Abstract

Previous work showed total fat can be assessed rapidly and accurately by near infrared (NIR) reflectance spectroscopy in processed cereal food products. In this study, the potential of NIR spectroscopy for the rapid measurement of saturated, monounsaturated and polyunsaturated fat was investigated. Fatty acid composition was determined in ground cereal products using a modification of AOAC Method 996.01 and reflectance spectra obtained with a dispersive NIR instrument. Modified partial least squares models were calculated for the prediction of lipid classes using multivariate analysis software. Models predicted saturated, monounsaturated and polyunsaturated fatty acids in separate validation samples with sufficient accuracy for screening samples (RPDs of 3.5–4.2).

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