کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
6483984 | 134 | 2016 | 35 صفحه PDF | دانلود رایگان |
عنوان انگلیسی مقاله ISI
Batch growth of Kluyveromyces lactis cells from deproteinized whey: Response surface methodology versus Artificial neural network-Genetic algorithm approach
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کلمات کلیدی
CMWMOEAANNMSEFFDRMSEWhey - آبپنیر، ماء الجبن، پودر آب ماستMultiobjective evolutionary algorithms - الگوریتم تکاملی چند منظورهGenetic algorithms - الگوریتم های ژنتیکMAE - بلهanalysis of variance - تحلیل واریانسANOVA - تحلیل واریانس Analysis of variancedegrees of freedom - درجه آزادیResponse surface methodology - روش سطح پاسخRSM - روششناسی سطح پاسخRoot mean square error - ریشه میانگین خطای مربعAmmonium sulfate - سولفات آمونیومArtificial Neural Network - شبکه عصبی مصنوعیcoefficient of determination - ضریب تعیینFull factorial design - طراحی فاکتوریل کاملYeast extract - عصاره مخمرLactose - لاکتوزSum of squares - مجموع مربعاتYeast - مخمرModelling - مدل سازیmagnesium sulfate - منیزیم سولفاتMean Absolute Error - میانگین خطا مطلقMean Square Error - میانگین مربع خطاWEKA - نه
موضوعات مرتبط
مهندسی و علوم پایه
مهندسی شیمی
بیو مهندسی (مهندسی زیستی)
پیش نمایش صفحه اول مقاله
چکیده انگلیسی
Deproteinized cheese making whey (CMW) was investigated as an alternative medium for the production of Kluyveromyces lactis as single-cell protein. Batch runs were performed according to a Full Factorial Design (FFD) on CMW supplemented with yeast extract, magnesium sulfate and ammonium sulfate in different concentrations. These independent variables were tested in duplicate at three levels, while dry biomass productivity was used as the response. The results were used to construct two models, one based on Response surface methodology (RSM) and another on Artificial neural network (ANN). Two different training methods (10-fold cross validation and training/testing) were utilized to obtain two different network architectures, while a Genetic algorithm was utilized to obtain optimal concentrations of the above medium components. A quadratic regression by RSM (R2Â =Â 0.840) was the best modeling and optimization tool under the specific conditions selected here. The highest biomass productivity (approximately 2.14Â gDW/LÂ h) was ensured by the following optimal levels: 7.04-9.99% (w/v) yeast extract, 0.430-0.503% (w/v) magnesium sulfate and 4.0% (w/v) ammonium sulfate. These results demonstrate the feasibility of using CMW as an interesting alternative to produce single-cell protein.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Biochemical Engineering Journal - Volume 109, 15 May 2016, Pages 305-311
Journal: Biochemical Engineering Journal - Volume 109, 15 May 2016, Pages 305-311
نویسندگان
Fábio Coelho Sampaio, Tamara Lorena da Conceição Saraiva, Gabriel Dumont de Lima e Silva, JanaÃna Teles de Faria, Cristiano Grijó Pitangui, Bahar Aliakbarian, Patrizia Perego, Attilio Converti,