USE OF MULTIVARIATE ADAPTIVE REGRESSION
SPLINES (MARS) FOR PREDICTING PARAMETERS OF BREAST MEAT IN QUAILS
T. Şengül*1, Ş. Çelik1 and Ö.
Şengül2
1Bingöl University,
Faculty of Agriculture, Department of Anim. Sci. Bingöl, 12000, Turkey
2Uludağ University, Faculty of
Agriculture, Department of Anim. Sci. Bursa, 16000, Turkey
*Corresponding Author E-mail:
tsengul2001@yahoo.com
ABSTRACT
The
aim of this study was to determine the effects of variety and sex on the color of
the breast meat (brightness: L*, red color: a*, yellow color:
b*) in quails. In this study, a total of 144 quails from three different
varieties (Wild-type, Dark Brown and Golden) were employed. The color and pH parameters
of the breast meat were measured in quails slaughtered in week 10. In order to predict
the brightness (L*), red color (a*), and yellow color (b*)
values of the breast meat, Multivariate Adaptive Regression Splines (MARS) models
were implemented. When determining the best model, attention was paid to minimize
the Generalized Cross Validation (GCV), Root Mean Square Error (RMSE), and Mean
Absolute Deviation (MAD) statistics and to maximize coefficient of determination
(R2) and adjusted R2 values. In the MARS models constructed
to predict L*, a* and b*, it was found that R2 values were 0.999, 0.999, and 0.999; adjusted R2 values were 0.997, 0.992,
and 0.996; and RMSE values were 0.068, 0.082, and 0.038, respectively. As a
result, it could be suggested that MARS modeling may be a useful tool for the
prediction of the color parameters of the breast meat.
Keywords: Quail,
breast meat, meat color, MARS model.
https://doi.org/10.36899/JAPS.2020.4.0092
Published
online April 25, 2020 |