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.     

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.     

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.     

At-line near-infrared spectroscopy for prediction of the solid fat content of milk fat from New Zealand butter

Near-infrared (NIR) spectroscopy calibrations that will allow prediction of the solid fat content (SFC) of milk fat extracted from butter by one measurement during manufacture were developed. SFC is a measure of the amount of the solid fraction of fat crystallized at a temperature expressed as a percentage (w/w). At-line SFC determinations are currently performed by nuclear magnetic resonance (NMR) spectroscopy, which involves a 16 h delay period for tempering of the milk fat at 0 degrees C prior to the SFC measurements, from 0 to 35 degrees C in a series of 5 degrees C increments. The NIR spectra (400-2500 nm) were obtained using a sample holder maintained at 60 degrees C. Accurate predictions for the SFC (%) were developed by principal component analysis (PCA) and partial least-squares (PLS) regression models to relate the NIR spectra to the corresponding NMR values. The independent validation samples (N = 22) had a standard error of prediction (SEP) of 0.385-0.762% for SFC between 0 and 25 degrees C, with SFC reference values ranging between 70.42 and 8.96% with a standard deviation range of 3.36-1.47. The low bias (from -0.351 to -0.025), the slopes (0.935-1.077), and the excellent predictive ability (R2; 0.923-0.978) supported the validity of these calibrations.     

NIR‐FT/Raman Spectroscopy for Nutritional Classification of Cereal Foods

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 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.     

Environmental Effects on Developing Wheat as Sensed by Near‐Infrared Reflectance of Mature Grains

For 30 years, near‐infrared (NIR) spectroscopy has routinely been applied to the cereal grains for the purpose of rapidly measuring concentrations of constituents such as protein and moisture. The research described herein examined the ability of NIR reflectance spectroscopy on harvested wheat to determine weather‐related, quality‐determining properties that occurred during plant development. Twenty commercial cultivars or advanced breeding lines of hard red winter and hard white wheat (Triticum aestivum L.) were grown in 10 geographical locations under prevailing natural conditions of the U.S. Great Plains. Diffuse reflectance spectra (1,100–2,498 nm) of ground wheat from these samples were modeled by partial least squares one (PLS1) and multiple linear regression algorithms for the following properties: SDS sedimentation volume, amount of time during grain fill in which the temperature or relative humidity exceeded or was less than a threshold level (i.e., >30, >32, >35, <24°C; >80%, <40% rh). Rainfall values associated with four pre‐ and post‐planting stages also were examined heuristically by PLS2 analysis. Partial correlation analysis was used to statistically remove the contribution of protein content from the quantitative NIR models. PLS1 models of 9–11 factors on scatter‐corrected and (second order) derivatized spectra produced models whose dimensionless error (RPD, ratio of standard deviation of the property in a test set to the model standard error for that property) ranged from 2.0 to 3.3. Multiple linear regression models, involving the sum of four second‐derivative terms with coefficients, produced models of slightly higher error compared with PLS models. For both modeling approaches, partial correlation analysis demonstrated that model success extends beyond an intercorrelation between property and protein content, a constituent that is well‐modeled by NIR spectroscopy. With refinement, these types of NIR models may have the potential to provide grain handlers, millers, and bakers a tool for identifying the cultural environment under which the purchased grain was produced.     

The influence of cereal and dairy consumption on the Irish diet: implications for developing food-based dietary guidelines

OBJECTIVES: To estimate the intakes of cereal and dairy products and their contribution to nutrient intakes in men and women from the Republic of Ireland with a view to formulating food-based dietary guidelines. DESIGN: The North/South Ireland Food Consumption Survey established a database of habitual food and drink consumption using a 7-day food diary. From this database all cereal and dairy products from recipes and identifiable sources were identified and a new database was generated from which analysis of the role of cereal and dairy products in the diet was carried out. RESULTS: Almost 100% of the population consumed cereal and dairy products over the course of the survey week. In general, men consumed significantly more cereal and dairy products than did women (P<0.05). Cereal products made an important contribution to the mean daily intakes of energy (26%), protein (21%), fat (13%), carbohydrate (41%), fibre (45%), iron (43%) and folate (27%). Dairy products also contributed largely to the mean daily intakes of energy (11%), protein (14%), fat (17%), calcium (48%), phosphorus (24%) and vitamin A (27%). Analysis of nutrient intakes across tertiles of cereal and dairy consumption showed that high consumers of wholemeal bread, breakfast cereals, reduced-fat milk and yoghurt had lower fat and higher carbohydrate, fibre and micronutrient intakes than low consumers of these foods. CONCLUSIONS: Findings from the present study could be used to develop effective health strategies to implement changes in cereal and dairy consumption that could alter fat, fibre and micronutrient intakes in the diet.     

