Measuring Head Rice Recovery in Rice.

Head rice recovery (HRR) is a milling quality attribute that is highly influential toward the market price of rice. It is defined as the proportion of paddy rice that retains 75% of its length after milling. For a new rice variety to be accepted and adopted by farmers, the new variety's HRR should satisfy consumer requirements of at least 55% or above. Hence, HRR is a crucial attribute by which new varieties are selected for release. Although the amount of head rice recovered depends on the genetic background of a rice variety, HRR is also highly affected by postharvest processing conditions that the variety goes through. To determine the maximum HRR, therefore, one must ensure that the processing conditions are as optimal as possible. This book chapter outlines how paddy rice is processed into head rice and how HRR is measured. It also proposes an improved laboratory-scale means for postharvest drying to minimize head rice losses.

Measurement of Rice Grain Dimensions and Chalkiness, and Rice Grain Elongation Using Image Analysis.

Measurements of rice grain dimensions, percent grain chalkiness, and grain elongation used to be tedious and slow due to the manual nature of measurements (e.g., use of calipers to measure grains one at a time) and the subjective nature of scoring based on visual inspection (i.e., chalkiness). Recent developments in imaging technologies have enabled more high-throughput means for measuring physical traits (i.e., grain dimensions and chalkiness) in raw grains and grain elongation by comparing ratio between raw versus cooked rice. The digital images of rice grains are captured through computer scanning and analyzed using software that can calculate area and pixel value statistics of user-defined parameters. The improvements in throughput made possible by the use of imaging technologies will allow faster quality grading of rice varieties. Market quality is usually defined based on the rice grain physical traits (grain size and shape), degree of chalkiness, and the ability of rice to elongate on cooking. In this chapter, the routine methods to measure the physical traits of rice and grain elongation using image analysis are described.

Method Development of Near-Infrared Spectroscopy Approaches for Nondestructive and Rapid Estimation of Total Protein in Brown Rice Flour.

Rice varietal development and improvement programs are constantly seeking means to shorten the breeding cycle in order to deliver new, consumer-acceptable rice varieties to farmers and to consumers. Advances in molecular biology technologies have enabled breeders to use high-throughput genotyping to screen breeding lines. However, current phenotyping technologies, particularly for rice cooking and eating properties, have yet to match the efficiency of genotyping methodologies. A high-throughput and cost-effective phenotyping suite is essential because without phenotype, the value of genotypic information cannot be maximized. In this book chapter, we explore the application of near-infrared spectroscopy (NIRS), a high-throughput and nondestructive approach in characterizing rice grains, primarily describing method development and validation, instrument calibration, upgrading, and maintenance. We then focus on estimating protein content (PC) in brown rice as a case study because (1) PC is an attribute that contributes to the cooking behavior and the eating properties of cooked rice; and (2) proteins contain chemical bonds that can easily be detected by NIRS.

Multi-Dimensional Cooking Quality Classification Using Routine Quality Evaluation Methods.

A battery of assays to characterize the cooking and eating attributes of rice have been in routine use for several decades. The classification system to group rice varieties into different quality types are often based on cooking and eating attributes defined based on amylose content, rather than being considered a set of attributes contributing to an overall quality type based on multi-dimensional approach. In this chapter, the methods developed to measure the cooking quality attributes of rice are described. Instead of considering each attribute on its own, the authors employ multidimensional data generated from the estimation of amylose content, gel consistency, gelatinization temperature, Rapid Visco-Analyzer parameters to classify rice into distinct cooking quality ideotypes. If used universally, such an approach can improve prediction of cooking quality classifications of rice varieties in the breeding programs.

Characterization of Mechanical Texture Attributes of Cooked Milled Rice by Texture Profile Analyses and Unraveling Viscoelasticity Properties Through Rheometry.

The mechanical attributes of cooked rice grains reflected by textural characteristics capture consumers preferences. Two of these attributes such as hardness and stickiness are typically indicated in grain quality evaluation programs by the amylose content of rice. However, the association of amylose content with two other textural attributes such as cohesiveness and springiness remains unknown. Hence, texture profile analyses play a role in quantifying these mechanical parameters of texture. Rheometry on the other hand can be utilized to characterize both viscous and elastic properties of rice during cooking in a water-rice proportion closer to what consumers typically use for cooking. In this chapter, methods for texture profiling and rheometry are presented to capture inferences on cooking quality modeling. Data extracted from rice texture and viscoelastic properties that go beyond what amylose content can predict, has been deciphered through mathematical modeling, which can help predict cooking quality of new rice breeding lines to improve textural and cooking quality specifications.

Determination of Macronutrient and Micronutrient Content in Rice Grains Using Inductively Coupled Plasma-Optical Emission Spectrometry (ICP-OES).

