Quality evaluation of traditional and aromatic Italian rice varieties

The cooking quality of rice is the most important characteristic that affects consumer acceptance. Different methodologies have been proposed to evaluate the cooking behaviour of rice; as quality predictors amylose content, alkali spreading scores, paste viscosity and gelatinization temperature are considered. This work reports the results of a collaborative study between the Department of Food Science and Microbiology (University of Milan) and C.R.A. - Experimental Institute for Cereal Crop regarding the quality evaluation of eleven Italian varieties of rice (six traditional and five aromatic) by applying different approaches. Image analysis was used as a tool for measuring the morphological characteristics of the kernel, before and after dehulling. To predict cooking quality, samples were analyzed for protein, starch, damaged starch, amylose and α-amylase content, extent of alkali degradation, and pasting behaviour. The hardness and stickiness of cooked rice were measured via a texture analyser by applying an official methodology. The results were statistically evaluated (Correlation and Principal Components Analysis) in order to find the significant correlations between variables and similarities among samples. The data obtained from the different procedures provide an extensive description of the quality of the eleven varieties examined. The results regarding the set up of an objective procedure for the alkali test and the retrogradation behaviour of pasted gels are reported in two posters. The main results related to the cooking properties are here summarized. The rheological parameters measured on cooked kernel made it possible to differentiate the group of the traditional varieties from the aromatic types; the latter group presented a higher hardness and a lower stickiness, with the exception of cv. Giano, characterized by a lower amylose content, in comparison with the other aromatic samples. The texture characteristics of cooked samples were statistically correlated with the amylographic indices (gelatinisation temperature, maximum viscosity, final viscosity and setback), thus confirming the importance of the latter parameters in predicting cooking behaviour. The PCA analysis showed that discrimination among the samples was based not only on amylose, but also on protein content.