engleski

Comparison of artificial neural network and mathematical models for drying of apple slices pretreated with high intensity ultrasound

In this paper artificial neural network model was compared to the traditional regression models for drying of food materials. High intensity ultrasound with amplitudes set to 25 %, 50 %, 75 % and 100 % of maximal was used for treatment of apple slices of different thickness. After 7 minutes of treatment, samples were dried in the infrared drier at two different temperatures. Four most often used regression models for drying available in literature were fitted based on experimental data, and their usability was tested on different experimental sets. For the creation of back-propagation neural network, 3 input parameters were used (amplitude of ultrasound, sample thickness and drying temperature) together with one output (moisture content). After training and validation of networks, statistical analysis was conducted and based on mean square error and correlation coefficient best network was selected. After assessment of networks and statistical results, neural networks show excellent fitting to experimental data, independently of used input parameters obtained in experiments. This is opposed to standard regression models, which had excellent fit to just one set of experimental data, and show inadequate fit even with introduced small changes in one or more input parameters.

apple ; artificial neural network ; drying ; mathematical model ; ultrasound

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