Application of Vis–NIR hyperspectral imaging in classification between fresh and frozen-thawed pork Longissimus Dorsi muscles |
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Affiliation: | 1. Graduate School of Agricultural & Life Sciences, The University of Tokyo, Japan;2. Department of Food Technology and Rural Industries, Faculty of Agricultural Engineering & Technology, Bangladesh Agricultural University, Mymensingh 2202, Bangladesh |
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Abstract: | Fresh and frozen-thawed (F-T) pork meats were classified by Vis–NIR hyperspectral imaging. Eight optimal wavelengths (624, 673, 460, 588, 583, 448, 552 and 609 nm) were selected by successive projections algorithm (SPA). The first three principal components (PCs) obtained by principal component analysis (PCA) accounted for over 99.98% of variance. Gray-level-gradient co-occurrence matrix (GLGCM) was applied to extract 45 textural features from the PC images. The correct classification rate (CCR) was employed to evaluate the performance of the partial least squares-discriminate analysis (PLS-DA) models, by using (A) the reflected spectra at full wavelengths and (B) those at the optimal wavelengths, (C) the extracted textures based on the PC images, and (D) the fused variables combining spectra at the optimal wavelengths and textures. The results showed that the best CCR of 97.73% was achieved by applying (D), confirming the high potential of textures for fresh and F-T meat discrimination. |
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Keywords: | Hyperspectral imaging Pork Fresh Frozen-thawed Successive projections algorithm Gray-level-gradient co-occurrence matrix Partial least squares-discriminate analysis Correct classification rate Imagerie hyperspectrale Porc Frais Congelé puis décongelé Algorithme de projections successives Matrice de co-occurrence des gradients de niveau de gris Analyse discriminante par les moindres carrés Vitesse de classification correcte |
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