Gıyasettin ÖZCAN, Necati KARAKUŞ, Serel Özmen AKYOL, Aysel GÜLBANDILAR, Eyyüp GÜLBANDILAR

Çok Katmanlı Perceptron Yapay Sinir Ağı Kullanılarak Maya Hücrelerinin Yaşam Döngüsü Parametrelerinin Araştırılması

Gıda endüstrisinde maya hücrelerinin büyüme parametrelerinin incelenmesi hem zaman hem de işçilik maliyetlerinde artışa neden olmaktadır. Bu maliyetleri azaltmak için simülasyon modelleri öne sürülebilir. Bu çalışmada Çok Katmanlı Perceptron Sinir Ağı (MLPNN) kullanarak Saccharomyces cerevisiae'nin büyüme döngüsü parametreleri için bir simülasyon modeli tasarlanması amaçlanmıştır. Bu modelde, ekim zamanı girdi parametresi olarak tanımlanmışken, saatteki büyüme oranı ve hücre sayısı çıktı parametreleri olarak belirlenmiştir. Tasarlanan modelde iki gizli katmanlı geri yayılımlı sinir ağı tercih edilmiştir. İlk gizli katmanda 10 düğümün kullanılırken ikinci gizli katmanı 2 düğüm kullanılmıştır. Modelin eğitimi için 144 deneysel veri kullanılırken, bu deneysel verilerin 72'si eğitilmiş modeli test etmek için kullanılmıştır. Geliştirilen model, büyüme eğrisi ve büyüme hızı için hem eğitim hem de test aşamasında yüksek bir korelasyon göstermiştir (büyüme eğrisi için, R2 training = 0,9993 ve R2 test = 0,9993; büyüme oranı için R2 training = 0.9381 ve R2 test = 0.9404). Sonuçlar, geliştirilen modelimizin gıda endüstrisinde deneysel çalışmaların yerine hücre kültürü çalışmalarında başarılı bir şekilde kullanılabileceğini göstermektedir.

Investigation of Yeast Cells Life Cycle Parameters by Using Multi-Layer Perceptron Artificial Neural Network

Examining the growth parameters of yeast cells in the food industry causes to increase both time andlabor costs. Simulation models can be put forward to reduce these costs. In this study aimed that designa simulation model for growth cycle parameters of Saccharomyces cerevisiae by using the Multi-LayerPerceptron Neural Network (MLPNN). While cultivation time is defined as input parameter in thismodel, the cell count per hour and growth rate is determined as output parameters. In the designedmodel, two hidden layer back propagation neural networks are preferred. The first hidden layer uses10 nodes, while the second hidden layer uses 2 nodes. For the training of this model, 144 experimentaldata are used, whereas 72 of these experimental data were used for testing the trained model. Thedeveloped model showed a high correlation on the growth curve and growth rate in the process bothtraining and test (R2training=0.9993 and R2test=0.9993 for growth curve; R2training=0.9381 and R2test=0.9404for growth rate). The results show that developed model can be used successfully in cell culture studiesinstead of experimental studies in food industry.

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