) using response surface methodology. LWT-Food Science and Technology 76B: 245-242.
Ramírez, M. 2014. Modelo de reglas difuso para el análisis y evaluación de MOOCs con la norma UNE 66181 de calidad de la formación virtual. Tesis de Doctorado, Universidad Pablo de Olavide, Sevilla. España. 283 pp.
Ramos, J.; Kirjoranta, S.; Tenitz, S.; Penttilä, P.; Serimaa, R.; Lampi A.; Jouppila, K. 2013. Use of amaranth, quinoa and kañiwa in extruded corn-based snacks. Journal of Cereal Science 58: 59-67.
Rojas, J.; Vasquez, V. 2012. Prediction by Artificial Neural Networks (ANN) of the diffusivity, mass, moisture, volume and solids on osmotically dehydrated yacon (Smallantus sonchifolius). Scientia Agropecuaria 3: 201-214.
Shahraki, H.; Ziaiifar, A.; Kashaninejad, S.; Ghorbani, M. 2014. Optimization of pre-fry microwave drying of French fries using response surface methodology and genetic algorithms. Journal of Food Processing and Preservation 38(1): 535-550.
Sjafrina, N.; Aminah, S. 2014. Consumers Preferences to Sweet Potato Flakes (Spf) from Sweet Potato Pasta as A Raw Material, with Enrichment Mung Bean Flour as Source Protein. International Journal on Advanced Science Engineering 4(3): 67-60.
Thanh, D. 2015. Optimization of drum drying process parameters for pumpkin powder production and its substitution in rice noodles. Journal of Science C: Agricultural Sciences, Fisheries and Biotechnology 3(3): 149-160.
Tavakolipour, H.; Mokhtarian, M.; Kalbasi-Ashtari, A. 2014. Intelligent Monitoring of Zucchini Drying Process Based on Fuzzy Expert Engine and ANN. Journal of Food Process Engineering 37(5): 474-481.
Wang, S.; Nie, S.; Zhu, F. 2016. Chemical constituents and health effects of sweet potato. Food Research International 89(1): 90-116.
Woolfe, J. 1992. Sweet potato: an untapped food source. Cambridge University Press. Cambridge, UK. 643 pp.
Zhong, Ch.; Pedrycz, W.; Li, Z.; Wang, D.; Li, L. 2016. Fuzzy associative memories: A design through fuzzy clustering. Neurocomputing 173(3): 1154-1162.
Received December 1, 2015.
Accepted January 4, 2018.
Corresponding author: vvasquez@unitru.edu.pe (V. Vásquez-Villalobos).
Downloads
Publicado
Como Citar
Edição
Seção
Licença
Los autores que publican en esta revista aceptan los siguientes términos:
a. Los autores conservan los derechos de autor y conceden a la revista el derecho publicación, simultáneamente licenciada bajo una licencia de Creative Commons que permite a otros compartir el trabajo, pero citando la publicación inicial en esta revista.
b. Los autores pueden celebrar acuerdos contractuales adicionales separados para la distribución no exclusiva de la versión publicada de la obra de la revista (por ejemplo, publicarla en un repositorio institucional o publicarla en un libro), pero citando la publicación inicial en esta revista.
c. Se permite y anima a los autores a publicar su trabajo en línea (por ejemplo, en repositorios institucionales o en su sitio web) antes y durante el proceso de presentación, ya que puede conducir a intercambios productivos, así como una mayor citación del trabajo publicado (ver efecto del acceso abierto).
