Prediction of wheat tortilla quality using multivariate modeling of kernel, flour and dough properties

Authors: JONDIKO, TOM - Texas A&M University; YANG, LIYI - Texas A&M University; HAYS, DIRK - Texas A&M University; IBRAHIM, AMIR - Texas A&M University; Tilley, Michael - Mike; AWIKA, JOSEPH - Texas A&M University

Submitted to: Innovative Food Science and Emerging Technologies
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 1/1/2016
Publication Date: 1/27/2016
Publication URL: http://handle.nal.usda.gov/10113/61856
Citation: Jondiko, T.O., Yang, L., Hays, D.B., Ibrahim, A., Tilley, M., Awika, J.M. 2016. Prediction of wheat tortilla quality using multivariate modeling of kernel, flour and dough properties. Innovative Food Science and Emerging Technologies. 34:9-15. https://doi.org/10.1016/j.ifset.2016.01.010.
DOI: https://doi.org/10.1016/j.ifset.2016.01.010

Interpretive Summary: Tortillas are currently an integral part of the American diet and growing globally. To consumers, the definition of good quality tortilla encompasses its ability to retain flexibility and be large enough to wrap food. Despite the growing popularity of tortillas, the main challenge is that there is no reliable and practical method to predict wheat functionality for tortillas. Currently, the only way to predict wheat functionality for tortillas is to actually make the product. A major challenge to predicting tortilla quality is the negative correlation that generally exists between the main quality factors, tortilla diameter, and flexibility during storage. Large diameter generally requires weak and extensible dough, which tends to be detrimental to tortilla flexibility. This study used a set of 16 variables derived from kernel properties, flour composition, dough mixing and rheological properties of 187 hard winter wheat samples to predict tortilla quality. Results indicated 83% prediction efficiency for the model. This work provides important insight on wheat quality attributes that may be used to predict the functional performance of wheat varieties for tortilla processing and targets for genetic improvement.

Technical Abstract: Wheat grain attributes that influence tortilla quality are not fully understood. This impedes genetic improvement efforts to develop wheat varieties for the growing market. This study used a multivariate discriminant analysis to predict tortilla quality using a set of 16 variables derived from kernel properties, flour composition, and dough mixing and rheological properties of 187 experimental hard winter wheat samples grown across Texas. A discriminant rule (suitability = diameter > 165mm + day 16 flexibility score >3.0) was used to classify wheat lines for suitability to make good quality tortillas. Samples were highly diverse in all the traits measured, thus providing a broad spectrum to capture attributes relevant to tortilla quality. 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, and dough extensibility, stress relaxation and compression force as the most important variables. Cross-validation indicated 83% prediction efficiency for the model. Interestingly, traditional wheat quality variables like protein content and dough mixing properties did not make it into the best model. This work provides important insight on wheat quality attributes that may be targets for genetic improvement for tortillas and flatbreads market.