COMPARISON OF ARTIFICIAL NEURAL NETWORK (ANN) AND RESPONSE SURFACE METHODOLOGY (RSM) IN OPTIMIZATION OF THE IMMOBILIZATION CONDITIONS FOR LIPASE FROM<i>Candida rugosa</i>ON AMBERJET® 4200-Cl

Benamia, Fatiha; Sameh, Bouchagra; Saihi, Youcef; Djeghaba, Zeineddine; Rebbani, Nacer

doi:10.1080/10826068.2012.693899

Cited by 18 publications

(19 citation statements)

References 27 publications

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“…There was no shift observed at the optimum pH for the CLEA lipase formed (pH 8.0), in comparison with that of free lipase despite any conformational changes possibly occurring during immobilization (Mateo et al, ). It has been reported that the free Cal B lipase exhibits maximum activity under basic conditions (pH 7.0–8.0) due to the stability maintained by the structure and maintenance of the open, active structural configuration under these conditions (Fatiha et al, ). No difference in the activity between free and CLEA lipase was observed at pH 8.0.…”

Section: Resultsmentioning

confidence: 99%

“…CLEA lipase exhibited a lower stability compared to free enzyme at pH 6.0, with 80% of the initial activity maintained after prolonged exposure of up to 240 min. This may be attributed to the conformational changes that could be occurring during the CLEA formation process, making the CLEA unstable under slightly acidic conditions of pH 6.0, despite maintaining high activity under basic conditions (Fatiha et al, ; Gupta et al, ) (Fig. b).…”

Section: Resultsmentioning

confidence: 99%

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Systemic Concocting of Cross‐Linked Enzyme Aggregates of Candida antarctica Lipase B (Novozyme 435) for the Biomanufacturing of Rhamnolipids

Rathankumar

SaiLavanyaa

Saikia

et al. 2019

J Surfact & Detergents

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In the present study, Candida antarctica lipase B was immobilized on amine‐functionalized silica microspheres as cross‐linked enzyme aggregates (CLEA) and utilized for the biomanufacturing of rhamnolipids (RL). Lipase CLEA synthesized under optimized conditions of 2.0:1.0 by volume of silica microsphere/enzyme concentration, a 1.0:2.5 (v/v) ratio of enzyme/2‐propanol, 7 mM glutaraldehyde concentration, when incubated at pH 9.0 and 40 °C, for a cross‐linking time of 30 min were observed to exhibit superior biocatalytic properties and a maximum enzyme load of 770 U g−1. Lipase CLEA exhibited enhanced pH stability in acidic and alkaline media and increased temperature resistance as compared to free lipase. Both free and CLEA lipases were used to synthesize RL in different solvent systems. After 12 h, from initiation of the esterification, the degree of esterification (molar conversion yield) reached 46% and 71% in the batch mode. 1H and 13C nuclear magnetic resonance (NMR) and high‐performance liquid chromatographic (HPLC) analysis confirm RL production by CLEA lipase. The CLEA showed greater confrontation to enzyme‐mediated bioprocess approach as compared to its soluble counterpart and exhibited excellent RL production and catalytic activity even after its tenth successive reuse.

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Systemic Concocting of Cross‐Linked Enzyme Aggregates of Candida antarctica Lipase B (Novozyme 435) for the Biomanufacturing of Rhamnolipids

Rathankumar

SaiLavanyaa

Saikia

et al. 2019

J Surfact & Detergents

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“…Comparison of the MLR and ANN model shows that the effect of some of the descriptors to activity may be non-linear [6]. Fatiha et al (2013) have been investigated optimal conditions for immobilization, through RSM and ANN. They have concluded that both models provided good quality predictions [7].…”

Section: Introductionmentioning

confidence: 99%

“…Fatiha et al (2013) have been investigated optimal conditions for immobilization, through RSM and ANN. They have concluded that both models provided good quality predictions [7]. Vasundara et al (2014) carried out research to predict the maximum elastic deformation of the workpiece during machining for the fixture layout.…”

Section: Introductionmentioning

confidence: 99%

RSM and MLR Model for Equivalent Stress Prediction of Eicher 11.10 Chassis Frame: A Comparative Study

Patel

Bhatt

2016

Proceedings of First International Conference on Information and Communication Technology for Intelligent Systems: Volume 2

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The main objective of the study is to compare the prediction accuracy of Response Surface Methodology (RSM) and Multiple Linear Regressions (MLR) model for the Equivalent stress of the chassis frame. The chassis frame is made of two sidebars connected with a series of crossbar. The web thickness, upper flange thickness and lower flange thickness of sidebar becomes the design variables for the optimization. Since the number of parameters and levels are more, so the probable models are too many. The variants of the frame are achieved by topology modification using the orthogonal array. Then Finite Element Analysis (FEA) is performed on those models. RSM model and MLR model are prepared using the results of FEA to predict equivalent stress on the chassis frame. The results indicate that predictions of RSM model are more accurate than predictions of MLR model.

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“…However, this method is limited in most cases by the use of polynomial equations. Instead, ANNs can be used to ensure better data fit and estimation capabilities (Ba & Boyaci, 2007; Fatiha et al 2013).…”

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confidence: 99%

Artificial neuronal networks (ANN) to model the hydrolysis of goat milk protein by subtilisin and trypsin

Espejo-Carpio

Pérez‐Gálvez

Guadix

et al. 2018

Journal of Dairy Research

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The enzymatic hydrolysis of milk proteins yield final products with improved properties and reduced allergenicity. The degree of hydrolysis (DH) influences both technological (e.g., solubility, water binding capacity) and biological (e.g., angiotensin-converting enzyme (ACE) inhibition, antioxidation) properties of the resulting hydrolysate. Phenomenological models are unable to reproduce the complexity of enzymatic reactions in dairy systems. However, empirical approaches offer high predictability and can be easily transposed to different substrates and enzymes. In this work, the DH of goat milk protein by subtilisin and trypsin was modelled by feedforward artificial neural networks (ANN). To this end, we produced a set of protein hydrolysates, employing various reaction temperatures and enzyme/substrate ratios, based on an experimental design. The time evolution of the DH was monitored and processed to generate the ANN models. Extensive hydrolysis is desirable because a high DH enhances some bioactivities in the final hydrolysate, such as antioxidant or antihypertensive. The optimization of both ANN models led to a maximal DH of 23·47% at 56·4 °C and enzyme-substrate ratio of 5% for subtilisin, while hydrolysis with trypsin reached a maximum of 21·3% at 35 °C and an enzyme-substrate ratio of 4%.

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COMPARISON OF ARTIFICIAL NEURAL NETWORK (ANN) AND RESPONSE SURFACE METHODOLOGY (RSM) IN OPTIMIZATION OF THE IMMOBILIZATION CONDITIONS FOR LIPASE FROMCandida rugosaON AMBERJET® 4200-Cl

Cited by 18 publications

References 27 publications

Systemic Concocting of Cross‐Linked Enzyme Aggregates of Candida antarctica Lipase B (Novozyme 435) for the Biomanufacturing of Rhamnolipids

Systemic Concocting of Cross‐Linked Enzyme Aggregates of Candida antarctica Lipase B (Novozyme 435) for the Biomanufacturing of Rhamnolipids

RSM and MLR Model for Equivalent Stress Prediction of Eicher 11.10 Chassis Frame: A Comparative Study

Artificial neuronal networks (ANN) to model the hydrolysis of goat milk protein by subtilisin and trypsin

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