No abstract

Barley grains are a rich source of compounds, such as resistant starch, beta-glucans and anthocyanins, that can be explored in order to develop various products to support human health, while lignocellulose in straw can be optimised for feed in husbandry, bioconversion into bioethanol or as a starting material for new compounds. Existing natural variations of these compounds can be used to breed improved cultivars or integrated with a large number of mutant lines. The technical demands can be in opposition depending on barley’s end use as feed or food or as a source of biofuel. For example beta-glucans are beneficial in human diets but can lead to issues in brewing and poultry feed. Barley breeders have taken action to integrate new technologies, such as induced mutations, transgenics, marker-assisted selection, genomic selection, site-directed mutagenesis and lastly machine learning, in order to improve quality traits. Although only a limited number of cultivars with new quality traits have so far reached the market, research has provided valuable knowledge and inspiration for future design and a combination of methodologies to achieve the desired traits. The changes in climate is expected to affect the quality of the harvested grain and it is already a challenge to mitigate the unpredictable seasonal and annual variations in temperature and precipitation under elevated [CO2] by breeding. This paper presents the mutants and encoded proteins, with a particular focus on anthocyanins and lignocellulose, that have been identified and characterised in detail and can provide inspiration for continued breeding to achieve desired grain and straw qualities.

To measure the concentration of trace CO 2 with high performance, tunable diode laser absorption spectroscopy technology was adopted in this paper.Kalman-wavelet algorithm was used to depress the detector noise and optical interference fringe noise. Experimental results show that the second harmonic signal-to-noise ratio of the measuring system at 50ppmv CO 2 gas was improved by 2.06 times compared with no filtering. Under different CO 2 concentrations, the measuring errors are from 2.57% to 2.66%. According to Allan variance analysis, the minimum detection limit was reduced to 5.2 ppmv with the optimal integration time of 61 s. And, the standard deviation value was decreased from 33 to 19 ppmv during long-term measurement, which can guarantee the high-performance measurement of CO 2 gas.