A Novel Acoustic Sensor Approach to Classify Seeds Based on Sound Absorption Spectra
A 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 stati...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2010-11-01
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| Series: | Sensors |
| Subjects: | |
| Online Access: | http://www.mdpi.com/1424-8220/10/11/10027/ |
| _version_ | 1798039196064022528 |
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| author | Ole Green Hasib Mansur Torben Brøchner Claus G. Sørensen Vicent Gasso-Tortajada Alastair J. Ward |
| author_facet | Ole Green Hasib Mansur Torben Brøchner Claus G. Sørensen Vicent Gasso-Tortajada Alastair J. Ward |
| author_sort | Ole Green |
| collection | DOAJ |
| description | A 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. |
| first_indexed | 2024-04-11T21:50:33Z |
| format | Article |
| id | doaj.art-76bb83a91860444193fc3bbd0acb894f |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-04-11T21:50:33Z |
| publishDate | 2010-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-76bb83a91860444193fc3bbd0acb894f2022-12-22T04:01:15ZengMDPI AGSensors1424-82202010-11-011011100271003910.3390/s101110027A Novel Acoustic Sensor Approach to Classify Seeds Based on Sound Absorption SpectraOle GreenHasib MansurTorben BrøchnerClaus G. SørensenVicent Gasso-TortajadaAlastair J. WardA 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.http://www.mdpi.com/1424-8220/10/11/10027/seedacousticsoundabsorptionnon-destructiveclassificationidentificationmultivariate statistics |
| spellingShingle | Ole Green Hasib Mansur Torben Brøchner Claus G. Sørensen Vicent Gasso-Tortajada Alastair J. Ward A Novel Acoustic Sensor Approach to Classify Seeds Based on Sound Absorption Spectra Sensors seed acoustic sound absorption non-destructive classification identification multivariate statistics |
| title | A Novel Acoustic Sensor Approach to Classify Seeds Based on Sound Absorption Spectra |
| title_full | A Novel Acoustic Sensor Approach to Classify Seeds Based on Sound Absorption Spectra |
| title_fullStr | A Novel Acoustic Sensor Approach to Classify Seeds Based on Sound Absorption Spectra |
| title_full_unstemmed | A Novel Acoustic Sensor Approach to Classify Seeds Based on Sound Absorption Spectra |
| title_short | A Novel Acoustic Sensor Approach to Classify Seeds Based on Sound Absorption Spectra |
| title_sort | novel acoustic sensor approach to classify seeds based on sound absorption spectra |
| topic | seed acoustic sound absorption non-destructive classification identification multivariate statistics |
| url | http://www.mdpi.com/1424-8220/10/11/10027/ |
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