No abstract

Fusarium head blight (FHB) is among the most common fungal diseases affecting wheat, resulting in decreased yield, low-density kernels, and production of the mycotoxin deoxynivalenol, a compound toxic to humans and livestock. Human visual analysis of representative wheat samples has been the traditional method for FHB assessment in both official inspection and plant breeding operations. While not requiring specialized equipment, visual analysis is dependent on a trained and consistent workforce, such that in the absence of these aspects, biases may arise among inspectors and evaluation dates. This research was intended to avoid such pitfalls by using longer wavelength radiation than the visible using hyperspectral imaging (HSI) on individual kernels. Linear discriminant analysis models to differentiate between sound and scab-damaged kernels were developed based on mean of reflectance values of the interior pixels of each kernel at four wavelengths (1100, 1197, 1308, and 1394 nm). Other input variables were examined, including kernel morphological properties and histogram features from the pixel responses of selected wavelengths of each kernel. The results indicate the strong potential of HSI in estimating fusarium damage. However, improvement in aligning this procedure to visual analysis is hampered by the inherent level of subjectivity in visual analysis.

Background and objectives Falling number, a procedure that indirectly characterizes germination enzyme activity in wheat grain by measuring the viscous behavior of a heated flour–water or meal–water mixture, is affected by the immersion water bath temperature. Maintained at boiling point, the water bath temperature is determined by barometric pressure which changes with land elevation. The effect of elevation, hence barometric pressure, on falling number over a simulated elevation range of near sea level (760 mm Hg) to 1,524 m (632.4 mm Hg) was modeled. Findings First‐order polynomial linear regression equations were developed on log‐transformed mean falling number readings to correct non‐sea level readings to sea level conditions represented as pressure, elevation, or water bath boiling temperature. With correction, the range in standard deviation of falling number over five simulated elevations declined to 0.9–14.1 s, from a precorrection range of 13.2–50.9 s. Conclusions The pressure effect was greatly reduced with application of the model. The correction functions are continuous linear relationships in the log domain and applicable to the entire simulated elevation range. Significance and novelty Ready availability of handheld digital barometers or reliance on the built‐in feature of barometric pressure measurement in newer falling number instruments now allow for ready correction of falling number to prevailing environmental conditions, which will translate into a fairer assessment of wheat quality in commercial trade.