Prediction of Tortilla Quality Using Multivariate Modeling of Kernel, Flour and Dough Properties
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 larger numbers of early generation wheat lines. Currently, the most reliable approach
is to process tortillas. This approach 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) was used to classify wheat lines for suitability in
making good quality tortillas. One hundred eighty seven hard winter wheat (HWW)
varieties from Texas 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).
The first three principal components explained 58% of variance. Multivariate
normal distribution of the data was determined (Shapiro-Wilk p > 0.05). PCA identified
significant correlation between stress relaxation force and rollability.
Canonical correlation analysis revealed significant correlation between kernel
and tortilla properties (p̂ = 0.75), kernel diameter and weight contributed the highest to
this correlation. Flour and tortilla properties were highly correlated (p̂ = 0.74). Glutenin
to Gliadin ratio (GGratio), IPP and peak time contributed highest to this correlation and can explain > 60% of variability in tortilla texture (force, distance and work to rupture).
The second canonical variate of flour properties is a measure of flour protein content and
can explain 26% of the variability in tortilla rollability. Dough and tortilla properties
were significantly correlated (p̂ = 0.82, 0.68, 0.54, 0.38 and 0.29). Dough stress
relaxation force after 25 seconds is negatively correlated with tortilla diameter (r = -
0.73).
Kernel hardness, diameter and weight are the best predictors of tortilla texture
after 16 days. Glutenin to gliadin ratio and IPP contributed significantly to tortilla
texture. This is the first study to identify the contribution of protein content on tortilla
rollability score. Dough extensibility can explain 37% of tortilla rollability. Stress
relaxation is the best predictor of tortilla diameter. Tortilla quality variation is attributed
to kernel, flour, and dough properties. Logistic regression and stepwise variable
selection identified an optimum model comprised of kernel hardness, GGratio, dough
extensibility and compression force as the most important variables. Cross-validation
indicated 83% prediction efficiency for the model. This emphasizes the feasibility and
practicality of the model using variables that are easily and quickly measured. This is the
first model that can be used to simultaneously predict both tortilla diameter and
rollability. It will be a useful tool for the flat bread wheat breeding programs, wheat
millers, tortilla processors and wheat marketers in the United States of America.
Subject
Wheat flour tortilla qualityWheat protein composition
Wheat Kernel properties
Wheat Milling
Tortilla formulation
Evaluation of wheat dough properties
Dough development time and tolerance
dough Stress relaxation
Dough extensibility test
Dough compression test
Evaluation of tortilla properties
Flour protein analysis
Total protein content
Polymeric and monomeric protein analysis
Wheat flour Polymeric to monomeric protein ratio
Insoluble polymeric protein content
Extractable and un-extractable protein content
High molecular weight and low molecular weight glutenin sub-units analysis
Univariate relationship between kernel
flour
dough and tortilla properties
The role of flour protein fractions in kernel and tortilla quality
Principal component analysis
Canonical correlation analysis
Discriminant analysis
tortilla industry association
Simultaneous prediction of tortilla diameter and rollability
Model for classification of wheat varieties for tortilla processing functionality
Citation
Jondiko, Tom O (2014). Prediction of Tortilla Quality Using Multivariate Modeling of Kernel, Flour and Dough Properties. Doctoral dissertation, Texas A & M University. Available electronically from https : / /hdl .handle .net /1969 .1 /152469.
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