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

Abstract

BackgroundFor 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.ResultsThis 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.ConclusionsOur 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.Electronic supplementary materialThe online version of this article (10.1186/s12284-018-0245-y) contains supplementary material, which is available to authorized users.

Effects of soluble starch synthase IIa allelic variation on rice grain quality with different Waxy backgrounds

“…According to result of cluster analysis for different combinations of Wx and SSIIa, as well as relationship between AAC and PTm for the different SSIIa haplotypes, AAC of samples should be classified as two classes: low (7.8–19.75%), middle‐high AAC (20.22–34.92%) to facilitate in dissecting the difference between PTm and GT. The parameters determined by RVA become diagnostic when the AAC range of samples is narrowed to the intermediate to high classes 28 . This also indicates that the effects of SSIIa alleles are affected by different Wx alleles.…”

“…The parameters determined by RVA become diagnostic when the AAC range of samples is narrowed to the intermediate to high classes. 28 This also indicates that the effects of SSIIa alleles are affected by different Wx alleles.…”

Genomics‐assisted breeding for successful development of multiple‐stress‐tolerant, climate‐smart rice for southern and southeastern Asia

“…Absorbance of the samples for amylose–iodine complex at a wavelength of 620 nm was measured, and AC was quantified using the standard curve for known AC values. The detailed procedure was followed as previously described (Cuevas, Domingo, & Sreenivasulu, 2018). On the basis of AC content, milled rice can be grouped into following classes: waxy, 0–2%; very low, 3–9%; low, 10–19%; intermediate, 20–25%, and high, >25% (IRRI, 2002).…”

Dataset on viscosity and starch polymer properties to predict texture through modeling

“…A set of samples ( n = 110) from the rice diversity Indica panel was chosen and were cooked as prescribed by Cuevas et al. [8] , [9] . Trained set of panelist, selected based on their availability was chosen and were cooked as prescribed by Cuevas et al.…”

“…Trained set of panelist, selected based on their availability was chosen and were cooked as prescribed by Cuevas et al. [8] , [9] . Trained set of panelist, selected based on their availability and previous training, evaluated the texture profile of the samples based on the properties presented in Table 1 .…”