The use of factorial design for ferulic acid production by co-culture

Samad, Kamaliah Abdul; Zainol, Norazwina

doi:10.1016/j.indcrop.2016.10.028

Cited by 16 publications

(11 citation statements)

References 17 publications

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“…The third factor was the external resistance which are 200 Ω and 1000 Ω. By using all these factors, its two-levels factorial was designed accordingly, similar pattern was reported by Kala et al (2016), Samad and Zainol (2017), and Tan et al (2016). These factors were to investigate in terms of its main and interaction effects and to determine the optimisation of MFC's bio-electrochemical process using the MFC device.…”

Section: Introductionmentioning

confidence: 93%

Palm Oil Mill Effluent’s Microbial Fuel Cell’s Optimisation Procedure by Using Two-Level Factorial Design Method and Chemical Oxygen Demand Treatment

Alkhair

Hassan

Aminah

et al. 2018

JOPR

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Microbial fuel cell (MFC) technologies represent the newest approach for generating electricity (bioelectricity generation) from biomass using bacteria. Bio-electricity generations by MFC have gained considerable attention due to its integration with wastewater treatment. The objectives of the work are to determine the optimisation of MFC's bio-electrochemical process using three different factors and its interaction, and to determine the optimal pH value for acidogenic, acetogenic and methanogenic by natural mixed culture electroactive bacteria (exoelectrogens) growth in presence and absence of oxygen using MFC. The two-level factorial design is used in order to achieve the main two objectives. The current generation, power generation and maximum power have been monitored. Experimental result shows that the best interaction between these three factors is (-+-) interaction which is the interaction between tryptic soya broth (TSB) , sodium hydroxide as pH controller and resistant of 200 Ω, and the interaction yield the power density of 57.44 mA m-2. The effects between those interactions also have been analysed. The interaction of all parameters that have been used in this experiment is given out the highest significant effect which is a value of effect of 24.56 with a significant F-value of 29.51. The chemical oxygen demand (COD) reduction by MFC treatment data based on the COD effective deduction concept shows that DMP produced lower percentage of COD effective deduction efficiency compared to nDMP. nDMP was 342% to 441% more efficient to deduct COD compared to DMP. nDMP 6.8 recorded the most effective COD deduction by MFC devices at 29.17%.

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Section: Introductionmentioning

confidence: 93%

Palm Oil Mill Effluent’s Microbial Fuel Cell’s Optimisation Procedure by Using Two-Level Factorial Design Method and Chemical Oxygen Demand Treatment

Alkhair

Hassan

Aminah

et al. 2018

JOPR

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“…The bottles were closed and incubated at selected temperature in the dark condition. The experiment set-up in Table 2 was performed by Design Expert software where all the factors were randomized [6]. Then, experimental data were analyzed by using Design Expert software in order to determine the most contributing factors.…”

Section: Experimental Set-up For Factorial Analysismentioning

confidence: 99%

Application of Artificial Neural Network to Improve Pleurotus sp. Cultivation Modelling

2019

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Mathematical modelling for nitrogen concentration in mycelium (N) during Pleurotus sp. cultivation had successfully been produced using multiple linear regression. Two different substrates were used to cultivate the Pleurotus sp. which were empty palm fruit bunch (EFB) and sugarcane bagasse (SB). Both substrates were collected and prepared as the selected factors which were type of substrate (SB - A and EFB - B), size of substrates (0.5 cm and 2.5 cm), mass ratio of spawn to substrate (SP/SS) (1:10 and 1:14), temperature during spawn running (25°C and ambient) and pre-treatment of substrates (steam and non-steam). The response was nitrogen concentration in mycelium (N). This paper presents the application of artificial neural network to improve the modelling process. Artificial neural network is one of the machine learning method which use the cultivation process information and extract the pattern from the data. Neural network ability to learn pattern by changing the connection weight had produced a trained network which represent the Pleurotus sp. cultivation process. Next this trained network was validated using error measurement to determine the modelling accuracy. The results show that the artificial neural network modelling produced better results with higher accuracy and lower error when compared to the mathematical modelling.

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“…FAE can be found naturally in plants with the presence of another microorganism. Both fungi and bacteria has been reported to be able to release FAE during FA production [21,22]. Among the fungi kingdom, the Aspergillus spp.…”

Section: Introductionmentioning

confidence: 99%

“…As for bacteria, the Bacillus spp. has shown the ability to produce FAE [22,24,25]. Since both of these microorganisms can be found in soil culture, it is safe to deduce that soil culture has the ability to produce FAE.…”

Section: Introductionmentioning

confidence: 99%

Application of Michaelis–Menten in the kinetics of oil palm frond enzymatic hydrolysis for ferulic acid production

et al. 2020

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Ferulic acid (FA) production has become a frequent focus on today's research due to its antioxidant properties. However, there has been little to none studies reported on the usage of mixed culture as inoculum in FA production. This study aims to determine the kinetics FA production in a mixed culture fermentation by applying the Michaelis-Menten kinetic model. In this study, mixed culture fermentation process using oil palm frond bagasse (OPFB) as substrate was applied for FA production improvement. Optimum condition was applied to study ferulic acid esterase (FAE) mechanism for kinetic modelling purposes. The kinetic model used was based on the Michaelis-Menten kinetic model. Runge-Kutta Fourth Order method was used to solve the kinetic model. Maximum FAE activity was achieved at the 28 h of fermentation process at 3.7 × 10 −3 mU mL −1. This result proved that enzymatic hydrolysis occurred during fermentation process. Kinetic study was conducted with Michaelis-Menten kinetic model used as a reference kinetic equation. Three kinetic constants, V max , K m and K s were determined as 3.725 × 10 −3 g L −1 h −1 , 28.231 g L −1 and 1.33 × 10 −2 h −1 respectively using Runge-Kutta Fourth Order approach. The outcome of this study confirms that the kinetics of the process fit well with the Michaelis-Menten model. This also suggests that the usage of mixed culture has the potential to induce enzymatic hydrolysis hence improving FA production from OPFB during fermentation process.

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The use of factorial design for ferulic acid production by co-culture

Cited by 16 publications

References 17 publications

Palm Oil Mill Effluent’s Microbial Fuel Cell’s Optimisation Procedure by Using Two-Level Factorial Design Method and Chemical Oxygen Demand Treatment

Palm Oil Mill Effluent’s Microbial Fuel Cell’s Optimisation Procedure by Using Two-Level Factorial Design Method and Chemical Oxygen Demand Treatment

Application of Artificial Neural Network to Improve Pleurotus sp. Cultivation Modelling

Application of Michaelis–Menten in the kinetics of oil palm frond enzymatic hydrolysis for ferulic acid production

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