Soft Computing Methodology for Shelf Life Prediction of Processed Cheese
نویسندگان
چکیده
منابع مشابه
Intelligent Artificial Neural Network Computing Techniques for Shelf Life Determination of Processed Cheese
In this study feedforward and competitive artificial neural network models were developed for predicting shelf life of processed cheese stored at 30 C. Processed cheese is a food product generally made from Cheddar cheese. Processed cheese has several advantages over unprocessed cheese, such as extended shelf-life, resistance to separation when cooked, and uniformity of product. Input parameter...
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— For predicting the shelf life of processed cheese stored at 7-8º C, Elman single and multilayer models were developed and compared. The input variables used for developing were applied in order to compare the prediction ability of the developed models. The Elman models got simulated very well and showed excellent agreement between the experimental data and the predicted values, suggesting tha...
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Radial basis ( fewer neurons) artificial neural network (ANN) models were developed for predicting the shelf life of processed cheese stored at 7-8o C. Mean square error, root mean square error, coefficient of determination and nash sutcliffo coefficient were applied in order to compare the prediction ability of the developed models. Soluble nitrogen, pH; standard plate count, yeast & mould cou...
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Elman artificial neural network models with single and multilayer for predicting shelf life of processed cheese stored at 7-8oC were developed. Input parameters were: Body & texture, aroma & flavour, moisture, and free fatty acid, while sensory score was output parameter. Bayesian regularization was training algorithm for the models. The network was trained up to 100 epochs, and neurons in each...
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ژورنال
عنوان ژورنال: International Journal of Informatics and Communication Technology (IJ-ICT)
سال: 2012
ISSN: 2252-8776
DOI: 10.11591/ij-ict.v1i1.506