NIR-FT/Raman Spectroscopy for Nutritional Classification of Cereal Foods

ABSTRACT The classification of cereals using near-infrared Fourier transform Raman (NIR-FT/Raman) spectroscopy was accomplished. Cereal-based food samples (n = 120) were utilized in the study. Ground samples were scanned in low-iron NMR tubes with a 1064 nm (NIR) excitation laser using 500 mW of power. Raman scatter was collected using a Ge (LN2) detector over the Raman shift range of 202.45~3399.89 cm-1. Samples were classified based on their primary nutritional components (total dietary fiber [TDF], fat, protein, and sugar) using principle component analysis (PCA) to extract the main information. Samples were classified according to high and low content of each component using the spectral variables. Both soft independent modeling of class analogy (SIMCA) and partial least squares (PLS) regression based classification were investigated to determine which technique was the most appropriate. PCA results suggested that the classification of a target component is subject to interference by other components ...

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