2012
DOI: 10.1002/jctb.3825
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Optimum culture medium composition for rhamnolipid production by pseudomonas aeruginosa AT10 using a novel multi‐objective optimization method

Abstract: BACKGROUND: Rhamnolipid is a biosurfactant that finds wide applications in pharmaceuticals and beauty products. Pseudomonas aeruginosa is a producer of rhamnolipids, and the process can be implemented under laboratory‐scale conditions. Rhamnolipid concentration depends on medium composition namely, carbon source concentration, nitrogen source concentration, phosphate content and iron content. In this work, existing data7 were used to develop an artificial neural network‐based response surface model (ANN RSM) f… Show more

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Cited by 36 publications
(14 citation statements)
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“…34 Pseudomonas aeruginosa AT10 can produce 18.07 g l À1 rhamnolipid due to medium optimization. 20 These results revealed that the engineered strain P. aeruginosa PoprAB is an efficient strain for rhamnolipid production. …”
Section: Rhamnolipid Production By Wild Strain and Engineered Strainsmentioning
confidence: 95%
“…34 Pseudomonas aeruginosa AT10 can produce 18.07 g l À1 rhamnolipid due to medium optimization. 20 These results revealed that the engineered strain P. aeruginosa PoprAB is an efficient strain for rhamnolipid production. …”
Section: Rhamnolipid Production By Wild Strain and Engineered Strainsmentioning
confidence: 95%
“…input layer to the hidden layer nodes and the hidden to the output layer nodes. Numerical processing is not carried out in the input layer; it is done by the hidden and output layer nodes, and hence they are termed as active nodes [2,3].…”
Section: Artificial Neural Networkmentioning
confidence: 99%
“…Prevalent studies have been established for classification by the statisticians, but the networkbased method emerging as a new area of research is viewed to provide better efficiency for classification problems. We have used neural networks because they have numerous applications in handling large databases [22,23], which includes biochemical engineering, biomedical science and bioinformatics [2,3,21], DNA sequence analysis and biological pattern recognition [24]. There are a number of reasons to incorporate prognostic markers with ANN, which has been thought to provide an efficient diagnostic methodology; First, prognostic signature belongs to many functional classes, which suggests that different paths could lead to disease progression, hence providing better means of detection.…”
Section: Artificial Neural Networkmentioning
confidence: 99%
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“…These are surface-active metabolites produced by Pseudomonas sp. They are widely studied under glycolipid biosurfactants that possess the ability to reduce surface tension of water as the standard value reduces from 72 to 30 mN/m, and the interfacial tension of water/oil systems reduces from 43 mN/m to standards of about 1 mN/m (Satya Eswari et al 2013, 2016; Satya Eswari and Venkateswarlu 2016a, b; Prabu et al 2015). These are produced by using a number of substrates such as glucose, pyruvate, glycerol, succinate, molasses, vegetable oils, starch-rich waste from potato processing, hydrocarbons, waste fruit processing, waste crop residue and agro industrial waste etc.…”
Section: Introductionmentioning
confidence: 99%