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Abstract
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