Near-Infrared Spectroscopy Coupled with Chemometrics Tools: A Rapid and Non-Destructive Alternative on Soil Evaluation

Near-infrared (NIR) spectroscopy is a rapid, non-destructive and accurate technique for analyzing a wide variety of samples, thus, the growing interest of using this technique in soil science. The objective of this study was to evaluate the potential of NIR spectroscopy to predict organic carbon (OC), total nitrogen (TN), available phosphorus (P) and available potassium (K) in the soil. NIR spectra from 20 cm³ of soil samples were acquired on the range of 750 to 2500 nm in diffuse reflectance mode, resolution of 16 cm^?1 and 64 scans. Eight models of calibration/validation were constructed. Calibration and validation models showed that the predictive potential of NIR varied with the specific soil property (OC, TN, P and K) under evaluation and according to the methodology employed in the model construction (cross-validation or test set). Good prediction models were obtained for OC and TN content based on the statistical parameters. Test set methodology was able to predict soil OC, TN, P, and K better than cross-validation methodology.     

应用可见/近红外高光谱成像测定鲑鱼片脂肪含量分布(英文)

脂肪作为一种重要的品质参数,在大西洋鲑鱼片中的分布很不均匀。为寻找一种能替代脂肪化学检测的快速无损的方法,该研究应用可见/近红外高光谱成像测定大西洋鲑鱼片的脂肪含量分布。分别采用可见/短波近红外(400-1100 nm)和近红外(900-1700 nm)系统获取大西洋鲑鱼片样本的高光谱图像。提取样本图像的平均光谱并与其相应的脂肪含量化学值采用偏最小二乘回归(partial least squares regression,PLSR)和最小二乘支持向量机(least-squares support vector machines,LS-SVM)建立相关性模型。为降低高光谱图像的共线性和冗余度,基于竞争性自适应重加权算法(competitive adaptive reweighted sampling,CARS)分别在可见/短波近红外和近红外光谱区间提取16个(468,479,728,734,785,822,863,890,895,899,920,978,1005,1033,1040,1051 nm)和15个(975,995,1023,1047,1095,1124,1167,1210,1273,1316,1354,1368,1575,1632,1661 nm)特征波长,并分别建立PLSR和LS-SVM模型。特征波长模型的性能优于全波段模型,且近红外区间的特征波长PLSR模型为最优,预测决定系数(R2p)为0.92,预测均方根误差(root mean square error of prediction,RMSEP)为0.92%,剩余预测偏差(residual predictive deviation,RPD)为3.50。最后,将最优模型用于预测高光谱图像上所有像素点的脂肪含量以展示样本上脂肪的分布。此外,还基于该技术对大西洋鲑整鱼片实现了脂肪分布可视化。结果表明高光谱成像技术结合化学计量学方法在大西洋鲑鱼片脂肪的定量和分布可视化上有一定的研究和应用前景。     

Dietary intake and a food-frequency instrument to evaluate a nutrition intervention for the Apache in Arizona

OBJECTIVE: To demonstrate how dietary data collected from 24-hour dietary recalls were incorporated into the development of a quantitative food-frequency questionnaire (QFFQ) for the Apache in Arizona. DESIGN: A cross-sectional study was first conducted using 24-hour dietary recalls to identify foods for inclusion on a QFFQ that would be used to evaluate a nutrition intervention. SETTING: The White Mountain and San Carlos Apache reservations in East-central Arizona. SUBJECTS: The 24-hour dietary recalls were collected from a random sample of 53 adults (34 women and 19 men). RESULTS: A QFFQ was developed that included all foods reported by two or more respondents, plus traditional and seasonal foods. Portion size was assessed using familiar household units and culturally appropriate food models. The final instrument contains 155 foods. Frequency of consumption is assessed using eight categories ranking from 'never or less than once a month' to '2 or more times a day'. Nutrient intakes and the five major food sources of energy, fat and sugar are presented. CONCLUSION: The QFFQ developed is complete and up-to-date for assessing usual food and nutrient intake for the Apache in Arizona. The instrument will be used to evaluate a food store-based nutrition intervention to reduce risk of chronic diseases.     

