Prediction of wheat tortilla quality using multivariate modeling of kernel, flour, and dough properties
Academic Article
Overview
Research
Identity
Additional Document Info
Other
View All
Overview
abstract
2016 Elsevier Ltd. All rights reserved. Traditional wheat quality methods for bread have poor predictive power for flatbread quality, which impedes genetic improvement of wheat for the growing market. We used a multivariate discriminant analysis to predict tortilla quality using a set of 16 variables derived from kernel properties, flour composition, and dough rheological properties of 187 experimental hard wheat samples grown across Texas. A discriminant rule (suitability for tortillas = diameter > 165 mm + day 16 flexibility score > 3.0) was used to classify samples. Multivariate normal distribution of the data was established (Shapiro-Wilk p > 0.05). Logistic regression and stepwise variable selection identified an optimum model comprising kernel weight, glutenin-gliadin ratio, insoluble polymeric proteins, and dough extensibility and stress relaxation parameters, as the most important variables. Cross-validation indicated 83% model prediction efficiency. This work provides important insight on potential targets for wheat quality genetic improvement for tortillas and specialty product market. Industrial relevance Tortillas and other flatbread manufacturers currently use wheat developed for other commodities and rely on trial and error, and use of various additives to optimize product quality. Genetic development of wheat for these markets is impeded by lack of knowledge of specific grain quality parameters to target. With the growing demand for clean label and healthy offerings by consumers, the industry is looking for natural ingredients with improved functionality. This work provides the first insight into the specific wheat composition and functional parameters that can predict tortilla quality.
