VIBE IMAGING ANALYTICS
Research
UniversitiesPublicationsCrop TypesWorkflowsQM3i Analyzer
Request a Sample Analysis
Vibe research study

Investigating the Malting Suitability and Brewing Quality of Different Rice Cultivars

University of Arkansas
University of Arkansas (Fayetteville, AR, USA)
Beverages
2024
DOI available
View journal pageBack to all publications
Investigating the Malting Suitability and Brewing Quality of Different Rice Cultivars
Image credit: Beverages (MDPI, 2024) - CC BY 4.0 | CC BY 4.0
Partner institutions
University of Arkansas logo
University of Arkansas

Fayetteville, AR, USA

Grain quality, extract yield, and rice market studies using Vibe data pipelines.

View profileWebsite
How Vibe analyzers were used

A brewing-focused evaluation of rice cultivars for malting, linking cultivar-dependent differences in wort/beer quality metrics to grain and processing characteristics.

Study overview

This study evaluates the malting suitability and brewing quality of different rice cultivars, comparing outcomes relevant to brewing performance and product quality across cultivars and processing conditions.

Topics and keywords
rice
malting
brewing
Beverages
grain quality
Bibliographic details

Year: 2024

Publication: Beverages

DOI: 10.3390/beverages10010016

Institutions: University of Arkansas

Back to publications page
More research using Vibe

Explore additional studies applying Vibe analyzers in grain and seed phenotyping workflows.

Improving panicle blast resistance and fragrance in a high-quality japonica rice variety through breeding
Frontiers in Plant Science
Improving panicle blast resistance and fragrance in a high-quality japonica rice variety through breeding

Marker-assisted backcrossing introgressed blast-resistance genes (Pigm, Pi-ta) and the aroma gene badh2 into Huruan1212, producing lines with improved panicle blast resistance and fragrance while maintaining high eating and cooking quality.

Utilizing X-ray radiography for non-destructive assessment of paddy rice grain quality traits
Plant Methods
Utilizing X-ray radiography for non-destructive assessment of paddy rice grain quality traits

A proof-of-concept workflow that uses single 2D X-ray radiographs of whole paddy rice grains to non-destructively infer chaffiness, chalky rice kernel percentage (CRK%), and head rice recovery percentage (HRR%).

Integrating genomics, phenomics, and deep learning improves the predictive ability for Fusarium head blight-related traits in winter wheat
The Plant Genome
Integrating genomics, phenomics, and deep learning improves the predictive ability for Fusarium head blight-related traits in winter wheat

A genomics-plus-phenomics study that combines Vibe-based grain phenotyping with deep learning to improve prediction of Fusarium head blight-related traits in winter wheat.