Moisture prediction of sweet potato-quinoa-kiwicha flakes dried by rotary drum dryer using artificial intelligence


  • Víctor Vásquez-Villalobos Universidad Nacional de Trujillo
  • Orlando Hernández-Bracamonte Departamento de Matemáticas. Universidad Nacional de Trujillo, Perú.
  • Julio Rojas-Naccha Departamento de Ciencias Agroindustriales. Universidad Nacional de Trujillo, Perú.
  • Viviano Ninaquispe-Zare Departamento de Ciencias Agroindustriales. Universidad Nacional de Trujillo, Perú.
  • Carmen Rojas-Padilla Departamento de Ciencias Agroindustriales. Universidad Nacional de Trujillo, Perú.
  • Julia Vásquez-Angulo Escuela de Ingeniería Agroindustrial. Universidad Nacional de Trujillo, Perú.


Palabras clave:

Artificial Neural Networks, Fuzzy Logic, Response Surface, Genetic Algorithms, Rotary Drum Drying


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.


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Received December 1, 2015.

Accepted January 4, 2018.

Corresponding author: (V. Vásquez-Villalobos).




Cómo citar

Vásquez-Villalobos, V., Hernández-Bracamonte, O., Rojas-Naccha, J., Ninaquispe-Zare, V., Rojas-Padilla, C., & Vásquez-Angulo, J. (2018). Moisture prediction of sweet potato-quinoa-kiwicha flakes dried by rotary drum dryer using artificial intelligence. Scientia Agropecuaria, 9(1), 83-91.



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