Prediction by artificial neural networks of the physicochemical quality of cane molasses vinegar by time-temperature effect of food to flash evaporator-distiller

• Autores: Vásquez, Víctor, Lescano, Carlos
• Formato: artículo
• Fecha de Publicación: 2010
• Institución: Universidad Nacional de Trujillo
• Repositorio: Revistas - Universidad Nacional de Trujillo
• Lenguaje: español
• OAI Identifier: oai:ojs.revistas.unitru.edu.pe:article/17
• Enlace del recurso: https://revistas.unitru.edu.pe/index.php/scientiaagrop/article/view/17
• Nivel de acceso: acceso abierto
• Materia: Artificial Neural Networks (ANN); molasses vinegar; flash evaporator; flash distiller; Redes Neuronales Artificiales (RNA); vinagre de melaza; evaporador flash; destilador flash

It was predicted via Artificial Neural Networks (ANN) important physicochemical characteristics of molasses vinegar: pH, density, total acidity, ethanol, total aldehydes and furfural, obtained by flash evaporation operations and flash distillation clarification. Alcoholic and acetic fermented molasses were fed to a flash evaporator at four temperatures (61, 66, 71 and 76 ° C) and in three times (25, 35 and 45 min). The prediction was made with two networks: ANN and ANN-A-B, both with good performance. The ANN-A was of the feedforward (FF) type with Backpropagation (BP) training algorithms and set of Levenberg-Marquardt (LM) weights adjustment, topology: 6 inputs (operations data of flash evaporation-distillation), 7 linear outputs (physicochemical characteristics), 9 tangent sigmoidal neurons in 1 hidden layer, 0.5 moment coefficient, 0.01 learning rate, 0.0001 error goal and 20 training stages. The ANN-A showed better performance than a statistical model of first order. The ANN-B also FF, BP and LM algorithms, topology: 2 inputs (data from flash evaporation), 7 linear outputs (physical and chemical characteristics), 84 logarithm sigmoid neurons in 1 hidden layer, 0.5 moment coefficient, 0.01 learning rate, 0.0001 error goal and 300 training stages. The ANN-B showed the same predictive capacity as a statistical model of the first-order with interaction of terms.