Using artificial neural networks in prediction of the drying process of foods that are rich in sugars

  • E. Figueroa-Garcia
  • V.S. Farias-Cervantes
  • M. Segura-Castruita
  • I. Andrade-Gonzalez
  • M.I. Montero-Cortés
  • A.M. Chávez-Rodríguez
Keywords: Secado por aspersión, Redes Neuronales Artificiales (ANN), Maltodextrina (Mdx), Alimentos ricos en azúcar (SRF).


The production of powder which comes from dehydration sugar-rich foods (SRF), has great economic potential. However, the concentration of the different types of sugar that the SRF consist of varies, this presents problems in the drying process. drying carrying agents are used, nevertheless there isn't an exact dosage for all the SRF. Artificial Neural Networks or (ANN) recently became one of the most efficient empirical methods used to predict and model such systems, especially non-linear systems, Therefore the main objective for this work, was to develop a mathematical model of inverse propagation ANN throughout predicting the spray drying of the SRF, we took 6 input variables Mdx, F, G, S, T and OA and took 6 output as well H, Tg, °BX, HI, WA, R. The selected ANN model was (10-16-14-10) was compared with experimental data done by orthogonal regression which shows that the PMSSRF) model is an efficient model to predict the spray drying of the SRF and therefore the number the amount of experimental testing has been reduced replacing in this way the traditional, trial and error methodologies used, by using the tool development.


