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

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.

Keywords: grain quality; hyperspectral imaging; plant breeding; wheat.

FIGURE 1

Wheat worldwide statistics from 1961 to 2020. (a) Wheat yield per unit area has significantly increased since 1960s given that the harvested area has not changed. (b) Asia and Europe produce more than 50% of the global wheat. (c) Top five wheat producing countries are all developed countries. (d) Top wheat importers are mostly developing countries which further puts a negative pressure on their economies. (e) In the last two decades, wheat imports by low‐income food deficit countries have massively increased whereas their exports have not changed. Figure is generated with data from FAOSTAT (FAO, 2022).

FIGURE 2

Wheat grain quality traits. The factors which determine wheat grain quality can be classified into morphological, technological and physiochemical characteristics. The figure demonstrates different parameters involved in each category and how they affect the grain in terms of quality, milling performance, yield stability and nutritional and health properties. For simplification, all the effects of each category have been presented together, for example, the effects of gain length, width, thickness and length/width ratio have been put collectively under the category of morphological characteristics. Their details are described in the text separately (Created with BioRender.com).

FIGURE 3

An illustration of wheat grain and its physiochemical properties. A mature grain contains ~85% carbohydrates and ~10–15% proteins. The majority proportion of carbohydrates is starch and that of proteins is gluten. High protein wheat often comes with a trade‐off of low starch. Lipid content is present in a very small quantity (1–3%) in the germ part of the grain (Created with BioRender.com).

FIGURE 4

A schematic diagram of hyperspectral imaging and data analysis. A typical indoor hyperspectral system consists of a movable stage for sample placement, a light source with equal scattering, a near‐infrared camera, a hyperspectral system that connects the stage and the camera and a computer interface to collect data. Data are produced as 3D hypercubes which are processed using chemometrics to extract desired information. A reference material is used to build a calibration for the quantification of trait data from future samples using only hyperspectral data (Created with BioRender.com).