The rice grain endosperm is mostly composed of starch , which serves as the major source of calories for more than half of the world's population. Macro and micronutrients make a minor proportion of the rice grain, which particularly gets accumulated in outer aleurone layer, which are in general eliminated upon milling. Because rice is the major staple, it is seen as an efficient mechanism for delivering both macro- and micronutrients, particularly for the poor who do not have ample access to diversified diets. Enriching micronutrient and macronutrient concentrations in milled rice of endosperm and/or in brown rice, is an important dietary intervention to create health benefits of rice consumers. Efforts are underway to increase the nutritional content of rice through bio/fortification approaches. The plant takes up these same elements from the soil, redirect the transport of these elements into the grain. Thus besides biofortification strategies, scientists can also use the knowledge to design proper soil nutrient management to enrich micronutrients in the grains. Therefore, it is important to be able to determine the macro- and the micronutrient composition of the vegetative parts of the rice plant and of the rice grain. In this chapter, nitric-perchloric acid digestion and inductively coupled plasma-optical emission spectrometry (ICP-OES) methods routinely used in IRRI's Grain Quality and Nutrition Services Laboratory (GQNSL) to determine the concentrations of various macro- and micronutrients found in the rice grain and the rice plant, are described.

Determination of Cadmium Concentration in Milled and Brown Rice Grains Using Graphite Furnace Atomic Absorption Spectrometry.

Heavy metal pollution is a growing public health concern since it poses a food risk to public health via metal transfer. Cadmium is of particular concern because it is a potential carcinogen if exceed tolerable limits in the grain. Hence, it is important to monitor the cadmium content of rice before it reaches the market to ensure public healthy safety, especially in areas known to have high cadmium levels in soil. In this chapter, the method used to determine the concentration of cadmium in milled and brown rice grain samples is described. This method involves sample digestion with concentrated nitric acid and hydrogen peroxide and analysis of cadmium by Graphite Furnace Atomic Absorption Spectrometry (GF-AAS). Because cadmium concentrations are low in rice grains, quantification of cadmium content requires the use of a more sensitive instrument, such as GF-AAS.

Deciphering the Genetic Architecture of Cooked Rice Texture.

The textural attributes of cooked rice determine palatability and consumer acceptance. Henceforth, understanding the underlying genetic basis is pivotal for the genetic improvement of preferred textural attributes in breeding programs. We characterized diverse set of 236 Indica accessions from 37 countries for textural attributes, which includes adhesiveness (ADH), hardness (HRD), springiness (SPR), and cohesiveness (COH) as well as amylose content (AC). A set of 147,692 high quality SNPs resulting from genotyping data of 700K high Density Rice Array (HDRA) derived from the Indica diversity panels of 218 lines were retained for marker-trait associations of textural attributes using single-locus (SL) genome wide association studies (GWAS) which resulted in identifying hotspot on chromosome 6 for AC and ADH attributes. Four independent multi-locus approaches (ML-GWAS) including FASTmrEMMA, pLARmEB, mrMLM, and ISIS_EM-BLASSO were implemented to dissect additional loci of major/minor effects influencing the rice texture and to overcome limitations of SL-based GWAS approach. In total 224 significant quantitative trait nucleotide (QTNs) were identified using ML-GWAS, of which 97 were validated with at least two out of the four multi-locus methods. The GWAS results were in accordance with the very significant negative correlation (r = -0.83) observed between AC and ADH, and the significant correlation exhibited by AC (r < 0.4) with HRD, SPR, and COH. The novel haplotypes and putative candidate genes influencing textural properties beyond AC will be a useful resource for deployment into the marker assisted program to capture consumer preferences influencing rice texture and palatability.

Multivariate-based classification of predicting cooking quality ideotypes in rice (Oryza sativa L.) indica germplasm.

BACKGROUND: For predicting texture suited for South and South East Asia, most of the breeding programs tend to focus on developing rice varieties with intermediate to high amylose content in indica subspecies. However, varieties within the high amylose content class may still be distinguishable by consumers, who are able to distinguish texture that cannot be differentiated by proxy cooking quality indicators. RESULTS: This study explored a suite of assays to capture viscosity, rheometric, and mechanical texture parameters for characterising cooked rice texture in a set of 211 rice accessions from a diversity panel and employed multivariate approaches to classify rice varieties into distinct cooking quality classes. Results suggest that when the amylose content range is narrowed to the intermediate to high classes, parameters determined by rheometry and RVA become diagnostic. Modeled parameters distinguishing cooking quality ideotypes within the same range of amylose classes differ in textural parameters scored by a descriptive sensory panel. CONCLUSIONS: Our results reinforced the notion that it is important to define cooking quality classes in indica subtypes based on multidimensional parameters, by going beyond amylose predictions. These predictive cooking models will be handy in capturing cooking and eating quality properties that address consumer preferences in future breeding programs. Policy implications of such findings may lead to changes in criteria used in assessing grain quality in the intermediate to high amylose classes.