Sandra E. Kays

Raman and NIR Spectroscopic Methods for Determination of Total Dietary Fiber in Cereal Foods: Utilizing Model Differences

This work evaluates the complementarity in the predictive ability of three Raman and three near-infrared reflectance (NIRR) partial least-squares regression (PLSR) models for total dietary fiber (TDF) determinations of a diverse set of ground cereal food products. For each spectral type (R or N), models had previously been developed from smoothed (D0), first-derivative (D1), or second-derivative (D2) spectral data. The NIRR and Raman models tend to have very different sets of outliers and uncorrelated errors in TDF determination. For a single spectral type, the prediction errors of various preprocessing methods are partially complementary. The samples are very diverse in terms of composition, but the main problem groups were narrowed to high-fat, high-bran, and high-germ samples, as well as and those containing synthetic fiber additives. Raman models perform better on the high-fat samples, while NIRR models perform better with high-bran and high-synthetic samples. Raman models were better able to accommodate a wheat germ sample, even though this sample type was poorly represented by the calibration set. Two methods are presented for utilizing the complementarity of the spectral and processing techniques: one involves simple averaging of predictions and the other involves avoidance of outliers by using statistics generated from the sample spectrum to choose the best model(s) for determination of the TDF value. The single best model (N-D1) has a root-mean-squared prediction error of 2.4% TDF. The best model of prediction averages yields an error of 1.9% (combining N-D0, N-D1, N-D2, R-D0, and R-D1). An error of 1.9% was also obtained by choosing a single prediction from the six models by using statistics to avoid outliers. With the selection of the best three models and averaging their predictions, an error of 1.5% was achieved.

NIR-FT/Raman spectroscopy for nutritional classification of cereal foods

ABSTRACT 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 (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. 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 ...

Near Infrared Analysis of Lipid Classes in Processed Cereal Products

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).

Fourier Transform near Infrared Spectroscopy Prediction of trans and cis Fats in Ground Cereal Products at Different Resolutions

In this study, Fourier transform near infrared (FT-NIR) spectroscopy was investigated for use as a tool to determine the trans and cis fat contents in ground cereal products without the need for further oil extraction. To compare the calibration results obtained at different resolutions, the near infrared (NIR) spectra of samples were obtained using an FT-NIR spectrometer at resolutions of 4 cm−1, 8 cm−1 and 16 cm−1. Fat contents of samples were determined using a gas chromatography method. Generally, higher resolution provides better predictions for all types of fats. Each type of fat had its own optimum resolution: 4 cm−1 for trans and 8 cm−1 for cis fat models. At optimal resolution, the models predicted trans and cis fat contents with a SEP and r2 of 0.75% and 0.96, and 0.70% and 0.96, respectively. The results indicated that the trans and cis fat content of cereal products could be determined in minutes without the need for oil extraction within the accuracy required for sample screening (RPD=4.3 or 4.8).