Location: Grain Quality and Structure ResearchTitle: High-throughput micro plate vanillin assay for determination of tannin in sorghum grain Author
Submitted to: Meeting Abstract
Publication Type: Abstract Only
Publication Acceptance Date: 6/25/2013
Publication Date: 9/29/2014
Citation: Herald, T.J., Wilson, J.D., Bean, S., Gadgil, P. 2014. High-throughput micro plate vanillin assay for determination of tannin in sorghum grain. AACC International Annual Meeting. Abstract. 12-P.
Technical Abstract: Sorghum tannins are phenolic compounds that offer health promoting antioxidant properties. The conventional HCl-vanillin assay for determining tannin content is a time-consuming method for screening large sample sets as seen in association mapping panels or breeder nursery samples. The objective of this research was to develop a high-throughput 96 well plate micro-titer platform assays for use as a diagnostic tool for efficient screening sorghums for tannin content in large sample sets. A high tannin Sumac was selected to show proof of concept of the high throughput assay and a larger sample set of 25 sorghum containing tannins were used to validate the assay. As a point of reference the conventional HCl-vanillin assay and bleach test were included in the study. The high-throughput 96-well platform was more rapid than the conventional assay. Approximately 30 measurements per day were completed using the HCl-vanillin conventional assay compared to 224 measurements using the high-throughput 96-well platform. The 96-well platform correlated with conventional assay. The %RSD was 3.54% and 3.21% for the high-throughput 96-well platform and conventional HCl-vanillin method, respectively. The 96 well assay exhibited good reproducibility with the inter plate 5RSD between 2.77%-4.85%. The high-throughput 96-well platform method proved to be as robust and reproducible as the conventional method for determining tannin content in sorghum grain. The high-throughput micro-titer platform assays developed is usable for routine screening of a large sample sets.