Near-infrared (NIR) prediction of trans-fatty acids in ground cereal foods.

Near-infrared (NIR) reflectance spectroscopy was evaluated as a rapid method for prediction of trans-fatty acid content in ground cereal products without the need for oil extraction. NIR spectra (400-2498 nm) of ground cereal products were obtained with a dispersive NIR spectrometer and correlated to trans- and cis-fatty acid content determined by a modification of AOAC Method 996.01. Partial least-squares regression and Marten's uncertainty test were applied to calculate models for prediction of trans-fatty acids using spectral regions affected by lipid absorption. The best model (n = 84) for trans-fat prediction used the 700-2498 nm region and second-derivative processing of spectra. When used to predict test samples (n = 27) the model had an RPD of 4.8 with a standard error of performance of 0.70% (range of 0.05-11.74%) and r(2) of 0.97. Optimum models for cis-fatty acids were developed with the 1100-2498 and 700-2498 nm ranges and had an RPD of 4.0. Regression coefficients indicated that useful absorbance for prediction of trans- and cis-fatty acids was in the overtone and combination regions for lipid absorption.

Synthesis and characterization of canola oil-stearic acid-based trans-free structured lipids for possible margarine application.

Incorporation of stearic acid into canola oil to produce trans-free structured lipid (SL) as a healthy alternative to partially hydrogenated fats for margarine formulation was investigated. Response surface methodology was used to study the effects of lipozyme RM IM from Rhizomucor miehei and Candida rugosa lipase isoform 1 (LIP1) and two acyl donors, stearic acid and ethyl stearate, on the incorporation. Lipozyme RM IM and ethyl stearate gave the best result. Gram quantities of SLs were synthesized using lipozyme RM IM, and the products were compared to SL made by chemical catalysis and fat from commercial margarines. After short-path distillation, the products were characterized by GC and RPHPLC-MS to obtain fatty acid and triacylglycerol profiles, 13C NMR spectrometry for regiospecific analysis, X-ray diffraction for crystal forms, and DSC for melting profile. Stearic acid was incorporated into canola oil, mainly at the sn-1,3 positions, for the lipase reaction, and no new trans fatty acids formed. Most SL products did not have adequate solid fat content or beta' crystal forms for tub margarine, although these may be suitable for light margarine formulation.

ATR-Fourier transform mid-infrared spectroscopy for determination of trans fatty acids in ground cereal products without oil extraction.

Fourier transform mid-infrared (FT-IR) spectroscopy was investigated as a method of analysis for trans fatty acid content of cereal products without the need for prior oil extraction. Spectra were obtained, with an FT-IR spectrometer equipped with an attenuated total reflectance (ATR) device, of ground samples pressed onto the diamond ATR surface, and trans fatty acids were measured by a modification of AOAC Method 996.01. Partial least-squares (PLS) models were developed for the prediction of trans fatty acids in ground samples using several wavenumber selections on the basis of bands related to lipids. The models (n = 79) predicted trans fatty acids in ground samples with standard error of cross-validation (SECV) of 1.10-1.25 (range 0-12.4) % and R2 of 0.85-0.88 and in validation samples (n = 26) with standard error of performance (SEP) of 0.96-1.12 (range 0-12.2) % and r2 of 0.89-0.92, indicating sufficient accuracy for screening. Sample trans fatty acid % was predicted as accurately with the fingerprint region (1500-900 cm(-1)) as with the entire range (4000-650 cm(-1)) indicating, in concert with the regression coefficients, the importance of the isolated trans double bonds at 966 cm(-1) in development of the model. Data is also presented on prediction of trans fatty acids using the spectra of residual oil films on the ATR surface after removing the solid portion of the sample.

Near-infrared spectroscopy for measurement of total dietary fiber in homogenized meals.

Near-infrared (NIR) reflectance spectroscopy was investigated as a method for prediction of total dietary fiber (TDF) in mixed meals. Meals were prepared for spectral analysis by homogenization only (HO), homogenization and drying (HD), and homogenization, drying, and defatting (HDF). The NIR spectra (400-2498 nm) were obtained with a dispersive NIR spectrometer. Total dietary fiber was determined in HDF samples by an enzymatic-gravimetric assay (AOAC 991.43), and values were calculated for HD and HO samples. Using multivariate analysis software and optimum processing, partial least squares models (n = 114) were developed to relate NIR spectra to the corresponding TDF values. The HO, HD, and HDF models predicted TDF in independent validation samples (n = 37) with a standard error of performance of 0.93% (range 0.7-8.4%), 1.90% (range 2.2-18.9%), and 1.45% (range 2.8-23.3%) and r(2) values of 0.89, 0.92, and 0.97, respectively. Compared with traditional analysis of TDF in mixed meals, which takes 4 days, NIR spectroscopy provides a faster method for screening samples for TDF. The accuracy of prediction was greatest for the HDF model followed by the HD model.

