THE ADVANCEMENT OF SPECTROSCOPIC SENSORS/CHEMOMETRIC ANALYSIS/BIOBASED PRODUCTS FOR QUALITY ASSESSMENT OF FIBER, GRAIN, AND FOOD COMMODITIES
Location: Quality and Safety Assessment Research Unit
Title: NIR analysis of lipid classes in processed cereal products
| Sohn, M. - UGA |
| Kim, Y. - KOREA UNIVERSITY |
| Vines, L. - MICHIGAN ST. UNIVERSITY |
| Kays, Sandra |
Submitted to: Near Infrared Spectroscopy Journal
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: June 2, 2009
Publication Date: June 9, 2009
Citation: Sohn, M., Kim, Y., Vines, L.L., Kays, S.E. 2009. NIR analysis of lipid classes in processed cereal products. Near Infrared Spectroscopy Journal. 17(3):127-133.
Interpretive Summary: The amounts of total fat and saturated, polyunsaturated and monounsaturated fatty acids in the diet can have major health implications. As a consequence the content of these lipids in foods is of interest to consumers and health professionals. The content of total fat and lipid classes in cereal foods is traditionally determined using an official reference method (AOAC Method 996.01), which is very labor intensive, costly, time consuming, and requires the use and disposal of hazardous organic solvents. It has been shown previously that total fat can be assessed rapidly and accurately by NIR reflectance spectroscopy in processed cereal food products. Near-infrared (NIR) reflectance spectroscopy is a technique that measures the amount of light energy, in a specific region of the electromagnetic spectrum, that is reflected by a substance and the light energy is related to a measured component of the substance by mathematical modeling. The mathematical model is then used to predict the parameter in samples with unknown quantities of the parameter. The technique is rapid and does not require the use, or disposal, of chemicals. The objectives of this study were to develop models, using NIR, for the prediction of lipid classes in processed cereal products. Models were developed to predict saturated, monounsaturated and polyunsaturated fatty acids in cereal products containing a wide range of fat, grain types, food additives, and processing methods. The test samples were predicted with sufficient accuracy for quality control of polyunsaturated fat and for screening samples for saturated and monounsaturated fat. The NIR reflectance models developed can be used by the food industry, commercial analysis laboratories and regulatory agencies responsible for monitoring the composition of foods.
Previous work showed total fat can be assessed rapidly and accurately by 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 (MPLS) 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).