Thesis - Open Access
Master of Science (MS)
Biology and Microbiology
Wheat is a valuable cereal grain in terms of its growability, versatility, and multifunctional nutritional components. Research into the genetic characteristics and growing conditions of the grain is advantageous to wheat breeders, farmers, food scientists, food processors, and consumers. Optimizing the quality of the wheat grain is important to yielding a crop with the most desirable traits. Analytically obtaining data on the quality attributes of wheat is a lengthy and resource intensive process. Near infrared reflectance spectroscopy (NIRS) technology is rapid, cost-effective, and a powerful analytical tool that can be harnessed to create predictive calibrations for estimations of wheat parameters. This study looked to analytically obtain Total Dietary Fiber reference values, as well as relate these values to genetic and environmental variability. Ninety-nine hard red spring wheat samples, including 33 varieties grown in three locations (Brookings, Miller, Groton) in 2018 were analyzed in duplicates. It was determined that both variety and growing location were significant in influencing variability of TDF residue at the 0.001 level of significance. A Duncan Multiple Range Test was conducted at the 0.05 level of significance to identify the rankings of the growing locations, which indicated that Brookings and Miller were statistically the same and both were better than Groton in terms of the TDF residue %. Similarly, a Duncan Multiple Range Test was used to identify that 13 of the 33 varieties were ranked the highest, and statistically the same, including SURPASS, SD4740, FOREFRONT, SD4719, SD4707, SD4816, LCSTRIGGER, SD4720, SD4721, BRICK, SD4711, SD4775, and ADVANCE. Predictive NIRS calibration estimations for TDF and other selected wheat constituents, and mixing and baking parameters were created for 2018, 2019, and combined 2018/2019 data. A good calibration will have a high coefficient of determination (RSQ), high variance ratio (1-VR), low standard error of calibration (SEC), low standard error of cross validation (SECV), and a low standard error of prediction (SEP). TDF did not yield a good calibration (RSQ 0.07, SEC 1.52, SECV 1.70, 1-VR -0.18). owing to a small range of occurrence and lack of homogeneity of the residue. Furthermore, milling of wheat grains involves grinding and sifting, removing bran, a significant source of fiber. For the 2018 dataset, the parameters with an RSQ>0.6 included single kernel hardness index (0.87), dry gluten (0.82), farinograph water absorption% (0.91), water absorption capacity (0.91), NIR grain moisture (0.84), NIR grain protein (0.99), NIR grain ash (0.87), flour protein (0.88), flour ash (0.85), mixograph’s mid-line peak value (0.85), total gluten (0.70), good wet gluten (0.78), wet gluten (0.70), WAM% (0.64), kernel protein (0.78), kernel ash (0.72), and flour extraction (0.63). For the 2019 dataset, the parameters with an RSQ>0.6 included the farinograph moisture% (0.81), water absorption% (0.92), WAC% (0.92), WAM% (0.84), NIR protein (0.99), NIR ash (0.88), and NIR moisture (0.64). For the combination 2018/2019 calibration model, parameters with RSQ> 0.60 included the Farinograph’s dough development time (0.95), water absorption (0.90), WAC% (0.88), WAM% (0.87), NIR moisture (0.92), NIR protein (0.99), NIR ash (0.90), farinograph moisture (0.77), mixing tolerance index (0.60), dry gluten (0.66), and time to breakdown (0.62). The accuracy of these calibrations was validated with a validation subset of data in which the reference values were known, but not included within the calibration development. A paired t-test showed that the NIRS predictions were not statistically different than the known reference values at a 95% confidence level for all tested parameters except for 2018 NIR ash, flour ash, and kernel ash. Correlations between wheat constituents, mixing parameters, and baking parameters were generated to determine their relationships. Pearson’s correlations coefficients indicated strong correlations among gluten parameters, water binding of flour, and mixograph/farinograph measurement values. This study shows that growing location and wheat variety have a statistical impact on dietary fiber variability, and that dietary fiber is poorly predicted by NIRS calibrations. NIRS predictive calibrations for other constituents (gluten, protein, moisture, mixograph/farinograph parameters), were able to be established with high RSQ, 1-VR, and low SEC, SECV, SEP, and bias. Many quality parameters of wheat were found to be correlated with one another, further increasing the predictive potential of desirable wheat quality traits.
Library of Congress Subject Headings
Wheat -- Analysis.
Wheat -- Quality.
Near infrared reflectance spectroscopy.
Hard red spring wheat.
Food -- Fiber content.
Number of Pages
South Dakota State University
In Copyright - Educational Use Permitted
Schimke, Lily, "Development of a Near Infrared Reflectance Spectroscopy (NIRS) Platform for Rapid Wheat Quality Analysis" (2020). Electronic Theses and Dissertations. 4091.