Abstract
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.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Blakeney AB, Lewin L, Reinke RF (2001) Quality rice for North Asia. Rural industries research and development corporation. RIRDC, Kingston, ACT, p 34
Deffenbaugh LB, Walker CE (1989) Comparison of starch pasting properties in the brabender viscoamylograph and the rapid visco-analyser. Cereal Chem 66:493–499
Cagampang GB, Perez CM, Juliano BO (1973) A gel consistency test for eating quality in rice. J Sci of Food and Agr 24:1589–1594
Kumar I, Khush GS, Juliano BO (1987) Genetic analysis of waxy locus in rice (Oryza sativa L.). Theor Appl Genet 73:481–488
Graham R (2002) A proposal for IRRI to establish a grain quality and nutrition research center. IRRI Discussion Paper Series No. 44. International Rice Research Institute, Los Banos, p 15
Cuevas RP, Daygon VD, Corpuz HM, Reinke RF, Waters DLE, Fitzgerald MA (2010) Melting the secrets of gelatinisation temperature in rice. Funct Plant Biol 37:439–447
Cuevas RP, Fitzgerald MA (2012) Genetic diversity of rice grain quality. In: Caliskan M (ed) Genetic diversity in plants. InTech, Rijeka, pp 285–310
Tuaño AP, Perez LM, Padolina TF, Juliano BO (2015) Survey of grain quality of Philippine farmers’ specialty rices. Phil Agric Sci 98:446–456
Zhao X, Zhou L, Ponce K, Ye G (2015) The usefulness of known genes/QTLs for grain quality traits in an indica population of diverse breeding lines tested using association analysis. Rice 8:29
Fitzgerald MA, Martin M, Ward RM, Park WD, Shead HJ (2003) Viscosity of rice flour: a rheological and biological study. J Agri Food Chem 51:2295–2299
Anacleto R, Cuevas RP, Jimenez R, Llorente C, Nissila E, Henry RJ, Sreenivasulu N (2015) Prospects of breeding high-quality rice using post-genomic tools. Theor Appl Genet 128:1449–1466
Collard BCY, Septiningsih EM, Das SR, Carandang J, Pamplona AM, Sanchez DL, Kato Y, Ye G, Reddy JN, Singh US, Iftekharuddaula KM, Venuprasad R, Vera Cruz CN, Mackill DJ, Ismail AM (2013) Developing new flood-tolerant varieties at the International Rice Research Institute (IRRI). SABRAO J Breed Genet 45:42–56
Gregorio GB, Islam MR, Vergara GV, Thirumeni S (2013) Recent advances in rice science to design salinity and other abiotic stress tolerant rice varieties. SABRAO J Breed Genet 45:31–41
International Organization for Standardization (2015) ISO 6647-1: 2015-Rice—Determination of amylose content—Part 1: Reference method p 1–4
International Organization for Standardization (2015) ISO 6647-2: 2015–Rice—Determination of amylose content—Part 2: Routine methods p 1–4
TA Instruments (2003) Differential scanning calorimeter q series getting started guide. TA Instruments –Waters LLC, New Castle
American Association of Cereal Chemists Inc (2000) Approved methods of the American Association of Cereal Chemists. American Association of Cereal Chemists International, St. Paul, MN
Acknowledgments
The authors thank Dennis Villegas, Teodoro Atienza, Leah Villanueva, Jennine Rose Lapis, Reah Gonzales, Eric Jhon Cruz, Marnol Santos, Ruben Chavez, and Mitzi Alodia Asih for assistance in processing and collecting data for the samples used in the case study and in the validation and optimization of the methodologies in GQNSL. This work has been supported under the CGIAR thematic area Global Rice Agri-Food System CRP, RICE, Stress-Tolerant Rice for Africa and South Asia (STRASA) Phase III funding.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Science+Business Media, LLC, part of Springer Nature
About this protocol
Cite this protocol
Molina, L., Jimenez, R., Sreenivasulu, N., Cuevas, R.P.O. (2019). Multi-Dimensional Cooking Quality Classification Using Routine Quality Evaluation Methods. In: Sreenivasulu, N. (eds) Rice Grain Quality. Methods in Molecular Biology, vol 1892. Humana Press, New York, NY. https://doi.org/10.1007/978-1-4939-8914-0_8
Download citation
DOI: https://doi.org/10.1007/978-1-4939-8914-0_8
Published:
Publisher Name: Humana Press, New York, NY
Print ISBN: 978-1-4939-8912-6
Online ISBN: 978-1-4939-8914-0
eBook Packages: Springer Protocols