Spectral imaging is a new technique that combines conventional imaging and spectroscopy in a single system to obtain both spatial and spectral information simultaneously from an object. In this study, potential of hyperspectral imaging in the spectral range of 910-1700 nm was investigated for detecting adulteration in minced lamb meat. Spectral data were extracted to develop a partial least squares regression (PLSR) model to predict the level of adulteration in minced lamb. Good prediction model was obtained using the whole spectral range with a coefficient of determination (R2 CV) of 0.97 and root-mean-square errors estimated by cross validation (RMSECV) of 1.80%. Successive projection algorithm (SPA) was employed for optimal waveband selection. The PLSR model using only 7 optimum wavelengths (930, 1067, 1396, 1460, 1658, 1668, and 1702 nm) resulted in a coefficient of determination (R2 CV) of 0.97 and RMSECV of 1.84%. The study demonstrated the ability of the hyperspectral imaging as a rapid and alternative to the time-consuming and conventional methods to detect adulteration in minced lamb meat


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