Volume 16, Issue 96 (2020)                   FSCT 2020, 16(96): 103-119 | Back to browse issues page

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Zandi M, Ganjloo A, Bimakr M. Modelling medlar (Mespilus germanica) quality changes during cold storage using kinetics models and artificial neural network. FSCT 2020; 16 (96) :103-119
URL: http://fsct.modares.ac.ir/article-7-35326-en.html
1- Assistant Professor, Department of Food Science and Engineering, Faculty of agricultural University of Zanjan, Zanjan, Iran , zandi@znu.ac.ir
2- Associate Professor, Department of Food Science and Engineering, University of Zanjan, Zanjan, Iran
3- Associate Professor, Department of Food Science and Engineering, Faculty of agricultural University of Zanjan, Zanjan, Iran
Abstract:   (2975 Views)
The aim of this research was to investigate the degradation kinetics of the major quality properties of medlar (Mespilus germanica) during cold storage. Medlar is a widely growth in northern Iran and its fruit is used as a nutritional component and as a medicinal remedy. In fruits, quality properties are used as a consumer-based criteria of acceptability. So it is important to evaluate parameters that affected the medlar quality. Measurement of these parameters is an expensive and time-consuming process. Therefore, parameter prediction due to affecting factors will be more useful. In the present research, mathematical models and artificial neural networks (ANN) were used for modelling the relationship between physicochemical properties and color attributes with cold storage time. Five kinetic models viz. zero order, first order, Second order, fractional conversion and Weibull models were used for modelling using MATLAB. Among the kinetics models, the Weibull model was found to be more suitable to predict the changes in all physicochemical ( , ) and color ( , ) parameters. In ANN, multi-layer perception (MLP) used with different number of neurons. The network’s inputs include storage time, medlar moisture content and ripening stage and the network’s output were the values of the physicochemical and color properties. The training rule was Momentum Levenberg-Marquardt. The transfer functions were Tansig, Purelin and Logsig. The results showed that MLP network with Levenberg-Marquardt training function, Purelin transfer function and 3-8-4-3 and 3-7-2 topologies had the best accuracy for prediction of for physicochemical and color properties. This network can predict physicochemical and color properties of the medlar with  coefficient of 0.9983 and 0.9992 and MSE of 0.021, 0.000008 and 0.000059 respectively.
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Article Type: Original Research | Subject: Statistics, modeling and response levels in the food industry
Received: 2019/07/29 | Accepted: 2020/02/3 | Published: 2020/01/30

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