An artificial neural network model for prediction of quality characteristics of apples during convective dehydration
نویسندگان
چکیده
Received 17/5/2012 Accepted 5/7/2013 (005700) 1 Food Engineering Research Group, Universidad Nacional de Mar del Plata – UNMdP, Mar del Plata, Buenos Aires, Argentina 2 Consejo Nacional de Investigaciones Científicas y Técnicas – CONICET, Av. Rivadavia, 1917, C1033AAJ, Ciudad Autónoma de Buenos Aires, Argentina, e-mail: [email protected] 3 Laboratory of Bioengineering, Universidad Nacional de Mar del Plata – UNMdP, Mar del Plata, Buenos Aires, Argentina 4 Department of Food Engineering, Universidad de La Serena – ULS, La Serena, Chile 5 Centro de Investigación y Desarrollo en Criotecnología de Alimentos – CIDCA, Centro Científico Tecnológico – CCT, Consejo Nacional de Investigaciones Científicas y Tecnológicas – CONICET, Universidad Nacional de La Plata – UNLP, La Plata, Buenos Aires, Argentina 6 Facultad Ingeniería – MODIAL, Universidad Nacional de La Plata – UNLP, La Plata, Buenos Aires, Argentina *Corresponding author An artificial neural network model for prediction of quality characteristics of apples during convective dehydration Karina DI SCALA1,2*, Gustavo MESCHINO3, Antonio VEGA-GÁLVEZ4, Roberto LEMUS-MONDACA4, Sara ROURA1,2, Rodolfo MASCHERONI2,5,6
منابع مشابه
Application of artificial neural network (ANN) for the prediction of water treatment plant influent characteristics
Application of a reliable forecasting model for any water treatment plant (WTP) is essential in order to provide a tool for predicting influent water quality and to form a basis for controlling the operation of the process. This would minimize the operation and analysis costs, and assess the stability of WTP performances. This paper focuses on applying an artificial neural network (ANN) approac...
متن کاملAn Artificial Neural Network Model for Prediction of the Operational Parameters of Centrifugal Compressors: An Alternative Comparison Method for Regression
Nowadays, centrifugal compressors are commonly used in the oil and gas industry, particularly in the energy transmission facilities just like a gas pipeline stations. Therefore, these machines with different operational circumstances and thermodynamic characteristics are to be exploited according to the operational necessities. Generally, the most important operational parameters of a gas pipel...
متن کاملComparison of artificial neural network and multivariate regression methods in prediction of soil cation exchange capacity (Case study: Ziaran region)
Investigation of soil properties like Cation Exchange Capacity (CEC) plays important roles in study of environmental reaserches as the spatial and temporal variability of this property have been led to development of indirect methods in estimation of this soil characteristic. Pedotransfer functions (PTFs) provide an alternative by estimating soil parameters from more readily available soil data...
متن کاملThe Application of Neural Network Method for the Prediction of the Osmotic Factors of Crookneck Squash
ABSTRACT: In this study the reliability of using response surface-neural network method to predict the osmotic dehydration properties of crookneck squash has been investigated. In order to carry out this project, the osmotic solution concentration, the osmotic solution temperature and immersion time were chosen as inputs and solid gain and water loss were selected as outputs of the designed net...
متن کاملApplication of Artificial Neural Network and Fuzzy Inference System in Prediction of Breaking Wave Characteristics
Wave height as well as water depth at the breaking point are two basic parameters which are necessary for studying coastal processes. In this study, the application of soft computing-based methods such as artificial neural network (ANN), fuzzy inference system (FIS), adaptive neuro fuzzy inference system (ANFIS) and semi-empirical models for prediction of these parameters are investigated. Th...
متن کامل