The effect of agitation speed, enzyme loading and substrate concentration on enzymatic hydrolysis of cellulose from brewer’s spent grain

Mussatto, Solange I.; Dragone, Giuliano; Fernandes, Marcelo; Milagres, Adriane M. F.; Roberto, Inês Conceição

doi:10.1007/s10570-008-9215-7

Cited by 91 publications

(62 citation statements)

References 36 publications

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“…It has been reported by some researchers that enzyme concentration had a more significant impact than substrate concentration on the release of sugars, which was in agreement with results obtained in this study (Mussatto et al 2008;Rosgaard et al 2007;Vasquez et al 2007). …”

Section: Effect Of Enzyme Loadingsupporting

confidence: 93%

“…Shorter hydrolysis times were required for low substrate loadings as compared to longer hydrolysis times for high substrate loading, which was in agreement to reported work on CMC and corn stover using cellulase (Al-Zuhair 2008; Lu et al 2010). This may be due to high initial viscosity at high substrate loadings which poses mass transfer limitations, resulting in a low conversion efficiency (Ioelovich and Morag 2012;Lu et al 2010;Mussatto et al 2008;Sarkar and Etters 2004). End-product inhibition on cellulases by cellobiose was avoided by supplementing b-glucosidase using Novozyme 188 (Teeri 1997).…”

Section: Effect Of Substrate Loadingmentioning

confidence: 99%

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Using an artificial neural network to predict the optimal conditions for enzymatic hydrolysis of apple pomace

et al. 2017

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The enzymatic degradation of lignocellulosic biomass such as apple pomace is a complex process influenced by a number of hydrolysis conditions. Predicting optimal conditions, including enzyme and substrate concentration, temperature and pH can improve conversion efficiency. In this study, the production of sugar monomers from apple pomace using commercial enzyme preparations, Celluclast 1.5L, Viscozyme L and Novozyme 188 was investigated. A limited number of experiments were carried out and then analysed using an artificial neural network (ANN) to model the enzymatic hydrolysis process. The ANN was used to simulate the enzymatic hydrolysis process for a range of input variables and the optimal conditions were successfully selected as was indicated by the R 2 value of 0.99 and a small MSE value. The inputs for the ANN were substrate loading, enzyme loading, temperature, initial pH and a combination of these parameters, while release profiles of glucose and reducing sugars were the outputs. Enzyme loadings of 0.5 and 0.2 mg/g substrate and a substrate loading of 30% were optimal for glucose and reducing sugar release from apple pomace, respectively, resulting in concentrations of 6.5 g/L glucose and 28.9 g/L reducing sugars. Apple pomace hydrolysis can be successfully carried out based on the predicted optimal conditions from the ANN.

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Section: Effect Of Enzyme Loadingsupporting

confidence: 93%

Section: Effect Of Substrate Loadingmentioning

confidence: 99%

Using an artificial neural network to predict the optimal conditions for enzymatic hydrolysis of apple pomace

et al. 2017

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“…In fact, the shear deactivation effect is a deterring factor in applying a more intensive agitation. However, a more properly designed and more intensive mixing regime should facilitate a better mass transfer inside the reactor, reducing a potentially high local product concentration surrounding the enzyme and active sites [15]. …”

Section: Effect Of Agitation Ratementioning

confidence: 99%

Preliminary Study on Factors that Enhanced the Production of Uricase by Aspergillus Flavus

Hatijah¹,

Ruhayu²

2013

IJBBB

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Abstract-Urate oxidase or uricase is an enzyme that catalyses the oxidation of uric acid to allantoin and plays important rule in purine metabolism. The first important application discovered for uricase was in clinical biochemistry as diagnosis reagent for measurement of uric acid in blood. The precipitation of uric acid can leading to gout symptom. The main purpose of this research is to optimize the culture condition for maximum uricase production by Aspergillus flavus. The parameters studied were pH (ranged pH4 to pH8), the sucrose concentration (10g/l to 50 g/l) and the agitation rate (100 to 300 rpm). The Aspergillus flavus were inoculated in yeast extract with sucrose and incubated for 24 hours at 200 rpm and 30 o C. The maximum enzyme activity obtained from the experiment is 0.03974 U/ml at pH 6, 200 rpm and 30 g/l sucrose concentration.

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“…Hemicellulose and lignin have been demonstrated to block enzymatic hydrolysis of cellulose. The lower in the proportions of hemicellulose and lignin in the biomass, the higher the value of enzymatic hydrolysis efficiency of cellulose (Mussatto et al, 2008;Erdei et al, 2010). Although there was no conspicuous difference in hemicellulose and lignin content of the native substrates (Table 1), pretreatments resulted in substantial degradation and removal of hemicellulose and lignin as evidenced by the HPLC and FTIR analyses (Figures 2 and 4).…”

Section: Soluble Fraction Analysis: Monosaccharides and Inhibitorsmentioning

confidence: 93%

Evaluation and Selection of Potential Biomass Sources of North-East India towards Sustainable Bioethanol Production

et al. 2017

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Vegetation biomass production in North-East India within Indo-Burma biodiversity hotspot is luxuriant and available from April to October to consider their potential for bioethanol production. Potential of six lignocellulosic biomass (LCB) sources; namely, sugarcane bagasse (BG), cassava aerial parts (CS), ficus fruits (Ficus cunia) (FF), "phumdi" (floating biomass), rice straw (RS), and sawdust were investigated for bioethanol production using standard techniques. Morphological and chemical changes were evaluated by Scanning electron microscopy and Fourier transform infrared spectroscopy and quantity of sugars and inhibitors in LCB were determined by High performance liquid chromatography. Hydrothermally treated BG, CS, and FF released 954.54, 1,354.33, and 1,347.94 mg/L glucose and 779.31, 612.27, and 1,570.11 mg/L of xylose, respectively. Inhibitors produced due to effect of hydrothermal pretreatment ranged from 42.8 to 145.78 mg/L acetic acid, below detection level (BDL) to 17.7 µg/L 5-hydroxymethylfurfural, and BDL to 56.78 µg/L furfural. The saccharification efficiency of hydrothermally treated LCB (1.35-28.64%) was significantly higher compared with their native counterparts (0.81-17.97%). Consolidated bioprocessing of the LCB using MTCC 1755 (Fusarium oxysporum) resulted in maximum ethanol concentration of 0.85 g/L and corresponded to 42 mg ethanol per gram of hydrothermally treated BG in 120 h followed by 0.83 g/L corresponding to 41.5 mg/g of untreated CS in 144 h. These ethanol concentrations corresponded to 23.43 and 21.54% of theoretical ethanol yield, respectively. LCB of CS and FF emerged as a suitable material to be subjected to test for enhanced ethanol production in future experiments through efficient fermentative microbial strains, appropriate enzyme loadings, and standardization of other fermentation parameters.

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The effect of agitation speed, enzyme loading and substrate concentration on enzymatic hydrolysis of cellulose from brewer’s spent grain

Cited by 91 publications

References 36 publications

Using an artificial neural network to predict the optimal conditions for enzymatic hydrolysis of apple pomace

Using an artificial neural network to predict the optimal conditions for enzymatic hydrolysis of apple pomace

Preliminary Study on Factors that Enhanced the Production of Uricase by Aspergillus Flavus

Evaluation and Selection of Potential Biomass Sources of North-East India towards Sustainable Bioethanol Production

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