Jondiko, Tom O (2014-05). Prediction of Tortilla Quality Using Multivariate Modeling of Kernel, Flour and Dough Properties. Doctoral Dissertation. Thesis uri icon

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 (p? = 0.75), kernel diameter and weight contributed the highest to this correlation. Flour and tortilla properties were highly correlated (p? = 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 (p? = 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.
  • 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 (p? = 0.75), kernel diameter and weight contributed the highest to
    this correlation. Flour and tortilla properties were highly correlated (p? = 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 (p? = 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.

publication date

  • May 2014