Prediction of Total Dietary Fiber by Near-Infrared Reflectance Spectroscopy in High-Fat- and High-Sugar-Containing Cereal Products

Simultaneous determination of constituents (e.g. dietary fibre, protein, fat) by near infrared (NIR) spectroscopy would increase the speed and efficiency of nutrient analysis while substantially reducing the cost. Previous work has described the development of NIR reflectance models for the prediction of dietary fibre in a diverse group of cereal food products. While NIR spectroscopy has been used to measure protein content in cereal samples comprised of a single grain type, the utility of the NIR technique would be greatly improved if it could be expanded to cereal products derived from a diverse cross-section of grains and formulations. The present study was conducted to investigate the potential of NIR spectroscopy for the analysis of protein in a data set that included products with numerous grains, such as wheat, oats, rice, rye, corn, millet, buckwheat and with a wide range of fat, sugar and fibre contents. In addition, numerous processing techniques and food additives were represented in the data set. Nitrogen content of dry-milled cereal products was measured by combustion analysis (AOAC Method 992.23) and the range in nitrogen values was from 0.65 to 3.31% of dry weight. Milled cereal products were scanned from 1100 to 2500nm with a scanning monochromator. A nitrogen calibration was developed, using a commercial analysis program, with modified partial least squares as the regression method. The standard error of cross validation and R2 for nitrogen ( n=147 calibration samples) were 0.090% and 0.973, respectively. Independent validation samples ( n=72) were predicted with a standard error of performance of 0.079% nitrogen and r2 of 0.984. Because of the diversity of grains in the data set, crude protein was calculated using two nitrogen-to-protein conversion methods and two PLS models were developed for the prediction of crude protein. Crude protein was predicted with a similar precision to nitrogen and the results for both protein models are within the precision required for US nutrition labelling legislation. In conclusion, NIR reflectance spectroscopy can be used for rapid and accurate prediction of nitrogen and crude protein content in a heterogeneous group of cereal products comprised of a wide cross-section of grains and formulations.

The insoluble and soluble fractions of dietary fibre have different human physiological effects and their presence in foods is of interest to consumers, the medical community and the cereal product industry. The development of a model, using near infrared (NIR) reflectance spectroscopy, to predict insoluble dietary fibre in a wide range of dry-milled cereal products and grains is described. The products included breakfast cereals, crackers, brans, pastas and flours. Insoluble dietary fibre was measured by the AOAC enzymatic–gravimetric procedure (AOAC 991.43). The range in insoluble dietary fibre was 0–48%. Near infrared reflectance spectra were obtained with a scanning monochromator and data analysed with a commercial analysis program. A calibration ( n = 90) was developed for prediction of insoluble dietary fibre using preprocessed spectra and modified partial least squares regression. The standard error of cross validation and R2 were 1.34% and 0.99, respectively. The model was tested with independent validation samples ( n = 32) and the resulting standard error of performance and r2 were 1.13% insoluble dietary fibre and 0.99, respectively. The results show that NIR spectroscopy can be used to predict the insoluble dietary fibre content in a wide variety of processed and unprocessed cereal products.

Crystalline sugar has unique spectral characteristics that influence the near-infrared (NIR) region of the spectrum often used to assess product composition. The current study investigated the potential of expanding an existing NIR spectroscopic calibration for the prediction of total dietary fiber in cereal products to include products with high sugar and crystalline sugar content. Using AOAC Procedure 991.43 as the reference method, an NIRSystems monochromator and ISI software for scanning and data analysis, and a selection algorithm to select representative high-sugar samples, a sugar-expanded partial least-squares model (n = 100) was developed for prediction of dietary fiber. The standard error of cross-validation, R2, standard error of performance (n = 45 independent validation samples), and r2 were 1.88%, 0.98, 1.40%, and 0.99, respectively. The NIR model for prediction of total dietary fiber in cereal products was, thus, expanded to include samples with high sugar and crystalline sugar content. Key...

