Reviving grain quality in wheat through non-destructive phenotyping techniques like hyperspectral imaging

Safdar, Luqman B.; Dugina, Kateryna; Saeidan, Ali; Yoshicawa, Guilherme V.; Caporaso, Nicola; Gapare, Brighton; Umer, M. Jawad; Bhosale, Rahul A.; Searle, Iain R.; Foulkes, M. John; Boden, Scott A.; Fisk, Ian D.

Abstract

A long-term goal of breeders and researchers is to develop crop varieties that can resist environmental stressors and produce high yields. However, prioritising yield often compromises improvement of other key traits, including grain quality, which is tedious and time-consuming to measure because of the frequent involvement of destructive phenotyping methods. Recently, non-destructive methods such as hyperspectral imaging (HSI) have gained attention in the food industry for studying wheat grain quality. HSI can quantify variations in individual grains, helping to differentiate high-quality grains from those of low quality. In this review, we discuss the reduction of wheat genetic diversity underlying grain quality traits due to modern breeding, key traits for grain quality, traditional methods for studying grain quality and the application of HSI to study grain quality traits in wheat and its scope in breeding. Our critical review of literature on wheat domestication, grain quality traits and innovative technology introduces approaches that could help improve grain quality in wheat.

Citation

Safdar, L. B., Dugina, K., Saeidan, A., Yoshicawa, G. V., Caporaso, N., Gapare, B., …Fisk, I. D. (2023). Reviving grain quality in wheat through non-destructive phenotyping techniques like hyperspectral imaging. Food and Energy Security, 12(5), Article e498. https://doi.org/10.1002/fes3.498

• Journal Article Type: Review
• Acceptance Date: Aug 14, 2023
• Online Publication Date: Sep 3, 2023
• Publication Date: 2023-09
• Deposit Date: Sep 4, 2023
• Publicly Available Date: Sep 7, 2023
• Journal: Food and Energy Security
• Electronic ISSN: 2048-3694
• Publisher: Wiley
• Peer Reviewed: Peer Reviewed
• Volume: 12
• Issue: 5
• Article Number: e498
• DOI: https://doi.org/10.1002/fes3.498
• Keywords: Grain quality; hyperspectral imaging; plant breeding; wheat
• Public URL: https://nottingham-repository.worktribe.com/output/25060961
• Additional Information: Received: 2023-05-15; Accepted: 2023-08-14; Published: 2023-09-03