Editorial for the Special Issue, "Quality Assay, Processing and Bio-Function of Rice Products".

Rice is one of the most important cereals in the world alongside wheat and maize [...].

Rice Bran Stabilisation and Oil Extraction Using the Microwave-Assisted Method and Its Effects on GABA and Gamma-Oryzanol Compounds.

Rice bran oil (RBO) is a valuable ingredient extracted from rice bran (RB), a side stream of polishing rice grain in the milling process. RBO is rich in bioactive ingredients with potential health benefits, such as gamma-oryzanol (GO) and gamma-aminobutyric acid (GABA). Despite its benefits, the quality of RBO depends on the degree of stabilisation of the RB, which is easily affected by lipase enzymes, and thus needs an effective treatment prior to RBO production. To assess the potential of the microwave-assisted method for RB stabilisation and RBO extraction, three Carolino rice varieties (Ariete, Teti, Luna) were tested. The effect of RB stabilisation was evaluated via acid value, water absorption, and GO and GABA levels. The RBO yield was optimised by solvent, temperature, and solvent-to-sample ratio, and the GO and fatty acid levels were determined. The RB stabilisation for the Luna variety did not affect the GO and GABA; for the Ariete and Teti varieties, the GO decreased by 34.4% and 24.2%, and the GABA increased by 26.5% and 47.0%, respectively. The GO levels in RBO samples were not affected by RB stabilisation. The RBO nutritional value was confirmed by the suitable ratio (>2) between polyunsaturated (PUFA) and saturated fatty acids (SFA), with the Teti variety presenting the highest ratio.

Use of Artificial Neural Network Model for Rice Quality Prediction Based on Grain Physical Parameters.

The main goal of this study was to test the ability of an artificial neural network (ANN) for rice quality prediction based on grain physical parameters and to conduct a comparison with multiple linear regression (MLR) using 66 samples in duplicate. The parameters used for rice quality prediction are related to biochemical composition (starch, amylose, ash, fat, and protein concentration) and pasting parameters (peak viscosity, trough, breakdown, final viscosity, and setback). These parameters were estimated based on grain appearance (length, width, length/width ratio, total whiteness, vitreous whiteness, and chalkiness), and milling yield (husked, milled, head) data. The MLR models were characterized by very low coefficient determination (R2 = 0.27-0.96) and a root-mean-square error (RMSE) (0.08-0.56). Meanwhile, the ANN models presented a range for R2 = 0.97-0.99, being characterized for R2 = 0.98 (training), R2 = 0.88 (validation), and R2 = 0.90 (testing). According to these results, the ANN algorithms could be used to obtain robust models to predict both biochemical and pasting profiles parameters in a fast and accurate form, which makes them suitable for application to simultaneous qualitative and quantitative analysis of rice quality. Moreover, the ANN prediction method represents a promising approach to estimate several targeted biochemical and viscosity parameters with a fast and clean approach that is interesting to industry and consumers, leading to better assessment of rice classification for authenticity purposes.

Alleles to Enhance Antioxidant Content in Maize-A Genome-Wide Association Approach.

The interest in antioxidant compound breeding in maize (Zea mays L.), a major food crop, has increased in recent years. However, breeding of antioxidant compounds in maize can be hampered, given the complex genetic nature of these compounds. In this work, we followed a genome-wide association approach, using a unique germplasm collection (containing Portuguese germplasm), to study the genetic basis of several antioxidants in maize. Sixty-seven genomic regions associated with seven antioxidant compounds and two color-related traits were identified. Several significant associations were located within or near genes involved in the carotenoid (Zm00001d036345) and tocopherol biosynthetic pathways (Zm00001d017746). Some indications of a negative selection against α-tocopherol levels were detected in the Portuguese maize germplasm. The strongest single nucleotide polymorphism (SNP)-trait associations and the SNP alleles with larger effect sizes were pinpointed and set as priority for future validation studies; these associations detected now constitute a benchmark for developing molecular selection tools for antioxidant compound selection in maize.

Genome-wide association study for kernel composition and flour pasting behavior in wholemeal maize flour.

BACKGROUND: Maize is a crop in high demand for food purposes and consumers worldwide are increasingly concerned with food quality. However, breeding for improved quality is a complex task and therefore developing tools to select for better quality products is of great importance. Kernel composition, flour pasting behavior, and flour particle size have been previously identified as crucial for maize-based food quality. In this work we carried out a genome-wide association study to identify genomic regions controlling compositional and pasting properties of maize wholemeal flour. RESULTS: A collection of 132 diverse inbred lines, with a considerable representation of the food used Portuguese unique germplasm, was trialed during two seasons, and harvested samples characterized for main compositional traits, flour pasting parameters and mean particle size. The collection was genotyped with the MaizeSNP50 array. SNP-trait associations were tested using a mixed linear model accounting for genetic relatedness. Fifty-seven genomic regions were identified, associated with the 11 different quality-related traits evaluated. Regions controlling multiple traits were detected and potential candidate genes identified. As an example, for two viscosity parameters that reflect the capacity of the starch to absorb water and swell, the strongest common associated region was located near the dull endosperm 1 gene that encodes a starch synthase and is determinant on the starch endosperm structure in maize. CONCLUSIONS: This study allowed for identifying relevant regions on the maize genome affecting maize kernel composition and flour pasting behavior, candidate genes for the majority of the quality-associated genomic regions, or the most promising target regions to develop molecular tools to increase efficacy and efficiency of quality traits selection (such as "breadability") within maize breeding programs.

Diversity of global rice markets and the science required for consumer-targeted rice breeding.

With the ever-increasing global demand for high quality rice in both local production regions and with Western consumers, we have a strong desire to understand better the importance of the different traits that make up the quality of the rice grain and obtain a full picture of rice quality demographics. Rice is by no means a 'one size fits all' crop. Regional preferences are not only striking, they drive the market and hence are of major economic importance in any rice breeding / improvement strategy. In this analysis, we have engaged local experts across the world to perform a full assessment of all the major rice quality trait characteristics and importantly, to determine how these are combined in the most preferred varieties for each of their regions. Physical as well as biochemical characteristics have been monitored and this has resulted in the identification of no less than 18 quality trait combinations. This complexity immediately reveals the extent of the specificity of consumer preference. Nevertheless, further assessment of these combinations at the variety level reveals that several groups still comprise varieties which consumers can readily identify as being different. This emphasises the shortcomings in the current tools we have available to assess rice quality and raises the issue of how we might correct for this in the future. Only with additional tools and research will we be able to define directed strategies for rice breeding which are able to combine important agronomic features with the demands of local consumers for specific quality attributes and hence, design new, improved crop varieties which will be awarded success in the global market.