Volume 8, Issue 32 (2011)                   FSCT 2011, 8(32): 85-94 | Back to browse issues page

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Effect of temperature and padding surface on apple bruise volume due to impact and its prediction by artificial neural network. FSCT 2011; 8 (32) :85-94
URL: http://fsct.modares.ac.ir/article-7-6707-en.html
Abstract:   (7859 Views)
  The mechanical losses which occur in agricultural products are the damage's which imposed on country economy. It is important to investigate the bruising phenomena, as an index of mechanical losses for loss reduction and optimization of harvest and postharvest machinery. In the current study, by means of an impact pendulum apparatus and by conducting a series of impact tests, the effects of temperature (0, 10, 20 and 300C), variety (Golden Delicious and Red Delicious), padding surface (corrugated carton, rubber and galvanized iron) and kinetic energy (300, 600 and 900 mJ) were investigated on rate of apple bruise. Statistical results showed that the effect of temperature, variety, padding surface and impact energy were significant on the mean value of bruise volume at 1% of statistical level. By increasing temperature, the bruised volume was decreased, whereas it increased by increase of energy level in both varieties. While, the Golden Delicious had more strength than Red Delicious. Also, the maximum rate of bruised volume was related to Red Delicious in contacting to galvanized iron and the minimum rate was related to Golden Delicious in contacting to corrugated carton. Prediction of bruised volume was provided using artificial neural network based on four factors of: temperature, impact energy, padding surface and variety. Multilayer perceptron of neural networks were used for prediction of bruised volume. In comparison with other topologies, algorithm Levenberg-Marquardt had better performance with structure 1-26-4 and logsigmoid transfer function in hidden layer. Based on this algorithm, the mean of prediction accuracy in training, evaluation and testing process was equal to 92.48, 88.94 and 87.72 percent, respectively. In addition, the correlation coefficient (R) was calculated equal to 0.975 for linear regression between predicted model and experimental data.  
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Received: 2009/09/24 | Accepted: 2010/02/7 | Published: 2012/06/27

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