DOI: 10.1094/CC-82-0660 |  VIEW ARTICLE

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

Miryeong Sohn (1,2), David S. Himmelsbach (1), Sandra E. Kays (1), Douglas D. Archibald (3), and Franklin E. Barton, II (1). (1) United States Department of Agriculture, Agricultural Research Service, R. B. Russell Agricultural Research Center, Athens, GA 30605. (2) Corresponding author. Phone: (706) 546-3374. Fax: (706) 546-3607. E-mail: <msohn@qaru.ars.usda.gov> (3) Department of Crop and Soil Sciences, Penn State University, University Park, PA 16802. Cereal Chem. 82(6):660-665. Accepted July 20, 2005. This article is in the public domain and not copyrightable. It may be freely reprinted with customary crediting of the source. AACC International, Inc., 2005.

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 (LN(2)) 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 in cereal. The Raman shifts that were 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 the selected spectral region (frequency region) for each component produced better results than the use of the entire region in both SIMCA and PLS-based classifications. PLS-based classification performed better 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.