To dissect the genetic control of the sensory and textural quality traits of Chinese white noodles, a population of recombinant inbred lines (RILs), derived from the cross of waxy wheat Nuomai 1 (NM1) and Gaocheng 8901 (Gc8901), was used.  The RILs were tested in three different environments to determine the role of environmental effects on quantitative trait loci (QTL) analysis.  A total of 45 QTLs with additive effects for 17 noodle sensory and textural properties under three environments were mapped on 15 chromosomes.  These QTLs showed 4.23–42.68% of the phenotypic variance explained (PVE).  Nineteen major QTLs were distributed on chromosomes 1B, 1D, 2A, 3B, 3D, 4A, and 6A, explaining more than 10% of the phenotypic variance (PV).  Clusters were detected on chromosomes 2B (3 QTLs), 3B (11 QTLs) and 4A (5 QTLs).  The cluster detected on chromosome 4A was close to the Wx-B1 marker.  Five co-located QTLs with additive effects were identified on chromosomes 2B, 3D, 4A, 6A, and 7B.  The two major QTLs, Qadh.sdau-3B.1 and Qspr.sdau-3B.1, in cluster wPt666008–wPt5870 on chromosome 3B were detected in three different environments, which perhaps can be directly applied to improve the textural properties of noodles.  These findings could offer evidence for the selection or development of new wheat varieties with noodle quality using molecular marker-assisted selection (MAS).

Protein and starch are the most important traits in determining processing quality in wheat. In order to understand the genetic basis of the influence of Waxy protein (Wx) and high molecular weight gluten subunit (HMW-GS) on processing quality, 256 recombinant inbred lines (RILs) derived from the cross of waxy wheat Nuomai 1 and Gaocheng 8901 were used as mapping population. DArT (diversity arrays technology), SSR (simple sequence repeat), HMW-GS, and Wx markers were used to construct the molecular genetic linkage map. QTLs for mixing peak time (MPT), mixing peak value (MPV), mixing peak width (MPW), and mixing peak integral (MPI) of Mixograph parameters were evaluated in three different environments. The genetic map comprised 498 markers, including 479 DArT, 14 SSR, 2 HMW-GS, and 3 Wx protein markers, covering 4 229.7 cM with an average distance of 9.77 cM. These markers were identified on 21 chromosomes. Eighteen additive QTLs were detected in three different environments, which were distributed on chromosomes 1A, 1B, 1D, 4A, 6A, and 7D. QMPT-1D.1 and QMPT-1D.2 were close to the Glu-D1 marker accounting for 35.2, 22.22 and 36.57% of the phenotypic variance in three environments, respectively. QMPV-1D and QMPV-4A were detected in all environments, and QMPV-4A was the nearest to Wx-B1. One minor QTL, QMPI-1A, was detected under three environments with the genetic distances of 0.9 cM from the nearest marker Glu-A1, explaining from 5.31 to 6.67% of the phenotypic variance. Three pairs of epistatic QTLs were identified on chromosomes 2D and 4A. Therefore, this genetic map is very important and useful for quality trait related QTL mapping in wheat. In addition, the finding of several major QTLs, based on the genetic analyses, further suggested the importance of Glu-1 loci on dough mixing characteristics.

To evaluate the possible genetic interrelationships between flour components and the sedimentation volume (SD), a doubled haploid (DH) population comprising 168 lines were used to identify the conditional quantitative trait loci (QTLs) for SD in three environments. Ten additive QTLs and 15 pairs of epistatic QTLs were detected for SD through unconditional and conditional QTL mapping. Three major additive QTLs were detected for SD conditioned on the seven quality traits. Two additive QTLs were found to be independent of these traits. Three additive QTLs were suppressed by three of the seven traits because of non-detection in unconditional mapping. Three pairs of epistatic QTLs were completely affected by the seven traits because of detection in unconditional mapping but no-detection in conditional mapping. Twelve pairs of epistatic QTLs were detected in conditional mapping. Our results indicated that conditional mapping could contribute to a better understanding of the interdependence of different and closely correlated traits at the QTL molecular level, especially some minor QTLs were found. The conditional mapping approach provides new insights that will make it possible to avoid the disadvantages of different traits by breeding through molecular design.

As one of the most effective enzymatic modification methods of protein, papain hydrolysis is applied widely in food production, accompanying starch pasting frequently in order to improve industrial quality. Effects of the papain hydrolysis on flour pasting properties were investigated in five papain/flour concentrations and five time-treatments. The structure of starch and protein networks in slurry was investigated under microscope before and after pasting. Results showed that papain hydrolysis influenced the pasting properties of wheat flour significantly through affecting structural characteristics, amylase activity and exothermic transition, especially during the early stage of hydrolysis. Peak viscosity, trough, final, integral area, and setback significantly decreased along with the increasing concentration of papain. Both hydrolysis time and concentration of papain had obviously effect on the breakdown. Pasting temperature and pasting time increased significantly with the enhancement of papain concentration. Hydrolysis time exerted minor effect on the pasting temperature and pasting time. The average peak time was slightly prolonged by lower concentration of papain, otherwise slightly shortened by higher concentration.