|JONDIKO, TOM - Texas A&M University|
|YANG, LIYI - Texas A&M University|
|Tilley, Michael - Mike|
|AWIKA, JOSEPH - Texas A&M University|
Submitted to: Meeting Abstract
Publication Type: Abstract Only
Publication Acceptance Date: 9/1/2013
Publication Date: 9/29/2013
Citation: Jondiko, T.O., Yang, L., Tilley, M., Awika, J.M. 2013. Prediction of tortilla quality using multivariate model of kernel, flour and dough properties. AACC International 2013 Annual Meeting. Meeting Abstract. Paper No. 25-P.
Technical Abstract: Advances in high-throughput wheat breeding techniques have resulted in the need for rapid, accurate and cost-effective means to predict tortilla making performance for large number of early generation wheat lines. Currently, the most reliable approach is to process tortilla which is laborious, time consuming, expensive and requires large sample size. This study used a multivariate discriminant analysis to predict tortilla quality using kernel, flour and dough properties. A discriminant rule (suitability = diameter > 165mm + day 16 flexibility score >3.0) used to classify wheat lines for suitability in making good quality tortillas. Wheat varieties from Texas (n = 86) were evaluated for kernel (hardness, diameter, and weight), flour (protein content, fractions and composition), dough (compression force, extensibility and stress relaxation from TA-XT2i) and tortilla properties (diameter, rheology and flexibility). First three principal components explained 62% of variance. Multivariate normal distribution of the data was determined (Shapiro-Wilk p <0.4227). Canonical correlation analysis revealed significant correlation between kernel and tortilla properties (r= 0.83), kernel hardness contributed the highest to this correlation. Flour and tortilla properties were highly correlated (r = 0.88), Glutenin to Gliadin ratio (Glu:Gli) contributed highest to this correlation and can predict tortilla flexibility and deformation modulus. Dough and tortilla properties were significantly correlated (r = 0.91). Logistic regression and stepwise variable selection identified an optimum model comprised of kernel hardness, Glu:Gli, dough extensibility and compression force as the most important variables. Cross-validation indicated an 89% prediction rate for the model. This emphasizes the feasibility and practicality of the model using variables that are easily and quickly measured.