Rendimiento Quesero

Su predicción como una herramienta para evaluar el proceso de elaboración

Abstract

There is not consensus concerning the correct way to predict cheese yield. Actually, the equations available are based on a mass balance of the components including transfer coefficients and / or retention of, regardless of the deviations caused by the processing conditions, these being so complex and diverse that make it impossible to develop a mathematical model able of including all factors involved: physicochemical, technological and human.
In this work, the cheese yield of a creamy cheese, provided by the ESIL pilot plant, Villa Maria, was studied. The evaluation was performed with A neural network for predicting cheese yield using milk composition data, and comparing the predicted and real yield.
Finally, the capability of cheese yield prediction with a neural network model was better than mass balance methods and suggests it could be expanded to the industrial level.