Prediction of Cucumber Acoustic Response, Crispness Index and Firmness Using Artificial Neural Networks

Cucumber fruit consumes in high quantities during all seasons in Iran; so it is important to evaluate parameters that affected the cucumber quality. Measurement of these parameters is expensive and time-consuming process. Therefore, parameters prediction due to affecting factors will be more useful. In this research work, artificial neural networks used for modelling the relationship between mechanical properties (crispness index and firmness) and sound pressure during cutting (acoustic response) with storage time, storage conditions on different positions on cucumber fruit. The networks input were storage time, storage conditions and test position on the fruit length. The networks output targets were the values of the mechanical properties and acoustic response. The different networks defined and trianed with different topologies. Multi layer perception (MLP) and radial basis function (RBF) networks used with different number of neurons. The training rules were Momentum, Conjugate Gradient and Levenberg-Marquardt. The transfer functions were TanhAxon and SigmoidAxon. The networks evaluated respected to estimation the accuracy of acoustic and mechanical properties. The results showed that MLP network with Momentom training function, TanhAxon transfer function and 3-5-3-3 topology had the best accuracy for prediction of acoustic and mechanical properties of Viola cucumber fruit. This network can predict the sound pressure, crispness index and firmness of the fruit with determinations coefficient of 0.9973, 0.9456 and 0.9129 and root mean square error of 0.021, 0.052 and 0.059 respectively.