Title: Cluster analysis of lowland and upland rice cultivars based on grain quality attributes Authors
Submitted to: Cereal Foods World
Publication Type: Abstract Only
Publication Acceptance Date: May 15, 2009
Publication Date: September 1, 2009
Citation: Bassinello, P.Z., De Morais, O.P., De Oliveira, J.P., Chen, M., McClung, A.M. 2009. Cluster analysis of lowland and upland rice cultivars based on grain quality attributes. Cereal Foods World 54:A34. Technical Abstract: Rice is cropped in many countries all over the world and plays an important role in human nutrition as well as in agricultural economics, besides its social importance. Embrapa Rice and Beans is responsible for national rice enhancement programs and is conducting breeding projects to increase yield and grain quality. A big challenge has been to characterize rice quality based on indirect methods and, especially for lowland cultivars, differentiate the cooking or technological properties of those samples with similar amylose content. The aim of this work was to evaluate some quality parameters in different rice cultivars from upland and lowland systems and submit them to the cluster analysis. The following analyses were done: apparent amylose content, gelatinization temperature (alkali test), RVA and cooking test according to standard methods. Data analyses were performed based on cluster and corr procedure using Statistical Analysis System (SAS Institute 2002). Based on the results, most of the samples were classified as intermediate apparent amylose, but presented different gelatinization temperatures (low and intermediate) and viscoamylographic profiles. The cluster analysis showed at least three main groups based on all studied parameters and it was possible to separate one group for lowland rice and two others for upland rice. Only one irrigated rice recommended for tropical areas was out of the lowland group. The main attributes which seem to affect this pattern were the apparent amylose content and gelatinization temperature, when considering all the attributes together. When only the RVA results are considered, a different profile is exhibited by the cluster analysis, showing new combinations. The analysis also revealed the cultivars from both systems with similar patterns for almost all attributes.