Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
6398211 | Food Research International | 2013 | 13 Pages |
Abstract
In the meat industry the fat portions coming from two different subcutaneous layers, i.e., inner and outer, are destined to the manufacturing of different products, hence the availability of cheap, rapid and affordable methods for the characterization of the overall fat quality is desirable. In this work the potential usefulness of three techniques, i.e. tristimulus colorimetry, FT-NIR spectroscopy and NIR hyperspectral imaging, were tested to rapidly discriminate fat samples coming from the two different layers. To this aim, various multivariate classification methods were used, also including signal processing and feature selection techniques. The classification efficiency in prediction obtained using colorimetric data did not reach excellent results (78.1%); conversely, the NIR-based spectroscopic methods gave much more satisfactory models, since they allowed to reach a prediction efficiency higher than 95%. In general, the samples of the outer layer showed a high degree of variability with respect to the samples of the inner layer. This is probably due to a greater variability of the outer samples in terms of fatty acid composition and water amount.
Keywords
SYMFOPWPTVIPHSITRNMSCFT-NIRROIdet1RMSECVPLS-DANIRSNVPCAS/NEffCross validationVariable selectionvariable importance in projectionOUTWavelet packet transformpartial least squares-discriminant analysisPrincipal component analysisHyperspectral imagingIntegrating sphereTristimulus colorimetrySignal to noiseMultivariate classificationFT-NIR spectroscopyNear infraredlatent variableregion of interestprincipal componentFiber optic probe
Related Topics
Life Sciences
Agricultural and Biological Sciences
Food Science
Authors
Giorgia Foca, Davide Salvo, Adelaide Cino, Carlotta Ferrari, Domenico Pietro Lo Fiego, Giovanna Minelli, Alessandro Ulrici,