Browsing by Author "Yağanoğlu A.M."

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    Chaid and logistic regression approaches for assessing the effects of non-genetic factors on lamb mortality
    (2017-02-01) Topal M.; Emsen E.; Yağanoğlu A.M.
    Lamb output from the ewe flock is a key determinant of the profitability of sheep farming. Here, we assessed the association between various factors (ewe breed, month of birth, year of birth, birth type, lamb sex and lamb birth weight) on lamb mortality (within the first 60 days of life) using data collected in northern Turkey between 2006 and 2014. The study included a total of 1958 lambs, including the Romanov (R), Awassi (I), Kivircik (K), Tuj (T), Anarom (AN), R ×I (Romanov×Awassi), R×K (Romanov×Kivircik), R×A (Romanov×Akkaraman), R×M (Romanov×Morkaraman) and F1 Romanov (Romanov× Turkish native) breeds. CHAID (Chi -Square Automatic Interaction Detector) analysis correctly classified 99.2% of surviving lambs and 12.4% of dying lambs, while 100% of surviving lambs and no dying lambs were correctly classified by logistic regression analysis. CHAID and logistic regression analyses correctly determined 91.5% and 91.1% of lamb mortality, respectively. The most important variables for the estimation of lamb mortality in the CHAID and logistic regression models were month of birth and lamb breed. Based on our findings, we propose that the CHAID algorithm (AUC of 0.843) is better to classify lamb mortality than a logistic regression analysis approach (AUC of 0.613).
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    Comparison of quality characteristics in honey using grey relational analysis and principal component analysis methods
    (2018-02-01) Topal M.; Yağanoğlu A.M.
    Composition characteristics are taken into account to determine authenticity and quality of honey. This study used grey relational analysis and principal component analysis methods to identify the most important variables affecting quality of honey.Honey specimens from 20 different producers were obtained to determine quality characteristics. C4 % (0.787), glucose (0.753), moisture (0.731), F+G (0.712), fructose (0.685), acidity (0.605), brix (0.581), conductivity (0.580), δ13C honey (0.576), proline (0.571), pH (0.530), δ13Cprotein -honey (0.527), δ13C protein (0.516), Fructose -glucose ratio (0.507) and diastase number (0.490) were found to be the most important variables on quality of honey based on the mean values of grey relational coefficients of quality characteristics. Grey relational grade (GRG) calculated by using eight values obtained from principal component analysis of quality parameters showed that S6 (0.690) was the honey with the highest quality, while S16 (0.501) was the honey with the lowest quality.