Feasibility of non-destructive evaluation for apple crispness based on portable acoustic signal

In this paper, apple crispness was evaluated by sensory evaluation and compared with non-destructive measurements of portable acoustic signal to discuss the feasibility of non-destructive evaluation for apple crispness based on portable acoustic signal. Acoustic eigenvalues from the acoustic signal were processed by time domain and Hilbert-Huang transform (HHT), followed by analysing the correlations with apple crispness that had been evaluated via sensory evaluation. Multiple linear regression (MLR) and artificial neural network (ANN) were applied to predict apple crispness. The results proved that crispness correlates significantly (P < 0.01) with four acoustic eigenvalues, including waveform index, sound intensity, energy of low frequency and energy of high frequency. The average relative error of apple crispness predicted by ANN was 1.42 +/- 1.9%, remarkably lower (P < 0.01) that of MLR (6.79 +/- 5.64%), implying that the model predicted by ANN is more accurate than that of MLR.

Automated summary

This study evaluated apple crispness using acoustic signals and predicted the crispness using multiple linear regression (MLR) and artificial neural network (ANN). Results showed that ANN predicted apple crispness more accurately than MLR.