GrainGenes

Relationship between flour yields and wheat physical characteristics.

Wheat characteristics and milling flour yields were determined using 12 hard winter wheat varieties harvested at six Kansas regions in 1993. Mean values of wheat single-kernel (SK) hardness scores (HS), peak forces of crushing profile, weights, sizes, moisture contents, and their standard deviations were analyzed by the USGMRL Single-Kernel Wheat Characterization System (SKWCS). The micro-test weight, grain density determined by an air pycnometer, wheat sizings, and NIR-HS also were measured. Wheat kernel density was correlated significantly with the SKWC-HS, showing a simple linear correlation coefficient (r) of 0.74 (n = 72) and r value of 0.70 (n = 72) with NIR-HS. Micro-test weights also were correlated positively with wheat kernel densities (r = 0.82, n = 72). Among wheat quality parameters, flour yields showed the highest r value of 0.54 with NIR-HS, next with peak force (r = 0.52), followed by micro-test weight (r = 0.51) and SKWC-HS (r = 0.43). Flour yields also were affected by SK weights (r = 0.39) and wheat kernel densities (r = 0.37). A prediction equation of wheat flour yield was derived from a principal-components stepwise multiple regression analysis, which was significant at 0.01 % level with r value of only 0.69 (r-square = 0.47).

Comparison of mixograms at optimum and constant absorption.

One of the most difficult parameters to be determined on a mixogram is the optimum absorption. A mixogram does not have a line where optimum absorption is obtained such as in a farinogram. Optimum absorption is obtained from trained personnel. The possibility has been discussed of running mixographs at the same absorption and not trying to optimize. Some laboratories run mixographs at a constant absorption and can use the mixograms as a screening method in wheat breeding. The objective of this study was to compare the results of the same flours run at optimum absorptions and at a constant absorption. Flours were chosen to represent the spectrum of mixing tolerance scores, which generally are assigned by a trained person. Mixograms for each flour sample of 10 g (on 14 % moisture basis) were obtained by using a 10-g mixograph bowl with optimum water absorptions. These samples were run at 62 % absorption with no adjustment made for the moisture of the flour. The tolerance scores were correlated highly between the mixograms at optimum and constant absorptions. A constant absorption method was concluded to be a useful screening tool for a large number of samples when the results need to show only if the flour is strong or weak and if its mixing requirement is short, medium, or long.

High-protein air-classified flour and its effects on frozen dough quality.

This was a cooperative study between KSU and the ARS. Dough quality deterioration with storage time and fluctuating temperature has been a major concern in making frozen, yeast-raised, dough products. The use of wheat with high protein content and good quality or flour enriched with vital gluten could improve frozen dough stability. Another method to strengthen the flour, namely the use of high-protein air-classified flour, was investigated in this research. A T11 Hurricane Turbo Separator was used to air-classify pin-milled, straight-grade flour from nine wheats. Protein was shifted to the fine fraction, together with damaged starch and enzyme activity. The finer high-protein flour fraction was used to improve frozen dough's quality by blending with straight-grade flour. The bread volume increased with flour protein content. Quality deterioration of dough with extended frozen storage and freeze-thawing was lessened by adding the high-protein flour. This trend was not shown in low-protein wheat flours that (straight grade) contained less than 9 % protein. Other properties of air-classified flour also were investigated to explain their function in making frozen bread dough.

Effects of wheat fiber on breadmaking performance.

This was a cooperative study between KSU and ARS. Wheat fiber (WF, Watson Foods Co. Inc.) with 98 % total dietary fiber content was blended into a bread flour (10.3 % protein on 14% mb) with or without vital wheat gluten (VG, 67 % protein on 14 % mb) and sodium stearoyl-2-lactylate (SSL). WF and VG were substituted for flour so that the final blend contained 10.3 % protein (14 % mb). SSL was added at 0.5 % based on a final blend. A control sample (unsubstituted original flour) and six blends were examined by using a pup-loaf straight-dough method to study the effects of WF on breadmaking. The six blends included: (A) 5 % WF; (B) 5 % WF+VG; (C) 5 % WF+VG+SSL; (D) 10 % WF; (E) 10 % WF+VG; and (F) 10 % WF+VG+SSL. Bake absorption and mix time increased with an increase in WF replacement levels. High-fiber breads containing WF showed reduced loaf volume; a coarse, open, and nonuniform crumb grain; and pale crust color. Additions of VG and SSL had no effect on loaf volume, but SSL improved both crumb grain and softness. A comparison of the stabilities of three antioxidant vitamins in a high-fiber bread (10 % WF) versus a control bread is under investigation.

Neural network classification and machine vision for bread crumb grain evaluation.

Bread crumb grain was studied to develop a model for pattern recognition of bread baked at The Hard Winter Wheat Quality Laboratory (HWWQL), Grain Marketing and Production Research Center (GMPRC). Images of bread slices were acquired with a scanner in a `512 x 512' format. Subimages in the central part of the slices were evaluated by several features such as mean, determinant, eigenvalues, shape of a slice, and other crumb features. Derived features were used to describe slices and loaves. Neural network programs of the MATLAB package were used for data analysis. The Learning Vector Quantization method and multivariate discriminant analysis were applied to bread slices from wheat of different sources. Training and test sets of different, bread-crumb, texture classes were obtained. The ranking of subimages was correlated well with visual judgement. The performance of different models on slice recognition rate was studied to choose the best model. The recognition of classes created by human judgement with image features was low. Recognition of arbitrarily created classes, according porosity patterns, with several feature patterns was approximately 90 %. The correlation coefficient was approximately 0.7 between slice shape features and loaf volume.