The Nutritional Labeling and Education Act of 1990 has increased the need for more rapid, efficient, and nonpolluting techniques of analyzing the nutrients in foods, particularly dietary fiber. The use of near-infrared spectroscopy (NIRS) for the prediction of total dietary fiber content of cereal products was investigated in this study. Cereal and grain products, including breakfast cereals, flours, brans, crackers, and samples containing commercial oat and wheat fibers, were selected for analysis. Products (n = 91) were dry milled, and total dietary fiber was measured by the AOAC (991.43) enzymatic-gravimetric method. Total dietary fiber values ranged from <1 to 52% of dry weight. Milled cereal products (n = 91) were scanned from 1100 to 2498 nm with a NIRSystems 6500 monochromator. Using ISI software for scanning and data analysis, a dietary fiber calibration was obtained with partial least squares as the regression method. The standard error of cross validation and multiple coefficient of determination were 1.58% and 0.99, respectively. For equation validation, an independent group of cereal products (n = 31) was dry milled, and total dietary fiber was determined. The samples were scanned, and total dietary fiber was predicted by NIRS. Samples were predicted with a standard error of performance of 1.51%, coefficient of determination of 0.99, bias of −0.38, and slope of 1.06. This study shows that NIRS can be used to rapidly and accurately predict total dietary fiber content in a wide range of cereal products. Keywords: Dietary fiber; near-infrared spectroscopy; nutrition labeling

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.

Published studies on the applications of near-infrared reflectance spectroscopy (NIRS) to the analysis of fiber in forages, feeds, grains, and cereal products indicate the presence of O-H absorbance, due to sample moisture content, in the calibration models. The objective of this study was to determine the extent to which residual moisture in samples interferes with the ability of NIRS to predict total dietary fiber (TDF) in cereal products and grains. Milled cereal products and grains were stored in 20%, 60%, and 80% experimental relative humidity (rh) environments and a vacuum oven. Samples (N = 143) were analyzed for moisture and predicted for TDF. Results showed significant differences between laboratory reference TDF and predicted TDF for samples that were either very low or very high in moisture. Cereal products and grain samples stored under ambient conditions (N = 90) were combined with selected samples stored under different rh environments (N = 53) to develop a new calibration using partial least squares regression. The standard error of cross validation and multiple coefficient of determination (R2) were 1.85% and 0.98, respectively. The model was validated with an independent set of cereal products (N = 29) stored under ambient and rh environments. Samples stored under ambient and rh environments were predicted with standard errors of performance of 1.70% and 1.86%, respectively. The study shows that NIRS can be used to predict TDF in cereal products and grains with a wide range of residual moistures when calibrations include the range of residual moisture expected. Keywords: Dietary fiber; near-infrared spectroscopy; moisture

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.

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.

Chapter 11 - Cereals and Cereal Products

The use of black soldier fly larvae (BSFL) grown on different organic waste streams as a source of feed ingredient is becoming very popular in several regions across the globe. However, information about the easy-to-use methods to monitor the safety of BSFL is a major step limiting the commercialization of this source of protein. This study investigated the ability of near infrared (NIR) spectroscopy combined with chemometrics to predict yeast and mould counts (YMC) in the feed, larvae, and the residual frass. Partial least squares (PLS) regression was employed to predict the YMC in the feed, frass, and BSFL samples analyzed using NIR spectroscopy. The coefficient of determination in cross validation (R2CV) and the standard error in cross validation (SECV) obtained for the prediction of YMC for feed were (R2cv: 0.98 and SECV: 0.20), frass (R2cv: 0.81 and SECV: 0.90), larvae (R2cv: 0.91 and SECV: 0.27), and the combined set (R2cv: 0.74 and SECV: 0.82). However, the standard error of prediction (SEP) was considered moderate (range from 0.45 to 1.03). This study suggested that NIR spectroscopy could be utilized in commercial BSFL production facilities to monitor YMC in the feed and assist in the selection of suitable processing methods and control systems for either feed or larvae quality control.

Shortwave-near infrared spectroscopy for determination of reducing sugar content during grape ripening, winemaking, and aging of white and red wines

