Statistical optimization of enzymatic saccharification and ethanol fermentation using food waste

Kim, Jung Kon; Oh, Baek Rock; Shin, Hyun‐Jae; Eom, Chi-Yong; Kim, Si Wouk

doi:10.1016/j.procbio.2008.07.007

Cited by 118 publications

(59 citation statements)

References 24 publications

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“…Although optimization of enzymatic hydrolysis has been reported using the one factor-at-a-time optimization approach, statistical methods for optimization are gaining growing interest and application as they have proved to be cost and time saving. Recently, several statistical experimental design methods have been employed for optimizing food waste saccharification, particularly for simultaneous saccharification and fermentation for ethanol production [13,14]. Among the optimization methods used, central composite design using response surface methodology (RSM) is a method suitable for identifying the effects of individual variables and 99…”

Section: Introductionmentioning

confidence: 99%

A Threshold-Based Weather Model for Predicting Stripe Rust Infection in Winter Wheat

et al. 2017

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Food waste (FW) generally has high starch content and is rich in nutritional compounds, including lipids, proteins and acids. It is therefore potentially a renewable resource and its utilization for value-added product development is gaining interest. In this study, FW from a cafeteria was used as sole substrate for glucose production, and the fermentation conditions for optimum glucose yield were firstly optimized using response surface methodology. It was found that glucose yield was significantly affected by α-amylase loading, solid loading and temperature. The optimal conditions were found to be an α-amylase loading of 12.15 U/g FW, a solid loading of 22.4% and a culture temperature of 83.8°C for 90 min, which resulted in a maximum glucose yield of 217 mg/g. Secondly, in order to increase the final glucose concentration, an in situ produced fungal mash rich in glucoamylase was obtained from Aspergillus awamori which resulted in a glucose concentration of 99.1 g/L. When a fungal mash rich in cellulase obtained from Trichoderma reesei was combined with glucoamylase, a maximum of 140 g/L of glucose was obtained. This study showed that FW is a suitable substrate for saccharification with high conversion yield, indicating the potential utilization of food wastes for value-added chemicals production.

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Section: Introductionmentioning

confidence: 99%

A Threshold-Based Weather Model for Predicting Stripe Rust Infection in Winter Wheat

et al. 2017

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“…17,18 Statistically designed minimum number of the replicates executes the information of the effects of main factors and the viable interactions amid them. 19 Statistical optimization to amplify the enzyme production is persuaded by a number of process variables that are assorted in to two i) media constituents such as carbon, nitrogen minerals and salts ii) cultural parameters such as pH, temperature and incubation time. Hence the method of desirability which is a multi-response optimization path is engaged in this context.…”

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confidence: 99%

Extracellular L-Asparaginase from Streptomyces labedae VSM-6: Isolation, Production and Optimization of Culture Conditions Using RSM

Mangamuri¹,

Vijayalakshmi²,

Ganduri³

et al. 2017

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Objective: The present study was intended to isolate actinomycetes VSM-6 from deep sea sediment samples of Bay of Bengal that is potent to produce L -asparaginase. Materials and Methods: The identification of the isolate was executed by polyphasic taxonomy.Optimization was carried out using one factor at a time (O-F-A-T) for the production of the L-asparaginase. RSM was pledged to optimize the L -asparaginase production by S.labedae VSM-6. Central composite design was applied to study the influence of the variables and their interactive effects on the production of L -asparaginase. Unstructured Kinetic modelling for L -asparaginase production was adopted using Leudeking-Piret (LILP) and Logistic Incorporated Modified Leudeking-Piret (LIMLP) models. Results: Optimization using One-Factor-At-A-time registered a turnout of 8.92 IU/ml of L -asparaginase production. But results obtained from the statistical design are in agreement with the experimental results. The model followed the second order polynomial equation and the model adequacy was determined by the P value (<0.0001), Coefficient determination (R 2 ) with a value of 0.9942 and the adjusted R 2 = 0.9087 which determines that the model was significant. The experimental values are in compliance with the model anticipated values and catalogued an escalation in yield of L -asparaginase (10.17 IU/ml) by RSM. Unstructured Kinetic modelling for L -asparaginase production adopting Leudeking-Piret (LILP) and Logistic Incorporated Modified Leudeking-Piret (LIMLP) models showed L -asparaginase production of (10.17 IU/ml), closer to model anticipated value (10.23 IU/ml) so unstructured models provided a better approximation for L -asparaginase production by S.labedae VSM-6. Conclusion: From our study we have reported for the first time the production of L -asparaginase from S.labedae VSM-6 using central composite design and kinetic modelling.

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“…Response surface methodology (RSM) is generally used to investigate a combined effect of several variables and to find optimum conditions for a multivariable system [11]. That is an empirical modeling technique used to evaluate the relationship between a set of controllable experimental factors and observed results [12].…”

Section: Rsm Design For Enzyme Saccharificationmentioning

confidence: 99%

Optimization of Enzymatic Saccharification of Alkali Pretreated Typha angustifolia for Glucose Production

Sopajarn¹,

Sangwichien²

2015

IJCEA

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Abstract-Lignocellulose ethanol is significantly sustainable bio-fuel. It is an environmentally friendly. This work is to develop a hydrolysis process of pretreated lignocellulose to ethanol production from narrow leaved cattail as a biomass material. Response surface methodology (RSM) with a central composite design (CCD) was followed to optimize the enzymatic saccharification process in order to obtain high glucose yield. Three independent variables (cellulase, β-glucosidase and temperature) that operating condition, vary at CCD five levels (-α, -1, 0, +1, +α). The optimum values for the predicted variables for glucose and xylose released were: cellulase loading 17.5 FPU/g substrate, β-glucosidase loading 0 U/g substrate and hydrolysis temperature 55°C. Under these optimal conditions, glucose and xylose yield reached to 413.25 mg/ g substrate and 75.48 mg/g substrate, respectively. The results of a confirmation experiment under the optimum conditions agreed with model predictions.

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Statistical optimization of enzymatic saccharification and ethanol fermentation using food waste

Cited by 118 publications

References 24 publications

A Threshold-Based Weather Model for Predicting Stripe Rust Infection in Winter Wheat

A Threshold-Based Weather Model for Predicting Stripe Rust Infection in Winter Wheat

Extracellular L-Asparaginase from Streptomyces labedae VSM-6: Isolation, Production and Optimization of Culture Conditions Using RSM

Optimization of Enzymatic Saccharification of Alkali Pretreated Typha angustifolia for Glucose Production

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