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\begin{document}

\title{Predicting alpha amylase content and starch breakdown in pregerminated
barley seeds using hyperspectral imaging}



\author[1]{Kevin Propst}%
\author[1]{Evan Hall}%
\author[1]{Cory D Hirsch}%
\author[2]{Jason G Walling}%
\affil[1]{Department of Plant Pathology, University of Minnesota}%
\affil[2]{Cereal Crops Research Unit, USDA-ARS}%


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  \date{May 25, 2024}


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\begin{abstract}
Malting is the process of controlled germination of cereal grain that is
steeped, germinated, and dry kilned to develop flavor compounds,
fermentable sugars, the hydrolytic enzymes necessary for the brewing
process. To meet the strict standards of the brewing industry, variation
in the germination of barley grains need to be minimal but also maintain
rapid germination and optimum levels of \(\alpha\) and
\(\beta\) amylase. Preharvest sprouting poses a signinicant
threat to barley cultivars prior to harvest, resulting in premature
endosperm modification, reduced enzyme content, and poor malthouse
pertormance. Pregerminated grain is ditticult to detect as there are
often no signs of damage, and current methods such as the Hagberg
falling/Stirring number, or pearling tests, each of which ultimately
leads to the destruction of the seed. Here we used time-series
hyperspectral imaging of barley seeds undergoing active and or
pregerminated seed and used a deep neural network to predict stirring
number and alpha amylase values, which are proxy measures for sprout
damage. Prediction models were made for seven genotypes tested
individually and with all genotypes combined. Stirring number assays
ranged from 0 to 190 and our prediction models had mean average errors
of 10.5 to 23.9 depending on the model. To increase the resolution and
accuracy of the predictions we transitioned from using bulk conventional
molecular assays to single seed assays and prediction models. These
results demonstrate that hyperspectral imaging and machine learning
models can be used to predict germinated grain in bulk and single seed
assays.%
\end{abstract}%



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