Future dietary protein demand will focus more on plant-based sources than animal-based products. In this scenario, legumes and pulses (lentils, beans, chickpeas, etc.) can play a crucial role as they are one of the richest sources of plant proteins with many health benefits. However, legume consumption is undermined due to the hard-to-cook (HTC) phenomenon, which refers to legumes that have high resistance to softening during cooking. This review provides mechanistic insight into the development of the HTC phenomenon in legumes with a special focus on common beans and their nutrition, health benefits, and hydration behaviour. Furthermore, detailed elucidation of HTC mechanisms, mainly pectin-cation-phytate hypothesis and compositional changes of macronutrients like starch, protein, lipids and micronutrients like minerals, phytochemicals and cell wall polysaccharides during HTC development are critically reviewed based on the current research findings. Finally, strategies to improve the hydration and cooking quality of beans are proposed, and a perspective is provided.

Near-infrared (800–2500 nm; NIR) spectroscopy coupled to hyperspectral imaging (NIR-HSI) has greatly enhanced its capability and thus widened its application and use across various industries. This non-destructive technique that is sensitive to both physical and chemical attributes of virtually any material can be used for both qualitative and quantitative analyses. This review describes the advancement of NIR to NIR-HSI in agricultural applications with a focus on seed quality features for agronomically important seeds. NIR-HSI seed phenotyping, describing sample sizes used for building high-accuracy calibration and prediction models for full or selected wavelengths of the NIR region, is explored. The molecular interpretation of absorbance bands in the NIR region is difficult; hence, this review offers important NIR absorbance band assignments that have been reported in literature. Opportunities for NIR-HSI seed phenotyping in forage grass seed are described and a step-by-step data-acquisition and analysis pipeline for the determination of seed quality in perennial ryegrass seeds is also presented.

Mungbeans are growing in popularity among Australian consumers, driven by their beneficial nutritional and phytochemical composition. However, data on the antioxidative, mineral, and phytochemical content of Australian mungbeans at the point of consumer purchase remains scarce. Here, five commercial mungbean samples were analysed for total antioxidant capacity, total phenolics, and total monomeric anthocyanins. Attenuated total reflectance midinfrared spectroscopy was utilised as a rapid and reliable method of obtaining information about the macrochemical composition of the mungbean hulls. Total antioxidant capacity ranged from 170 to 570 mg Trolox equivalents per 100 g, total phenolic content from 130 to 240 mg gallic acid equivalents per 100 g, and anthocyanin content from 10 to 40 mg cyanidin‐3‐glucoside equivalents per 100 g. There was a significant difference between varieties in all measures of antioxidant, phenolic and anthocyanin contents. Using principal component analysis, the midinfrared spectra for the five mungbean varieties could be isolated, highlighting the differences in their phytochemical composition. In general, whole Australian mungbeans appear to have the highest antioxidant, phenolic, and anthocyanin contents. Midinfrared spectroscopy appears to be a valuable method of obtaining and comparing the macrochemical composition of mungbeans. This technology is likely to be of increasing use in the future.

Genomic selection accelerates genetic progress in crop breeding through the prediction of future phenotypes of selection candidates based on only their genomic information. Here we report genetic correlations and genomic prediction accuracies in 22 agronomic, disease, and seed quality traits measured across multiple years (2015–2017) in replicated trials under rain-fed and irrigated conditions in Victoria, Australia. Two hundred and two spring canola lines were genotyped for 62,082 Single Nucleotide Polymorphisms (SNPs) using transcriptomic genotype-by-sequencing (GBSt). Traits were evaluated in single trait and bivariate genomic best linear unbiased prediction (GBLUP) models and cross-validation. GBLUP were also expanded to include genotype-by-environment G × E interactions. Genomic heritability varied from 0.31to 0.66. Genetic correlations were highly positive within traits across locations and years. Oil content was positively correlated with most agronomic traits. Strong, not previously documented, negative correlations were observed between average internal infection (a measure of blackleg disease) and arachidic and stearic acids. The genetic correlations between fatty acid traits followed the expected patterns based on oil biosynthesis pathways. Genomic prediction accuracy ranged from 0.29 for emergence count to 0.69 for seed yield. The incorporation of G × E translates into improved prediction accuracy by up to 6%. The genomic prediction accuracies achieved indicate that genomic selection is ready for application in canola breeding.