Location: Commodity Utilization ResearchTitle: Aromaticity of secondary products as the marker for sweet sorghum [Sorghum bicolor (L.) Moench] genotype and environment effects
Submitted to: Journal of Agriculture and Food Research
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 7/1/2022
Publication Date: 7/5/2022
Citation: Uchimiya, M. 2022. Aromaticity of secondary products as the marker for sweet sorghum [Sorghum bicolor (L.) Moench] genotype and environment effects. Journal of Agriculture and Food Research 9. Article 100338. https://doi.org/10.1016/j.jafr.2022.100338.
Interpretive Summary: Field experiments are influenced by a variety of factors including weather and new pests. This study developed methods to extract information to rapidly interpret results from field experiments. Rapid data analytics is versatile and allows end-users to observe chemistry trends to grow cultivars enriched with specific value added products.
Technical Abstract: Although data analytics and systems modeling are increasingly being utilized to interpret genotype x environment interactions in plant breeding, chemometrics is currently underutilized. Prior reports indicated correlations between redox-active polyphenols in stem juice of sweet sorghum [Sorghum bicolor (L.) Moench] and its resistance against sugarcane aphid [Melanaphis sacchari (Zehntner)]. However, such correlations are often confounded by the outperforming genotype (outlier in correlations) capable of accumulating several-fold higher secondary metabolites than other cultivars examined. To investigate the underlying chemical characteristics responsible for the pest resistance, this study first employed principal component analysis (PCA) as the exploratory analysis to visualize the primary factors affecting genotype and environmental (April, May, and June planting months) dependence of juice extracted from 24 sweet sorghum cultivars. Chemical parameters arising from redox reactivity were primarily responsible for distinguishing a resistant genotype. The distance of dendrogram based on the genotype-dependent electrochemistry (cyclic voltammetry) was then used as the perturbation parameter in 2D correlation analysis to understand the controlling chemical structures; both genotype and environment were controlled by the redox reactivity and aromaticity of sweet sorghum. Aromatic structures detectable by UV/visible spectrophotometry were then used to build calibrations based on machine learning. The workflow of data analytics in this study could be applied to expedite the biomarker-driven plant breeding without repeating chemical analysis of new field samples.