Adhikari, B., Howes, T., Bhandari B. R. & Truong V. (2003) a. Characterization of the Surface Stickiness of Fructose-Maltodextrin Solutions During Drying. Drying Technology and An International Journal 21, 17-34.
Adhikari, B., Howes, T., Bhandari, B. R. and Truong V. (2003) b. Surface Stickiness of Drops of Carbohydrate and Organic Acid Solutions During Convective Drying: Experiments and Modeling. Drying Technology and An International Journal 21, 839-873
Adhikari, B., Howes, T., Bhandari, B. R. and Truong, V. (2000). Experimental studies and kinetics of single drop drying and their relevance in drying of sugar-rich foods: a review. International Journal of Food Properties 3, 323-351.
Aghav, R. M., Kumar, S. and Mukherjee, S. N. (2011). Artificial neural network modelling in competitive adsorption of phenol and resorcinol from water environment using some carbonaceous adsorbents. Journal of Hazardous Materials 188, 67-77.
Avila, E. L. Rodríguez, M. C. and Velásquez, H.J.C (2015). Influence of maltodextrin and spray drying process conditions on sugarcane juice powder quality. Revista Facultad Nacional de Agronomía, Dedellin, 68, 7509-7520
Bas, D., & Boyaci, I. H. (2007). Modeling and optimization 1: Usability of response Surface methodology. Journal of Food Engineering 78, 836-845.
Bartoluzza, A., C. Fagnano, M.A. Morelli, A. Tinti and M. R. Tosi, 1993. “The role of water in biological systems”. J. Molec. Struc. 297:425-437.
Bezerra, M. A., Santelli. R. R., Oliveira, E. P., Villar, L. S., and Escaleira, I. A. (2008). Response Surface methodology (RSM) as a tool for optimization in analytical chemistry. Talanta 76, 965-977.
Bjandari, B. R., Senoussi, A., Dumoulin, E. D., & Lebert, A. (1993). Spray Drying of concentrated fruit juices. Drying Technology, 11(5), 1081-1092.
Bhandari, B.R. & Hartel, R. W. (2005). Phase transitions during food poder production and powder stability. In Encapsulated and powdered Foods, FL. 261 – 292.
Bhandari, B.R, and Howes, T. (1999). Implication of glass transitions for the drying and stability of foods. Journal of Food Engineering, 40, 71-79.
Bhandari, B.R., Datta, N, Cooks, R, Howes, T. and Rigby, S.A. (1997). Semiempirical approach to optimize the quatinty of drying aids required to spray dry sugar rich foods. Drying Technology, 5 2509-2525.
Ҫakmark, G. and Yildiz, C. (2011). The prediction of seedy grape drying rate using a neural network method. Computers and Electronics in Agriculture, Vol 75 (1), pp 132-138.
Carroll, R.J., and Ruppert, D, (1994). The use and misuse of orthogonal regression estimation in linear errors-in-variables models. Tech. Rep, Department of Statistics, University of Texas A&M, College Station, Tex, USA.
Cevallos, Ampuero, J. (2004). “Aplicación de redes neuronales para optimizar problemas multirespuesta en mejora de la calidad”. Revista de la facultad de Ingeniería Industrial, 2, 31-34.
Chávez-Rodríguez, A., Farias-Cervantes, V. S., Luna-Solano, G., Chávez-Rodríguez, A. M., Ortiz-Basulto, R. I., and Andrade-González, I. (2016). Quality attributes and particles deposition of spray dried fructans of blue agave juice. Revista Mexicana de Ingeniería Química, 15(2)
Cynthia, S. Bosco, J.D. and Bhol, S. (2015). Physical and structural properties of spray dried tamarind (Tamarindus Indica L.) pulp extract powder with encapsulating hydrocolloids. International Journal of Food Properties, 18, 1793-1800.
Dolinsky, A., Maletskaya, K. & Snezhkin, Y. (2000). Fruit and vegetable powders production technology on the bases of spray and convective drying methods, Drying Technology 18, 747-758.
Douglas, E. M., Jacobs, J. M., Sumne, D. M., & Ray, R. L. (2009). A comparison of models for estimating potential evapotranspiration for Florida land cover types. Journal of Hydrology 373, 366-376.
Goula, A. M. (2017). Implications of non-equilibrium state glass transitions in spray-dried sugar-rich foods. In Non-Equilibrium States and Glass Transitions in Foods (pp. 253-282). Woodhead Publishing.
Gunhan, T., Demir, V., Hancioglu, E., & Hepbasli, A. (2005). Mathematical modeling of drying of bay leaves. Energy Conversion Management, 46(11-12), 1667-1679.
Gutierrez L. F., Arias S., Garzón D., López D. M. & Osorio A. (2004). Transición vítrea en alimentos: Sistemas binarios agua-carbohidratos. Revista Vector 9, 21-28.
Horuz, E., Aylin, A. & MAskan, M. (2012). Spray Drying and Process Optimization of Unclarified Oomegranate (Punica granatum) Juice. Drying Technology 30, 787-798.
Lipiäinen, T., Räikkönen, H., Kolu, A. M., Peltroniemi, M., & Juppo, A. (2018). Comparison of meliiose and trehalose as stabilizing excipients for spray-dried β-galactosidase formulations. International journal of pharmaceutics, 543(1-2), 21-28.
Martínez, V., Baladrón, C., Gomez, J., Ruiz, G., Navas, L.M. Aguiar, J.M. & Carro, B. Drying Process Using Artificial Neural Networks. Sensors 12, 14004-14021.
Movagharnehad, K. & Nikzad, M. (2007). Modeling of tomato drying using artificial neural network. Computers and Electronics in Agriculture 59, 78-85.
Nair, A., Khunt, D., & Misra, M. (2009). Application of quality by design for optimization of spray drying process used in drying of Risperidone nanosuspension. Powder Technology 342, 156-165.
Ozdemir, U., Ozbay, B., Veli, S. & Zor, S., (2011). Modeling adsorption of sodium dodecyl benzene sulfonate (SDBA) onto polyaniline (PANI) by using multi linar regression and artificial neural networks. Chemical Engineering Journal 178, 183-190.
Reisi-Dehkordi, A. & Eslami-Farsanim, R. (2015). Prediction of High-Performance Fibers Strength Using Back Propagation Neural Network. Journal of Macromolecular Science, Part A and Pure and Applied Chemistry 52, 642-647.
Roos, Y. & Karel, M. (1991). Phase transition of mixture of amorphous polysaccharides and sugars. Biotechnology Progress 7, 49-53.
Roustapour, O. Hosseinalipur, M. and Ghobadian, B. (2006). An experimental investigation of lime juice drying in a pilot plant spray dryer. Drying Technology, 24, 181-188.
Sahoo, G. B. & Ray, Ch. (2006). Application of artificial neural networks to assess pesticide contamination in shallow groundwater. Journal of Membrane Science 283, 147-157.
Samborska, K. Gajek, P and Kaminińska-Dwórznicka A. (2015). Spray drying of honey: The effect of drying agents on powder properties. Polish Journal of Food and Nutrition Sciences, 65, 109-118.
Segura-Castruita, M. A. & Ortiz-Solorio, C. A. (2017). Modelación de la evapotranspiración potencial mensual a partir de temperaturas máximas-mínimas y altitud. Tecnología y Ciencias del Agua, Vol 8, 93-110.
Sinha, K., Saha, P.D. & Datta, S. (2012). Response Surface optimization and artificial neural network modeling of microwave assisted natural dye extraction from pomegranate rind. Industrial Crops and Products 37, 408-414.
Youssefi, Z, Sh. Emam-Djomeh, & Mousavi, S.M. (2009). Comparison of Artificial Neural Network (ANN) and Response Surface Methodology (RSM) in the Prediction of Quality Parameters of Spray-Dried Pomegranate Juice. Drying Technology, 27, 910-917.
How to Cite
Figueroa-Garcia, E., Farias-Cervantes, V., Segura-Castruita, M., Andrade-Gonzalez, I., Montero-Cortés, M., & Chávez-Rodríguez, A. (2020). Using artificial neural networks in prediction of the drying process of foods that are rich in sugars. Revista Mexicana De Ingeniería Química, 20(1), 161-171.
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