Combination of Support Vector Machines (SVM) and Near Infrared (NIR) imaging spectroscopy for the detection of meat and bone meat (MBM) in compound feeds.
- Baeten, V. , Michotte Renier, A. , Cogdill, R. , Dardenne, P. & Fernández Pierna, J.A. (2004). Combination of Support Vector Machines (SVM) and Near Infrared (NIR) imaging spectroscopy for the detection of meat and bone meat (MBM) in compound feeds. Journal of chemometrics, 18: (7-8), 341-349.
| Type | Journal Article |
| Year | 2004 |
| Title | Combination of Support Vector Machines (SVM) and Near Infrared (NIR) imaging spectroscopy for the detection of meat and bone meat (MBM) in compound feeds. |
| Journal | Journal of chemometrics |
| Label | U15-0489 |
| Edition | Journal Article |
| Recnumber | 325 |
| Volume | 18 |
| Issue | 7-8 |
| Pages | 341-349 |
| Date | 2004 |
| Type of article | scientifique, recherche |
| Endnote keywords | chemometrics |
| Endnote Keywords | Compound feeds, NIR, Imaging Spectroscopy, Chemometrics, PLS, ANN, SVM |
| Abstract | This study concerns the development of a new system to detect Meat and Bone Meal (MBM) in compound feed, which will be used to enforce legislation concerning feedstuffs enacted after the European mad cow crisis. Focal plane array near infrared (NIR) imaging spectroscopy, which collects thousands of spatially-resolved spectra in a massively-parallel fashion, has been suggested as a more efficient alternative to the current methods, with are tedious and require significant expert human analysis. Chemometric classification strategies have been applied to automate the method, and reduce the need for constant expert analysis of the data. In this work, the performance of a new method for multivariate classification, Support Vector Machines (SVM), was compared to classical chemometric methods such as Partial Least Squares (PLS), and Artificial Neural Networks (ANN), in classifying feed particles as either MBM or vegetal using the spectra from NIR images. While all three methods were able to effectively model the data, SVM was found to perform substantially better than PLS and ANN, exhibiting a much lower rate of false-positive detection. |
| Author address | Dardenne Pierre, Quality Department of Agro-food Products, Walloon Agricultural Research Centre (CRA-W), Chaussée de Namur, 24, B-5030 Gembloux, dardenne@cra.wallonie.be |
| Fichier | |
| Caption | U15-0489-fernandez-2004.pdf |
| Lien | http://dx.doi.org/10.1002/cem.877 |
| Authors | Baeten, V., Michotte Renier, A., Cogdill, R., Dardenne, P., Fernández Pierna, J.A. |
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