Near-infrared reflectance model for the rapid prediction of total fat in cereal foods.

AOAC method 996.01, used in cereal foods to determine total fat as defined by the U.S. Nutrition Labeling and Education Act (NLEA), is laborious and time-consuming and utilizes hazardous chemicals. Near-infrared (NIR) reflectance spectroscopy, a rapid and environmentally benign technique, was investigated as a potential method for the prediction of total fat using AOAC method 996.01 as the reference method. Near-infrared reflectance spectra (1104-2494 nm) of ground cereal products (n = 72) were obtained using a dispersive spectrometer, and total fat was determined according to AOAC method 996.01. Using multivariate analysis, a modified partial least-squares model was developed for total fat prediction. The model had a SECV of 1.12% (range = 0.5-43.2%) and a multiple coefficient of determination of 0.99. The model was tested with independent validation samples (n = 36); all samples were predicted within NLEA accuracy guidelines. The results indicate that NIR reflectance spectroscopy is an accurate means of determining the total fat content of diverse cereal products for nutrition labeling.

Energy from fat determined by near-infrared reflectance spectroscopy.

The amount of energy derived from fat in foods is a requirement of U.S. nutrition labeling legislation and a significant factor in diet development by health professionals. Near-infrared (NIR) spectroscopy has been used to predict total utilizable energy in cereal foods for nutrition labeling purposes, and in the current study, was investigated as a method for evaluation of the amount of energy derived from fat. Using NIR reflectance spectra (1104-2494 nm) of ground cereal samples and reference values obtained by calorimetry and by calculation, modified PLS regression models were developed for the prediction of percent energy from fat and energy from fat/g. The models were able to predict the percent of utilizable energy derived from fat with SECV and R(2) of 1.86-1.89% of kcal (n = 51, range 0-43.0) and 0.98, respectively, and SEP and r(2) of 1.74% of kcal (n = 55, range 0-38.0) and 0.98, respectively, when used to predict independent validation samples. Results indicate that NIR spectroscopy provides useful methods for predicting the energy derived from fat in food products.

Near-infrared analysis of soluble and insoluble dietary fiber fractions of cereal food products.

The use of near-infrared (NIR) reflectance spectroscopy for the rapid and accurate measurement of soluble and insoluble dietary fiber was explored in a diverse group of cereal products. Ground samples were analyzed for soluble and insoluble dietary fiber (AOAC Method 991.43) and scanned (NIRSystems 6500 monochromator) to obtain NIR spectra. Modified PLS models were developed to predict insoluble and soluble dietary fiber using data sets expanded to include products with high fat and high sugar contents. The models predicted insoluble dietary fiber accurately with an SECV of 1.54% and an R(2) of 0.98 (AOAC determined range of 0-48.77%) and soluble dietary fiber less accurately with an SECV of 1.15% and an R(2) of 0.82 (AOAC determined range of 0-13.84%). Prediction of independent validation samples by the soluble fiber model resulted in a bias that may be related to the way the reference method treats samples with different soluble fiber constituents. The insoluble fiber model can be used to rapidly monitor insoluble dietary fiber in cereal products for nutrition labeling.

Rapid prediction of gross energy and utilizable energy in cereal food products using near-infrared reflectance spectroscopy.

Near-infrared (NIR) spectroscopy has been used in foods for the rapid assessment of several macronutrients; however, little is known about its potential for the evaluation of the utilizable energy of foods. Using NIR reflectance spectra (1104-2494 nm) of ground cereal products (n = 127) and values for energy measured by bomb calorimetry, chemometric models were developed for the prediction of gross energy and available energy of diverse cereal food products. Standard errors of cross-validation for NIR prediction of gross energy (range = 4.05-5.49 kcal/g), energy of samples after adjustment for unutilized protein (range = 3.99-5.38 kcal/g), and energy of samples after adjustment for unutilized protein and insoluble dietary fiber (range = 2.42-5.35 kcal/g) were 0.053, 0.053, and 0.088 kcal/g, respectively, with multiple coefficients of determination of 0.96. Use of the models on independent validation samples (n = 58) gave energy values within the accuracy required for U.S. nutrition labeling legislation. NIR spectroscopy, thus, provides a rapid and accurate method for predicting the energy of diverse cereal foods.