Comparison of performance of different algorithms in noisy signals filtering of process in enzymatic hydrolysis of cheese whey

Pinto, Gilson A.; Sousa, Ruy; Giordano, Roberto de Campos

doi:10.1590/s1516-89132005000400019

Cited by 7 publications

(4 citation statements)

References 4 publications

(4 reference statements)

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“…Within the field of chemical processes, there is increasingly more literature describing the use of artificial neural networks for a diverse range of engineering applications, such as fault detection and signal processing, in addition to process modeling and control (Himmelblau, 2000). In a study by Pinto et al (2005) on the partial enzyme hydrolysis of cheese whey proteins, an off-line smoothing algorithm, based on penalized least squares, was implemented in the monitoring system. For filtering on-line signals, various algorithms were compared: artificial neural networks, a moving average and a smoothing algorithm.…”

Section: Introductionmentioning

confidence: 99%

Neural modeling of bromelain extraction by reversed micelles

Fileti

Fischer

Tambourgi

2010

Braz. arch. biol. technol.

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A pulsed-cap microcolumn was used for bromelain extraction from pineapple juice by reversed micelles. The cationic micellar solution used BDBAC as the surfactant, isooctane as the solvent and hexanol as the co-solvent. In order to capture the dynamic behavior and the nonlinearities of the column, the operating conditions were modified in accordance with the central composite design for the experiment, using the ratio between the light phase flow rate and the total flow rate, and the time interval between pulses. The effects on the purification factor and on total protein yield were modeled via neural networks. The best topology was defined as 16-9-2, and the input layer was a moving window of the independent variables. The neural model successfully predicted both the purification factor and the total protein yield from historical data. At the optimal operating point, a purification factor of 4.96 and a productivity of 1.29 mL/min were obtained.

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Section: Introductionmentioning

confidence: 99%

Neural modeling of bromelain extraction by reversed micelles

Fileti

Fischer

Tambourgi

2010

Braz. arch. biol. technol.

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“…On‐line measurements contain errors caused by exhausting use of instruments, human errors or unmeasured disturbances. Random noise was reduced by a filter based on NNs54 before training the NNs. This filter is based on the concept of ‘microfeature’, suggested by Baughman and Liu 55.…”

Section: Methodsmentioning

confidence: 99%

Artificial neural networks to infer biomass and product concentration during the production of penicillin G acylase from Bacillus megaterium

Silva

Pinotti

Cruz

et al. 2008

J of Chemical Tech & Biotech

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“…Experimental curves of pseudocomponents concentration vs. time were previously smoothed [29]. Reaction rates were obtained after the direct differentiation of these curves using the proper "calculus tool" of the software Microcal Origin (Microcal Software, Northampton, MA, USA).…”

Section: Methodsmentioning

confidence: 99%

Neural Network Inference of Molar Mass Distributions of Peptides during Tailor-Made Enzymatic Hydrolysis of Cheese Whey: Effects of pH and Temperature

Pinto

Giordano

2007

Appl Biochem Biotechnol

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The fine-tuning of the enzymatic hydrolysis of proteins may provide a pool of peptides with predefined molar mass distributions. However, the complex mixture of molecules (peptides and amino acids) that results after the proteolysis of cheese whey turns unfeasible the assessment of individual species. In this work, a hybrid kinetic model for the proteolysis of whey by alcalase, multipoint-immobilized on agarose, is presented, which takes into account the influence of pH (8.0-10.4) and temperature (40-55 degrees C) on the activity of the enzyme. Five ranges of peptides' molar mass have their reaction rates predicted by neural networks (NNs). The output of NNs trained for constant pH and temperatures was interpolated, instead of including these variables in the input vector of a larger NN. Thus, the model complexity was reduced. Coupled to differential mass balances, this hybrid model can be employed for the online inference of peptides' molar mass distributions. Experimental kinetic assays were carried out using a pH-stat, in a laboratory-scale (0.03 L) batch reactor. The neural-kinetic model was integrated to a supervisory system of a bench-scale continually stirred tank reactor (0.5 L), providing accurate predictions during validation tests.

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Comparison of performance of different algorithms in noisy signals filtering of process in enzymatic hydrolysis of cheese whey

Cited by 7 publications

References 4 publications

Neural modeling of bromelain extraction by reversed micelles

Neural modeling of bromelain extraction by reversed micelles

Artificial neural networks to infer biomass and product concentration during the production of penicillin G acylase from Bacillus megaterium

Neural Network Inference of Molar Mass Distributions of Peptides during Tailor-Made Enzymatic Hydrolysis of Cheese Whey: Effects of pH and Temperature

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