贮备饲料近红外光谱模型快速预测青绿饲料营养成分含量

为快速检测饲料的营养成分,该研究利用贮备饲料的近红处技术(near-infrared,NIR)快速分析模型预测青绿饲料的营养成分含量。基于贮备饲料的NIR定标模型,将建模优化模式转移应用到青绿饲料的营养成分定量检测,以判断模型转移能力。在实验室环境下扫描并记录新鲜的青绿饲料样本和储存的贮备饲料样本的近红外反射光谱,利用230个贮备饲料样本进行光谱定标训练,以修正偏最小二乘(modified-partial least squares,M-PLS)建模方法,结合随机局部样本、局部选参、局部非连续性可调、交叉检验等技术相结合的方式建立局部优化模型,分别测试120个贮备饲料样本和120个青绿饲料样本中的氮(nitrogen,N)、中性洗涤纤维(neutral detergent fiber,NDF)、酸性洗涤纤维(acid detergent fiber,ADF)含量。将贮备饲料的定标校正模型应用于贮备饲料验证样本的营养成分测定,其标准误差(square error of prediction,SEP):N为1.02、NDF为16.56和ADF为13.47,相关系数均在0.9以上,相对预测偏差(relative prediction derivation,RPD)均大于3;该模型具有对青绿饲料样本的营养成分预测能力,其预测SEP:N为0.90、NDF为14.11和ADF为9.98,预测相关系数均在0.9以上,预测RPD均大于3,达到快速检测误差标准。由于局部建模过程中考虑了数据的潜在非线性结构和具有近似光谱响应的样本之间的不均匀性,相对全局建模方式而言具有更好的数据驱动性质,其建模效果优于全局建模方法。结果表明,基于贮备饲料样本建立的NIR定标校正模型可以用于青绿饲料营养成分的预测,特别是局部分析模型的应用能够提高NIR快速分析的预测精度。     

Prediction chemical composition and alveograph parameters on wheat by near-infrared transmittance spectroscopy

Moisture, protein, wet gluten, dry gluten, and alveograph parameters (W, P, and P/L) of whole wheat grown in different countries around the world were analyzed using near infrared (NIR) transmittance spectroscopy. Modified partial least squares on NIR spectra (850-1048.2 nm) were developed for each constituent or physical property. The best models were obtained for protein, moisture, wet gluten, and dry gluten with r(2) = 0.99, 0.99, 0.95, and 0.96, respectively. Initial alveograph NIR models proposed for all wheat samples did not perform well. However, when wheat samples were divided in two groups depending on W (deformation energy) values, NIR models were highly improved, showing enough prediction accuracy for screening wheat at the receiving stage at mills or elevators.     

Food supplementation, nutritional intake of recipients and operational aspects: an integrated pilot nutrition initiative of BRAC

OBJECTIVES: To explore the nutritional quality of supplementary foods and additional energy consumption by the recipients in a pilot nutrition initiative of BRAC. DESIGN: In-depth interviews, observations during feeding at feeding centres, and laboratory analyses of supplementary foods for nutrient contents performed at the Institute of Nutrition and Food Science, University of Dhaka, Bangladesh. SETTING: Muktagacha thana (sub-district) in Mymensingh district, a rural area of Bangladesh. SUBJECTS: Pregnant and lactating mothers and children below 2 years of age. RESULTS: Analysis revealed that supplementary food, if taken completely, could provide daily energy equivalent to 752 kcal to a mother and 212 kcal to a child below 2 years of age. Mothers consumed about 75% of the food provided (approximately 564 kcal day(-1)). The food was shared mostly with young children and husbands. In-depth interview with mothers also suggested that they usually skipped breakfast if the food was given in the morning. The children liked the taste of food, and unless the child was sick or had some food before coming to the centre, she/he ate all the food provided. Although the main purpose of the project was to provide nutrition education, it was observed that activities at feeding centres were limited to food distribution with little time devoted to the communication of nutritional messages. CONCLUSIONS: Training should be given to service providers to communicate nutritional messages effectively as part of understanding the goal of the initiative. It is important to explore whether the regular diets of the recipients are replaced by the food supplementation or not.     

