Food Engineering / Gıda Mühendisliği

Permanent URI for this collectionhttps://hdl.handle.net/11147/12

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  • Article
    Citation - WoS: 33
    Citation - Scopus: 34
    Exploitation of Agricultural Wastes and By-Products for Production of Aureobasidium Pullulans Y-2311 Xylanase: Screening, Bioprocess Optimization and Scale Up
    (Springer Verlag, 2017) Yeğin, Sırma; Büyükkileci, Ali Oğuz; Sargın, Sayıt; Göksungur, Yekta
    The potential of several agricultural wastes and by-products (wheat bran, oat bran, corn cob, brewer’s spent grain, malt sprout, artichoke stem, sugar beet pulp, olive seed, cotton stalk and hazelnut skin) was examined as the substrate for xylanase production by Aureobasidium pullulans Y-2311-1. Based on the screening studies, wheat bran was selected as the best substrate for further optimization studies. The effects of initial medium pH, temperature and incubation time on xylanase production in shake flask system were optimized by response surface methodology (RSM). The optimum levels of the process variables defined by the model (initial medium pH, 4.24; temperature, 30.27 °C; and incubation time 126.67 h) resulted in production of 85.19 U/ml xylanase. Taking the RSM optimized parameters in shake-flask scale into consideration; xylanase production was scaled up to bioreactor system with a working volume of 1.5 l. The peak of enzyme production was achieved after 126 h incubation that has previously been determined by RSM studies at shake flask level. Furthermore, the optimum levels of agitation and aeration in bioreactor system was found as 200 rpm and 1.5 vvm. Maximum enzyme production was close to 85 kU/l which could be translated into a productivity of 0.68 kU/l/h. No previous work considered the statistical optimization of xylanase production by A. pullulans on wheat bran and scale up of the bioprocess to a bioreactor system
  • Article
    Citation - WoS: 1
    Citation - Scopus: 2
    Process Neural Network Method: Case Study I: Discrimination of Sweet Red Peppers Prepared by Different Methods
    (Springer Verlag, 2011) Ünlütürk, Sevcan; Ünlütürk, Mehmet S.; Pazır, Fikret; Kuşçu, Alper
    This study utilized a feed-forward neural network model along with computer vision techniques to discriminate sweet red pepper products prepared by different methods such as freezing and pureeing. The differences among the fresh, frozen and pureed samples are investigated by studying their bio-crystallogram images. The dissimilarity in visually analyzed bio-crystallogram images are defined as the distribution of crystals on the circular glass underlay and the thin or the thick structure of crystal needles. However, the visual description and definition of bio-crystallogram images has major disadvantages. A methodology called process neural network (ProcNN) has been studied to overcome these shortcomings.