A Novel Acoustic Sensor Approach to Classify Seeds Based on Sound Absorption Spectra
AbstractA non-destructive and novel in situ acoustic sensor approach based on the sound absorption spectra was developed for identifying and classifying different seed types. The absorption coefficient spectra were determined by using the impedance tube measurement method. Subsequently, a multivariate statistical analysis, i.e., principal component analysis (PCA), was performed as a way to generate a classification of the seeds based on the soft independent modelling of class analogy (SIMCA) method. The results show that the sound absorption coefficient spectra of different seed types present characteristic patterns which are highly dependent on seed size and shape. In general, seed particle size and sphericity were inversely related with the absorption coefficient. PCA presented reliable grouping capabilities within the diverse seed types, since the 95% of the total spectral variance was described by the first two principal components. Furthermore, the SIMCA classification model based on the absorption spectra achieved optimal results as 100% of the evaluation samples were correctly classified. This study contains the initial structuring of an innovative method that will present new possibilities in agriculture and industry for classifying and determining physical properties of seeds and other materials. View Full-Text
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Gasso-Tortajada, V.; Ward, A.J.; Mansur, H.; Brøchner, T.; Sørensen, C.G.; Green, O. A Novel Acoustic Sensor Approach to Classify Seeds Based on Sound Absorption Spectra. Sensors 2010, 10, 10027-10039.
Gasso-Tortajada V, Ward AJ, Mansur H, Brøchner T, Sørensen CG, Green O. A Novel Acoustic Sensor Approach to Classify Seeds Based on Sound Absorption Spectra. Sensors. 2010; 10(11):10027-10039.Chicago/Turabian Style
Gasso-Tortajada, Vicent; Ward, Alastair J.; Mansur, Hasib; Brøchner, Torben; Sørensen, Claus G.; Green, Ole. 2010. "A Novel Acoustic Sensor Approach to Classify Seeds Based on Sound Absorption Spectra." Sensors 10, no. 11: 10027-10039.