Thermal stability of alpha-amylase from malted jowar (Sorghum bicolor)

Malted cereals are rich sources of alpha-amylase, which catalyzes the random hydrolysis of internal alpha-(1-4)-glycosidic bonds of starch, leading to liquefaction. Amylases play a role in the predigestion of starch, leading to a reduction in the water absorption capacity of the cereal. Among the three cereal amylases (barley, ragi, and jowar), jowar amylase is found to be the most thermostable. The major amylase from malted jowar, a 47 kDa alpha-amylase, purified to homogeneity, is rich in beta structure ( approximately 60%) like other cereal amylases. T(m), the midpoint of thermal inactivation, is found to be 69.6 +/- 0.3 degrees C. Thermal inactivation is found to follow first-order kinetics at pH 4.8, the pH optimum of the enzyme. Activation energy, E(a), is found to be 45.3 +/- 0.2 kcal mol(-)(1). The activation enthalpy (DeltaH), entropy (DeltaS*), and free energy change (DeltaG) are calculated to be 44.6 +/- 0.2 kcal mol(-)(1), 57.1 +/- 0.3 cal mol(-)(1) K(-)(1), and 25.2 +/- 0.2 kcal mol(-)(1), respectively. The thermal stability of the enzyme in the presence of the commonly used food additives NaCl and sucrose has been studied. T(m) is found to decrease to 66.3 +/- 0.3, 58.1 +/- 0.2, and 48.1 +/- 0.5 degrees C, corresponding to the presence of 0.1, 0.5, and 1 M NaCl, respectively. Sucrose acts as a stabilizer; the T(m) value is found to be 77.3 +/- 0.3 degrees C compared to 69.6 +/- 0.3 degrees C in the control.     

Measurement of Blend Concentrations of Conventional and Waxy Hard Wheats Using NIR Spectroscopy

Breeding development of waxy (amylose‐free) hard wheat lines adapted to the North American climate has been underway for more than a decade, with releases of competitive varieties imminent. Because of required identity preservation and a possible premium value placed on waxy lots, a rapid and accurate method is desired to identify and quantify the mixing of conventional wheat with waxy wheat, a condition that might occur at harvest or any point downstream. Our previous work demonstrated that lines pure with waxy starch can be identified from nonwaxy lines by use of near‐infrared (NIR) spectroscopy applied either on a whole kernel or ground meal basis. However, mixture quantification by NIR techniques has not been examined until now. Using hard winter wheat grown in two seasons (2011 and 2012) and at two locations (Nebraska and Arizona), a series of mixtures ranging in proportion (conventional/waxy) percentage by weight, from 0:100 to 100:0, were formed from nine pairs of waxy and nonwaxy varieties or lines, with year and location being consistent within a pair. Twenty‐nine mixtures (0, 1, 2, 3, 4, 5, 10, 15, …, 85, 90, 95, 96, 97, 98, 99, and 100%) were formed for each pair. Partial least squares regression models were developed by using eight of the nine pairs, with model validation accomplished by using the pair excluded. This procedure was repeated for each pair. The results indicate that, regardless of sample format or spectral pretreatment, the optimal models typically produce coefficients of determination in excess of 0.98, with standard errors of 4–7%, thus demonstrating the feasibility of the use of the NIR technique to predict the mixture level to within 10% by weight.     