Volatiles in some selected commercial breads determined by dynamic headspace and GC-MS/IR methods.

Five types of commercial breads obtained from a local market, including white sandwich, Irish oatmeal, soft rye, hearty rye, and sourdough, were analyzed for volatiles. By using a purge and trap instrument, volatiles were purged directly from fresh crumb and crust samples of each bread type, collected on a Tenax trap, and then transferred to a gas chromatograph. Separated components were detected and identified by using mass and infrared spectroscopic detectors. More than 160 compounds were detected. Many of the components were present in all of the bread samples, with relative amounts of components varying among bread types and among crust and crumb samples of a given bread type. Alcohols were generally the most abundant, followed in approximate order by aldehydes, esters, ketones, acids, various aromatics, terpenes, and hydrocarbons. Flavoring additives such as limonene, carvone and other related compounds were found mostly in the rye breads. Composition of volatiles from sourdough bread differed greatly from all that of the other breads, especially in increased levels of aldehydes, acids, and certain esters. Unsaturated aldehydes such as 2-hexenal and 2-heptenal were most abundant in sour dough bread.

Classification of grain odors by artificial neural network analysis of volatile compounds.

Commercial neural network software was used for relating perceived odor in grain samples to concentrations of volatiles. A gas chromatograph-mass spectrometer system was set up for detection of specific compounds purged from whole grain. Grain samples included wheat, corn, soybeans, and sorghum that had been graded for odor by Federal Grain Inspection Service inspectors and by a panel at the USGMRL. In classifying 95 samples as simply `OK' or `off-odor', the networks exceeded 90 % accuracy. Accuracy was lower when classifying 182 samples into specific categories such as musty, sour, smoke, insect, or other objectionable odors. Having a larger number of samples with a particular odor improved the accuracy of classifying that odor. Samples with multiple odor problems often were classified correctly as having more than one odor. Initially, 35 selected compounds were used as inputs to the network; some could be eliminated without greatly affecting performance.

Grain odor project.

A cooperative project on classification and detection of grain odor problems was initiated between the U.S. Grain Marketing Research Laboratory (USGMRL), the Federal Grain Inspection Service (FGIS), and the Kansas State University Sensory Analysis Center. Plans and outlines for the work have been completed, and samples are being collected through FGIS. Sensory data are being obtained by FGIS and by personnel at USGMRL. Sensory analysis by the KSU Sensory Analysis Center will begin in January, 1996, and will be correlated with the other sensory data and with GC analysis of volatiles from the grain. If possible, one or more `electronic nose' devices also will be tested for its ability to classify grain odors.

TCK project.

Wheat cannot be exported from the Pacific Northwest region of the U.S. to China because of a Chinese quarantine against the dwarf bunt fungus, also called TCK smut. We are involved in a cooperative project with Kansas State University Department of Grain Science and Industry to study the fate of TCK spores in the milling process. A mixture of contaminated and clean wheat will be made and milled at KSU. The survival of teliospores will be documented through all of the steps of mixing, cleaning, tempering, milling, and processing of milling by-products for animal feed. Preliminary tests have been done to establish techniques for separating and identifying the spores.

Storage molds and mycotoxins.

(From the `Handbook of Agricultural Crop Drying & Storage'.) Fungi are a major component in damaged grain and, along with insects, rodents, and birds, cause considerable loss to stored grain throughout the world. Prevention of fungal growth through good management practices will help assure good quality grain for commercial market products and also will prevent the loss of dry matter and the production of mycotoxins that cause potential human health hazards and loss in livestock production. This chapter reviews the major fungi found in grain, the conditions for their growth, their significance in relation to grain quality, the development of mycotoxins in grain, and the prediction and control of damage by storage fungi.

Finding wet kernels in bulk wheat sample.

During storage and handling of grain, wet spots in the grain may develop because of changing atmospheric conditions. Grain inspection will benefit if the detection of wet kernels in bulk samples is automated. Identification of wet kernels in bulk grain samples is possible using image analysis. Images of dry and wet kernels in bulk wheat samples were acquired with different bandwidth filters. Boundaries of wet and dry areas in an image were determined by comparison of different bandwidth gray values for the same pixel location. The effect of water adsorption with time and the threshold of recognition also was studied. Several mathematical manipulations of the multispectral data were done to enhance recognition of wet vs dry spots in bulk wheat samples.

Energy requirements for size reduction of wheat using a roller mill.

An experimental two-roll mill was developed and instrumented for computerized data acquisition. Milling tests were preformed on three classes of wheat. Included in the study were six independent variables each with three levels, namely class of wheat, moisture content, feed rate, fast roll speed, roll speed differential, and roll gap. Two covariates, single-kernel hardness and single-kernel weight, also were included in the statistical analysis. Prediction models were constructed for five dependent variables (fast roll power, slow roll power, net power, energy per unit mass, and specific energy). The prediction models fit the experimental data well. The power and energy requirements for size reduction of wheat were correlated highly with the single-kernel characteristics of wheat. Feed rate significantly affected fast roll power, slow roll power, and net power. Roll gap had a significant effect on roller mill grinding. Additional milling tests were conducted by randomly selecting independent variables and covariates to verify the robustness and validity of the prediction models.