A Hierarchical Discriminant Analysis for Species Identification in Raw Meat by Visible and near Infrared Spectroscopy
| Autor: | John McElhinney, Gerard Downey, Tom Fearn, Thorsteinn Arnalds |
|---|---|
| Rok vydání: | 2004 |
| Předmět: |
010401 analytical chemistry
Analytical chemistry food and beverages Visible and near infrared spectroscopy 04 agricultural and veterinary sciences Linear discriminant analysis 040401 food science 01 natural sciences 0104 chemical sciences Chemometrics 0404 agricultural biotechnology Near infrared spectra Statistics Principal component analysis Red meat Species identification Raw meat Spectroscopy Mathematics |
| Zdroj: | Journal of Near Infrared Spectroscopy. 12:183-188 |
| ISSN: | 1751-6552 0967-0335 |
| DOI: | 10.1255/jnirs.425 |
| Popis: | The problem tackled is that of discriminating between chicken, turkey, pork, beef and lamb using visible and near infrared spectra collected from homogenised meat samples. This five-group classification task was treated as a hierarchical sequence of binary splits: white versus red meat, then either poultry versus pork or beef versus lamb, and finally, in the case of poultry, chicken versus turkey. Most of the splits were achieved by linear discriminant analysis applied to principal component scores. The chicken versus turkey split worked better with soft independent modelling of class analogy (SIMCA). One of the attractions of the hierarchical approach is that different methods can be used for different splits. The results, with only two classification errors on 115 validation samples, were better than those achieved in previous analyses of the same data. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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