The potential of Fourier transform infrared (FT-IR), near-infrared (NIR), and Raman spectroscopic techniques combined with partial least squares (PLS) regression (PLSR) to predict concentrations of eicosapentaenoic acid (EPA), docosahexaenoic acid (DHA), and total omega-3 fatty acids (n-3 FAs) in fish oil supplements was investigated. FT-IR spectroscopy predicted EPA (coefficient of determination (R(2)) of 0.994, standard error of cross-validation (SECV) of 2.90%, and standard error of prediction (SEP) of 2.49%) and DHA (R(2) = 0.983, SECV = 2.89%, and SEP = 2.55%) with six to seven PLS factors, whereas a simpler PLS model with two factors was obtained for total n-3 FAs (R(2) = 0.985, SECV = 2.73%, and SEP = 2.75%). Selected regions in the NIR spectra gave models with good performances and predicted EPA (R(2) = 0.979, SECV = 2.43%, and SEP = 3.11%) and DHA (R(2) = 0.972, SECV = 2.34%, and SEP = 2.60%) with four to six PLS factors. Both the whole and selected NIR regions gave simple models (two PLS factors) with similar results (R(2) = 0.997, SECV = 2.18%, and SEP = 1.60%) for total n-3 FAs. The whole and selected regions of Raman spectra provided models with comparable results and predicted EPA (R(2) = 0.977, SECV = 3.18%, and SEP = 2.73%) and DHA (R(2) = 0.966, SECV = 3.31%, and SEP = 2.56%) with seven to eight PLS factors, whereas a simpler model (three PLS factors) with R(2) = 0.993, SECV = 2.82%, and SEP = 3.27% was obtained for total n-3 FAs. The results demonstrated that FT-IR, NIR, and Raman spectroscopy combined with PLSR can be used as simple, fast, and nondestructive methods for quantitative analysis of EPA, DHA, and total n-3 FAs. FT-IR and NIR spectroscopy, in particular, have the potential to be applied in process industries during production of fish oil supplements.

This study was conducted to evaluate the ability of near infrared (NIR) spectroscopy to estimate oil content, and per cent of cake, resin and residue in beauty leaf tree ( Calophyllum inophyllum L.) kernel samples. Fruits were collected from various geographical locations of tropical Australia (from Rockhampton to Darwin) and air dried before the kernels were manually separated from the fruits. Kernel samples were oven dried, crushed (5–10 mm) and their NIR spectra collected using a Fourier transform (FT) NIR instrument where the same batch of kernels were used to extract oil using a screw press. Calibration models between the NIR spectra and reference data were developed using partial least squares (PLS) regression. The cross-validation statistics including the coefficient of determination (r2) and standard error in cross validation (SECV) were 0.83 (SECV: 2.39%) for oil content, 0.89 (SECV: 2.81%) for cake, 0.88 (SECV: 1.92%) for resin and 0.79 (SECV: 2.15%) for residue, respectively. This research showed that NIR spectroscopy can be used as an alternative, faster and low-cost technique to predict oil content, per cent of cake, resins and residues in various genotypes of beauty leaf tree. Further studies should be carried out to increase the sample size and chemical variation, as well as to evaluate different methods of oil extraction (e.g., solvent extraction) to improve the reliability of the calibration models.

Volatile chemical compounds responsible for the aroma of wine are derived from a number of different biochemical and chemical pathways. These chemical compounds are formed during grape berry metabolism, crushing of the berries, fermentation processes (i.e. yeast and malolactic bacteria) and also from the ageing and storage of wine. Not surprisingly, there are a large number of chemical classes of compounds found in wine which are present at varying concentrations (ng L(-1) to mg L(-1)), exhibit differing potencies, and have a broad range of volatilities and boiling points. The aim of this work was to investigate the potential use of near infrared (NIR) spectroscopy combined with chemometrics as a rapid and low-cost technique to measure volatile compounds in Riesling wines. Samples of commercial Riesling wine were analyzed using an NIR instrument and volatile compounds by gas chromatography (GC) coupled with selected ion monitoring mass spectrometry. Correlation between the NIR and GC data were developed using partial least-squares (PLS) regression with full cross validation (leave one out). Coefficients of determination in cross validation (R(2)) and the standard error in cross validation (SECV) were 0.74 (SECV: 313.6 microg L(-1)) for esters, 0.90 (SECV: 20.9 microg L(-1)) for monoterpenes and 0.80 (SECV: 1658 microg L(-1)) for short-chain fatty acids. This study has shown that volatile chemical compounds present in wine can be measured by NIR spectroscopy. Further development with larger data sets will be required to test the predictive ability of the NIR calibration models developed.

Near-infrared reflectance spectra of cereal food products were acquired with a commercial dual-diode-array (Si, InGaAs) spectrometer customized to allow rapid acquisition of scans of intact breakfast cereals, snack foods, whole grains, and milled products. Substantial gains in the performance of multivariate calibration models generated from these data were obtained by a computational strategy that systematically analyzed the performance of various spectral windows. The calibration model based on 137 cereal food products determined the total dietary fiber (TDF) content of a test set of 45 intact diverse cereal food products with root-mean-squared error of cross-validation of between 1.8 and 2.0% TDF, relative to the laborious enzymatic-gravimetric reference method. The calibration performance is adequate to estimate TDF over the range of values found in diverse types of cereal food products (0.7-50.1%). The method requires no sample preparation and is relatively unaffected by specimen moisture content.