Potential of hyperspectral imaging and pattern recognition for categorization and authentication of red meat

In this study, the reliability and accuracy of hyperspectral imaging technique in tandem with multivariate analyses were investigated for identification and authentication of different red meat species. Hyperspectral images were acquired from longissimus dorsi muscle of pork, beef and lamb and their spectral data were extracted and analyzed by principal component analysis (PCA) and partial least squares discriminant analysis (PLS-DA) for recognition and authentication of the tested meat. The spectra were pre-treated by second derivative and six wavelengths (957, 1071, 1121, 1144, 1368 and 1394 nm) were identified as important wavelengths from the 2nd derivative spectra. The resulting wavelengths were used in a pattern recognition algorithms for classification of meat samples with PLS-DA yielding 98.67% overall classification accuracy in the validation sets. The developed classification algorithms were then successfully applied in the independent testing set for the authentication of minced meat. The results clearly showed that the combination of hyperspectral imaging, multivariate analysis and image processing has a great potential as an objective and rapid method for identification and authentication of red meat species. Industrial Relevance: This study was carried out to investigate the potential of NIR hyperspectral imaging system for identification and authentication of red meat species for the meat industry.