|Barton Ii, Franklin|
Submitted to: Federation of Analytical Chemistry and Spectroscopy Societies Final Program
Publication Type: Abstract Only
Publication Acceptance Date: 7/26/1996
Publication Date: N/A
Citation: N/A Interpretive Summary:
Technical Abstract: Measurement of dietary fiber is laborious and time consuming. Food labeling legislation has increased the need for more rapid and efficient methods of analyzing dietary fiber. A near-infrared (NIR) model was previously developed to predict total dietary fiber (TDF) in dry milled cereal products (N=90, range in TDF=<1-52%), using partial least squares regression. The standard error of cross validation (SECV) and multiple coefficient of determination were 1.58% and 0.99, respectively. The standard error of performance (SEP) and coefficient of determination for the prediction of TDF in an independent set of cereal samples (N=29) were 1.51% and 0.99, respectively. In the present study the model was expanded to include cereal products with high fat (>10% fat, N=20) and high sugar (>20% sugar, N=39). The SECV and multiple coefficient of determination of the new model were 1.70% and 0.98, with three factors explaining 92% of the variation. The model was validated using the original independent validation set combined with high fat (N=11) and high sugar (N=10) cereal samples (total N=50). The SEP was 1.45% and coefficient of determination was 0.98 for prediction of TDF in the new independent validation set. Thus, the NIR model for prediction of TDF in cereal and grain products has been expanded to include samples with high fat and high sugar content without loss in prediction accuracy.