Starch, mainly composed of amylose and amylopectin, is the major nutrient in grain sorghum. Amylose and amylopectin composition affects the starch properties of sorghum flour which in turn determine the suitability of sorghum grains for various end uses. Partial least squares regression models on near infrared (NIR) spectra were developed to estimate starch and amylose contents in intact grain sorghum samples. Sorghum starch calibration model with a coefficient of determination (R²) = 0.87, root mean square error of cross validation (RMSECV) = 1.57% and slope = 0.89 predicted the starch content of validation set with R² = 0.76, root mean square error of prediction (RMSEP) = 2.13%, slope = 0.93 and bias = 0.20%. Amylose calibration model with R² = 0.84, RMSECV = 2.96% and slope = 0.86 predicted the amylose content in validation samples with R² = 0.76, RMSEP = 2.60%, slope = 0.98 and bias = −0.44%. Final starch and amylose cross validated calibration models were constructed combining respective calibration and validation sets and used to predict starch and amylose contents in 1337 grain samples from two diverse sorghum populations. Protein and moisture contents of the samples were determined using previously tested NIR spectroscopy models. The distribution of starch and protein contents in the samples of low amylose (15%) and the overall relationship between starch and protein contents of the sorghum populations were investigated. Percent starch and protein were negatively correlated, low amylose lines tended to have lower starch and higher protein contents than lines with high amylose. The results showed that NIR spectroscopy of whole grain can be used as a high throughput pre-screening method to identify sorghum germplasm with specific starch quality traits to develop hybrids for various end uses.

Near infrared (NIR) spectroscopy is widely used for evaluating quality traits of cereal grains. For evaluating protein content of intact sorghum grains, parallel NIR calibrations were developed using an established benchtop instrumentation (Perten DA-7250) as a baseline to test the efficacy of an adaptive handheld instrument (VIAVI MicroNIR OnSite-W). Spectra were collected from 59 grain samples using both instruments at the same time. Cross-validated calibration models were validated with 33 test samples. The selected calibration model for DA-7250 with a coefficient of determination (R²) = 0.98 and a root mean square error of cross validation (RMSECV) = 0.41% predicted the protein content of a test set with R² = 0.94, root mean square error of prediction (RMSEP) = 0.52% with a ratio of performance to deviation (RPD) of 4.13. The selected model for the MicroNIR with R² = 0.95 and RMSECV = 0.62% predicted the protein content of the test set with R² = 0.87, RMSEP = 0.76% with an RPD of 2.74. In comparison, the performance of the DA-7250 was better than the MicroNIR, however, the performance of the MicroNIR was also acceptable for screening intact sorghum grain protein levels. Therefore, the MicroNIR instrument may be used as a potential tool for screening sorghum samples where benchtop instruments are not appropriate such as for screening samples in the field or as a less expensive option compared with benchtop instruments.

Wheat bran possesses diverse nutritional and functional properties. In this study, wheat bran aqueous extract (WBE) was produced and thoroughly characterized as a functional ingredient and improver for bakery application. The WBE contained 50.3% total carbohydrate, 24.5% protein, 13.0% ash, 6.7% soluble fiber, 2.9% insoluble fiber, and 0.5% β-glucan. Notably, adding 7.5% WBE significantly increased the bread-specific volume to 4.84 cm³/g, compared with the control of 4.18 cm³/g. Adding WBE also resulted in a remarkable improvement in dough properties. The WBE-enriched dough showed increased peak, setback, breakdown, and final viscosities, along with higher storage and loss modulus. Scanning electron microscopy analysis further revealed that the WBE promoted the aggregation of protein and starch within the dough. The extractable gliadin to glutenin ratio increased with 5 and 7.5% WBE additions, compared with the control and 2.5% WBE addition. WBE did not significantly alter the starch gelatinization temperature or dough extension properties. These findings demonstrate that the inclusion of WBE in wheat flour is a promising approach for producing high-quality bread that is enriched with dietary fiber and protein.

Background and Objectives: Non‐alpha‐amylase induced low falling number was identified in 2024 soft white wheat. The objectives of this work were to characterize these low falling number samples and determine the impact on end‐product quality. The results were analyzed to determine differences in flour characteristics that might result in a low falling number. Findings: Analysis suggests that physiochemical changes in starch properties and low protein content may be contributing to the 2024 low falling numbers. Low falling number Japanese sponge cakes were comparable to the control cakes and produced an acceptable product. Conclusions: The falling number method did not correctly determine grain end‐product quality for the 2024 samples. Starch pasting properties identified by the Rapid Visco Analyzer provided significant diagnostic information and will be important for future work. Significance and Novelty: To our knowledge, this is the first characterization of grain with low falling number in the absence of elevated alpha‐amylase activity and the impact on end‐product quality.