HANDE KÜÇÜKÖNDER, TUGAY AYAŞAN, Hatice HIZLI

Classification of Holstein Dairy Cattles in Terms of Parameters Some Milk Component Belongs by Using The Fuzzy Cluster Analysis

Bu çalışma, Siyah Alaca ırkı süt sığırlarının bazı süt bileşenleri bakımından bulanık kümeleme analizi ile sınıflandırılması ve oluşan küme yapılarında ilgili parametrelerin incelenmesi üzerine yürütülmüştür. Araştırma kapsamında somatik hücre sayısı (SHS), süt yağı (%), süt proteini (%), süt laktoz (%), kazein (%), üre (%), kuru madde (%), yağsız kuru madde (%), yoğunluk (g/cm3 ), asitlik (ºSH), serbest yağ asidi (mmol/10L), sitrik asidi (%) ve donma noktası (ºC) olmak üzere on üç farklı ölçüt kullanılmıştır. Bulanık kümeleme analizinde bulanık eşitlik ilkesine dayalı Fanny algoritması kullanılarak yapılan analiz sonucunda ise toplam 282 adet inek %97.5 doğru sınıflandırma oranı ile 2 ayrı kümeye ayrıldığında bulanıklık düzeyinin minumum olduğu görülmüştür. Buna göre inekler incelenen özellikler bakımından 25 tanesi küme 1de, 257 tanesi de küme 2de yer alacak şekilde 2 farklı kümede sınıflandırılmıştır. Oluşan küme yapıları incelendiğinde ise küme 2nin küme 1e göre daha kararlı bir küme oluşturduğu tespit edilmiştir. Kümelere göre süt bileşenlerinin değişimi değerlendirildiğinde ise SHS, süt yağı, kuru madde (%), süt yağı (%) ve süt yoğunluğunun (g/cm3 ) kümeler arası önemli bir (P0.05) sonucuna varıldı.

Bulanık Kümeleme Analizi İle Siyah Alaca Süt Sığırlarının Bazı Süt Bileşenlerine Ait Parametreler Bakımından Sınıflandırılması

This study was carried out on classification of Holstein Friesian breed dairy cattles in terms of some milk component parameters and on investigating the relevant parameters in the resulting cluster structures. Within the scope of this study, thirteen different criteria were used including somatic cell count (SCC), milk fat (%), milk protein (%), milk lactose (%), casein (%), urea (%), dry matter (%), non-fat dry matter (%), density (g/cm3 ), acidity (ºSH), free fatty acids (mmol/10L), citric acid (%) and freezing point (ºC). As a result of the analysis using Fanny algorithm based on the principle of fuzzy equality, the fuzziness level was found to be minimum when a total of 282 cattles were divided into 2 clusters with the accuracy rate of 97.5%. Accordingly, the cattles were classified in terms of the investigated characteristics in 2 different clusters in which 25 cattles were in Cluster 1 and the rest of the cattles were in Cluster 2. When the resulting cluster structures were studied, it was found that Cluster 2 has a more stable clustering than Cluster 1. When evaluating the change in milk components according to the clusters, it was concluded that somatic cell count, dry matter (%), milk fat (%) and density (g/cm3 ) have significant differences between clusters (P<0.05), while the other parameters were found statistically non-significant (P>0.05).

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