Grain quality evaluation of japonica rice effected by cultivars, environment, and their interactions based on appearance and processing characteristics

Genetic architecture of head rice and rice chalky grain percentages using genome-wide association studies

High head rice and low chalky grain percentages are key grain quality traits selected in developing rice cultivars. The objectives of this research were to characterize the phenotypic variation of head rice and chalky grain percentages in a diverse collection of rice accessions, identify single nucleotide polymorphism (SNP) markers associated with each of these traits using genome-wide association studies (GWAS), and identify putative candidate genes linked to the SNPs identified by GWAS. Diverse rice varieties, landraces, and breeding lines were grown at the Texas A&M AgriLife Research Center in Beaumont. Head rice percentages (HRP) and chalky grain percentages (CGP) of 195 and 199 non-waxy accessions were estimated in 2018 and 2019, respectively. Phenotypic data were analyzed along with 854,832 SNPs using three statistical models: mixed linear model (MLM), multi-locus mixed model (MLMM), and fixed and random model circulating probability unification (FarmCPU). Significant variations in HRP and CGP were observed between rice accessions. Two significant marker-trait associations (MTAs) were detected on chromosomes 1 and 2, respectively, based on best linear unbiased prediction (BLUP) values in 2018, while in 2019, one SNP was significantly associated with HRP in each of chromosomes 6, 8, 9, and 11, and two in chromosome 7. CGP was significantly associated with five SNPs located in chromosomes 2, 4, 6, and 8 in the 2018 study and ten SNPs in chromosomes 1, 2, 3, 4, 7, 8, 11, and 12 in the 2019 study. The SNPs are located within or linked to putative candidate genes involved in HRP and CGP. This study reports five and ten novel MTAs for HRP and CGP, respectively, while three and five MTAs co-located with previously reported quantitative trait loci for HRP and CGP, respectively. The validation of candidate genes for their roles in determining HRP and CGP is necessary to design functional molecular markers that can be used to effectively develop rice cultivars with desirable grain quality.

The Effects of Genotype × Environment on Physicochemical and Sensory Properties and Differences of Volatile Organic Compounds of Three Rice Types (Oryza sativa L.)

Understanding the effects of genotype, environment and their interactions on rice quality is of great importance for rice breeding and cultivation. In this study, six rice varieties with two indica, two japonica and two indica-japonica types of rice were selected and planted at ten locations in Zhejiang Province to investigate the genotype (G) × environment (E) on physicochemical and sensory properties and the differences of volatile organic compounds (VOCs) among the three types of rice. Analysis of variances showed that apparent amylose content (AC), total protein content (PC), alkali spreading value (ASV), RVA profiles, and appearance (ACR), palatability (PCR), and sensory evaluation value (SEV) of cooked rice and texture of cooled cooked rice (TCCR) were mainly affected by genotypic variation, whereas the smell of cooked rice (SCR) was mainly affected by environment (p < 0.05). The G × E effect was significant for most parameters. The weather in the middle and late periods of filling had important effects on the formation of rice quality, especially on setback (SB) and pasting temperature (PT) (p < 0.01). They were negatively correlated with the texture of cooked rice (TCR) and SEV (p < 0.05). Peak viscosity (PV) and breakdown (BD) were positively related to the sensory evaluation parameters (p < 0.01) and could be used to predict cooked rice quality. A total of 59 VOCs were detected, and indica, japonica and indica-japonica had 9, 6 and 19 characteristic compounds, respectively. The principal component analysis showed that the physicochemical and sensory properties and VOCs of indica-japonica rice were more stable than those of indica and japonica rice at ten locations in Zhejiang Province. It is helpful for rice breeders to understand how the environment affects the physicochemical, sensory properties and VOCs of the three rice types, and it is also important for food enterprises to provide rice products with stable quality.

Rapid Assessment of Rice Quality Traits Using Low-Cost Digital Technologies

Aroma and other physicochemical parameters are important attributes influencing consumer perception and acceptance of rice. However, current methods using multiple instruments and laboratory analysis make these assessments costly and time-consuming. Therefore, this study aimed to assess rice quality traits of 17 commercial rice types using a low-cost electronic nose and portable near-infrared spectrometer coupled with machine learning (ML). Specifically, artificial neural networks (ANN) were used to classify the type of rice and predict rice quality traits (aromas, color, texture, and pH of cooked rice) as targets. The ML models developed showed that the chemometrics obtained from both sensor technologies successfully classified the rice (Model 1: 98.7%; Model 2: 98.6%) and predicted the peak area of aromas obtained by gas chromatography-mass spectroscopy found in raw (Model 3: R = 0.95; Model 6: R = 0.95) and cooked rice (Model 4: R = 0.98; Model 7: R = 0.96). Furthermore, a high R = 0.98 was obtained for Model 5 to estimate the color, texture, and pH of cooked rice. The proposed method is rapid, low-cost, reliable, and may help the rice industry increase high-quality rice production and accelerate the adoption of digital technologies and artificial intelligence to support the rice value chain.