PH—Postharvest Technology: Comparison of Fourier Transform and Dispersive Near-Infrared Reflectance Spectroscopy for Apple Quality Measurements

The large amount of publications about quality measurements of horticultural products with near-infrared (NIR) reflectance spectroscopy demonstrates its usefulness for measuring their internal composition in a non-destructive way. Up to now, dispersive instruments have been used exclusively to yield satisfactory calibration models. In this study, Fourier transform (FT) NIR spectroscopy has been compared to dispersive NIR spectroscopy. The instrument stability, the light penetration depth and the predictive capacity of some quality characteristics between both instruments were compared. The FT spectrophotometer achieved a higher signal-to-noise ratio. The light penetration depth in a Jonagold apple differed between the two instruments: the FT spectrophotometer established a lower penetration depth. Both instruments were able to measure the quality characteristics (soluble solids content, firmness and titratable acidity) with comparable accuracy. Based on the results, it was concluded that FT–NIR reflectance spectroscopy is an interesting alternative for standard dispersive instruments for the non-destructive quality evaluation of apples.     

Detection and quantification of roundup ready soy in foods by conventional and real-time polymerase chain reaction

Transgenic soybean line GTS-40-3-2, marketed under the trade name Roundup Ready (RR) soy, was developed by Monsanto (USA) to allow for the use of glyphosate, the active ingredient of the herbicide Roundup, as a weed control agent. RR soy was first approved in Canada for environmental release and for feed products in 1995 and later for food products in 1996 and is widely grown in Canada. Consumer concern issues have resulted in proposed labeling regulations in Canada for foods derived from genetically engineered crops. One requirement for labeling is the ability to detect and accurately quantify the amount of transgenic material present in foods. Two assays were evaluated. A conventional qualitative Polymerase Chain Reaction (PCR) assay to detect the presence of soy and RR soy and a real-time PCR to quantify the amount of RR soy present in samples that tested positive in the first assay. PCR controls consisted of certified RR soy reference material, single transgenic soybeans, and a processed food sample containing a known amount of RR soy. To test real-world applicability, a number of common grocery store food items that contain soy-based products were tested. For some samples, significant differences in amplification efficiencies during the quantitative PCR assays were observed compared to the controls, resulting in potentially large errors in quantification. A correction factor was used to try to compensate for these differences.     

Geographic classification of spanish and Australian tempranillo red wines by visible and near-infrared spectroscopy combined with multivariate analysis

Visible (vis) and near-infrared (NIR) spectroscopy combined with multivariate analysis was used to classify the geographical origin of commercial Tempranillo wines from Australia and Spain. Wines (n = 63) were scanned in the vis and NIR regions (400-2500 nm) in a monochromator instrument in transmission. Principal component analysis (PCA), discriminant partial least-squares discriminant analysis (PLS-DA) and linear discriminant analysis (LDA) based on PCA scores were used to classify Tempranillo wines according to their geographical origin. Full cross-validation (leave-one-out) was used as validation method when PCA and LDA classification models were developed. PLS-DA models correctly classified 100% and 84.7% of the Australian and Spanish Tempranillo wine samples, respectively. LDA calibration models correctly classified 72% of the Australian wines and 85% of the Spanish wines. These results demonstrate the potential use of vis and NIR spectroscopy, combined with chemometrics as a rapid method to classify Tempranillo wines accordingly to their geographical origin.     

Testing nutrient profile models using data from a survey of nutrition professionals

OBJECTIVE: To compare nutrient profile models with a standard ranking of 120 foods. DESIGN: Over 700 nutrition professionals were asked to categorise 120 foods into one of six positions on the basis of their healthiness. These categorisations were used to produce a standard ranking of the 120 foods. The standard ranking was compared with the results of applying eight different nutrient profile models to the 120 foods: Models SSCg3d and WXYfm developed for the UK Food Standards Agency, the Nutritious Food Index, the Ratio of Recommended to Restricted nutrients, the Naturally Nutrient Rich score, the Australian Heart Foundation's Tick scheme, the American Heart Association's heart-check mark and the Netherlands tripartite classification model for foods. Rank correlation was assessed for continuous models, and dependence was assessed for categorical models. RESULTS: The continuous models each showed good correlation with the standard ranking (Spearman's rho = 0.6-0.8). The categorical models achieved high chi(2) results, indicating a high level of dependence between the nutrition professionals' and the models' categorisations (P < 0.001). Models SSCg3d and WXYfm achieved higher scores than the other models, implying a greater agreement with the standard ranking of foods. CONCLUSIONS: The results suggest that Models SSCg3d and WXYfm rank and categorise foods in accordance with the views of nutrition professionals.     

Determination of total dietary fiber of intact cereal food products by near-infrared reflectance

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.