|Sohn, Mi Ryeong|
|Archibald, D. - PENN STATE UNIVERSITY|
|Barton Ii, Franklin|
Submitted to: Cereal Chemistry
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: July 20, 2005
Publication Date: August 1, 2005
Citation: Sohn, M., Himmelsbach, D.S., Kays, S.E., Archibald, D.D., Barton II, F.E. 2005. Nir-ft/raman spectroscopy for nutritional classification of cereal foods. Cereal Chemistry. 82(6):660-665. Interpretive Summary: The classification of cereals for their nutritional components was accomplished using Raman spectroscopy and modeling software to separate the cereals into classes. One hundred twenty cereal based food samples were classified on the basis of their fiber, sugar, fat and protein contents. Samples were simply ground, placed in a sample tube and the spectra obtained in a little over five minutes time. Thus, this Raman spectroscopi method of product classification represents a rapid method by which to classify cereal foods based on their nutritional components.
Technical Abstract: The classification of cereals using near-infrared Fourier transform Raman (NIR-FT/Raman) spectroscopy was accomplished. One hundred twenty cereal based food samples were utilized in the study. Ground samples were scanned in low-iron NMR tubes with a 1064 nm (NIR) excitation laser using 500mW 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. Soft independent modeling of class analogy (SIMCA) and partial least square (PLS) regression based classification were used to classify the samples according to high and low content of each component using the spectral variables. PCA results suggested that the classification of a target component is subject to interference by other components in cereal. The x-variables most responsible for classification of each component were 1600-1630 cm-1 for TDF, 1440 and 2853 cm-1 for fat, 2910 and 1660 cm-1 for protein and 401 and 848 cm-1 for sugar. The use of selected x-variables for each component produced better results than the use of the whole spectral region in both SIMCA and PLS based classification. PLS based classification was more precise than SIMCA for all four components, resulting in correct classification of samples 85-95% of the time. NIR-FT/Raman spectroscopy represents a rapid and reliable method by which to classify cereal foods based on their nutritional components.