Czech J. Food Sci., 2009, 27(6):393-402 | DOI: 10.17221/82/2009-CJFS

Eggshell crack detection based on acoustic impulse response and supervised pattern recognition

Hao LIN, Jie-Wen ZHAO, Quan-Sheng CHEN, Jian-Rong CAI, Ping ZHOU
School of Food and Biological Engineering, Jiangsu University, Zhenjiang, Jiangsu, People's Republic of China

A system based on acoustic resonance was developed for eggshell crack detection. It was achieved by the analysis of the measured frequency response of eggshell excited with a light mechanism. The response signal was processed by recursive least squares adaptive filter, which resulted in the signal-to-noise ratio of the acoustic impulse response reing remarkably enhanced. Five features variables were exacted from the response frequency signals. To develop a robust discrimination model, three pattern recognition algorithms (i.e. K-nearest neighbours, artificial neural network, and support vector machine) were examined comparatively in this work. Some parameters of the model were optimised by cross-validation in the building model. The experimental results showed that the performance of the support vector machine model is the best in comparison to k-nearest neighbours and artificial neural network models. The optimal support vector machine model was obtained with the identification rates of 95.1% in the calibration set, and 97.1% in the prediction set, respectively. Based on the results, it was concluded that the acoustic resonance system combined with the supervised pattern recognition has a significant potential for the cracked eggs detection.

Keywords: eggshell; crack; detection; acoustic resonance; supervised pattern recognition

Published: December 31, 2009  Show citation

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LIN H, ZHAO J, CHEN Q, CAI J, ZHOU P. Eggshell crack detection based on acoustic impulse response and supervised pattern recognition. Czech J. Food Sci. 2009;27(6):393-402. doi: 10.17221/82/2009-CJFS.
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