Differences in physicochemical properties of commercial rice from urban markets in West Africa

Abstract

Rice consumers in West Africa (WA) have an acquired preference for imported rice. Enhancing consumption of local rice requires matching the grain quality attributes of the imported benchmarks in addition to increasing productivity of local rice cultivars. Thus, there is a need to develop screening tools that will aid breeding programs select for high-yielding and stress-tolerant cultivars whose grain quality are at par with imported rice. Hence, this study evaluated various grain quality characteristics of 316 commercial milled rice samples from urban markets in three WA countries (Benin, Cameroon, and Ghana) and developed linear discriminant models (LDAs) to classify rice according to their origins and to predict the imported rice classification of local germplasm based on their grain quality attributes. More than half of the commercial rice samples that were collected originated from Thailand (60%); in contrast, only a small fraction was locally grown (2%). The commercial rice from different origins were distinguishable based on the quality attributes evaluated, contributing to the relatively high classification rates achieved by the fitted LDAs. These results indicate that multivariate models could be useful during varietal improvement as tools for screening for cultivars that can match the quality of imported rice.

Keywords: Consumer preference; Grain quality; Multivariate analysis; Varietal improvement.

Fig. 1

Canonical centroid plot of grain quality attributes of rice from different origins depicting differences in the composite quality of rice from different countries

Fig. 2

Receiver operating characteristic (ROC) curve of a LDA model 1 b LDA model 2. LDA model 1 contains 15 grain quality attributes as predictors namely: Impurities, head rice ratio, grain dimensions (i.e., length, width, and length-to-width ratio), chalky area, color (i.e., L, a, b values), apparent amylose content, paste viscosities (i.e., peak, breakdown and setback); cooking properties (i.e., cooking time and swelling ratio). LDA model 2 contains 13 grain quality attributes as predictors. Impurities and head rice ratio are excluded as these are more likely to be affected by processing factors (color figure online)