Chitinase Production in Solid-State Fermentation from Oerskovia xanthineolytica NCIM 2839 and its Application in Fungal Protoplasts Formation

Waghmare, Shailesh R.; Kulkarni, S. S.; Ghosh, Jai S.

doi:10.1007/s00284-011-9978-1

Cited by 17 publications

(8 citation statements)

References 16 publications

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“…The factor moisture substrate was found as a significant effect in chitinase production. The moisture content in SSF is most important parameter [17]. Fungi are well known to favor moist environments for their growth.…”

Section: Resultsmentioning

confidence: 99%

Screening factors influencing chitinase production by Trichoderma virens using two level factorial design

Rachmawaty¹,

Syamsiah²,

Idris³

et al. 2018

AIP Conference Proceedings

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A study was conducted to screen parameters affecting the production of chitinase by Trichoderma virens on solid state fermentation of shrimp waste using the application of 2-level factorial design. The variables having most significant effect on chitinase production of shrimp waste were identified using a 2-level factorial design. The variable that were included in the screening experiment and their settings are incubation time, temperature, moisture substrate, pH, inoculum size and concentration ammonium sulphate. Half factorial experimental design for six variables was selected with six replicates at center point, leading to a total 38 experiments. All data obtained were then used as input to the Design Expert software for further analysis, according to steps outline. ANOVA analysis showed that the models were significant (p < 0.0001) for chitinase production. Incubation, temperature and moisture substrate were identified as significant factor affecting chitinase production on solid state fermentation. Statistical analysis revealed that the linear model is significant with R 2 value of 0.9448 for chitinase production. Chitinase production was found to be high at the optimal condition were: moisture substrate 50%, temperature 25 0 C and incubation 5 days. The obtained optimal process parameters predicted, Y= 0.265 U/g and verified by confirmation experiments, Y= 0.261 U/g. The actual experimental results were in agreement with the prediction. This statistical design proved to locate the optimum levels of the most significant parameters for chitinase production, with minimum effort and time

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Section: Resultsmentioning

confidence: 99%

Screening factors influencing chitinase production by Trichoderma virens using two level factorial design

Rachmawaty¹,

Syamsiah²,

Idris³

et al. 2018

AIP Conference Proceedings

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“…Protoplast have been formed from the various genera of fungi by using mixtures of different enzymatic preparations including β,1-3 glucanase, chitinase and protease (Robinson and Deacon 2001; Solis et al 1996; Stasz et al 1988; Tilburn et al 1983; Waghmare et al 2011). In this study we have used crude extract of chitinase produced from R. stolonifer to form protoplasts from various fungi such as A. niger, A. oryzae, T. viride, F. moniliforme.…”

Section: Discussionmentioning

confidence: 99%

Intergeneric fusant development using chitinase preparation of Rhizopus stolonifer NCIM 880

Sonawane¹,

Dandagal²,

Naikwadi³

et al. 2016

AMB Expr

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Fungal chitinase have tremendous applications in biotech industries, with this approach we focused on extracellular chitinase from Rhizopus stolonifer NCIM 880 for the formation of fungal protoplasts. The maximum chitinase production reached after 24 h at 2.5% colloidal chitin concentration in presence of starch as an inducer. Chitinase was extracted efficiently at 65% cold acetone concentration and then purified by using DEAE-Cellulose column chromatography. Purified chitinase having molecular weight 22 kDa with single polypeptide chain was optimally active at pH 5.0 and temperature 30 °C. The purified chitinase revealed kinetic properties like Km 1.66 mg/ml and Vmax 769 mM/min. Crude chitinase extract efficiently formed protoplasts from A. niger, A. oryzae, T. viride and F. moniliforme. The formed protoplasts of A. niger and T. viride showed 70 and 66% regeneration frequency respectively. Further, intergeneric fusants were developed successfully and identified at molecular level using RNA profiling. Thus, this study could be useful for strain improvement of various fungi for biotechnological applications.

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“…However, SSF has gain importance with years because of high productivity fields and numeral applications including utilization and treatment of agricultural-industrial wastes. Different studies on secondary metabolites or enzyme production are commonly compared between both fermentation processes, in majority of cases SSF is over SmF [20][21][22].…”

Section: Technological Advantages Of Ssfmentioning

confidence: 99%

“…In case of enzymes, they are more studied for many applications including value added to industrial wastes [26] which could be used as feed for ruminant [27]. Food industries take enzymes for clarification purposes in fruit juices, like pectinases [28] andlipases for hydrolysis of fats and oils [29], other industries use cellulases for second generation ethanol production [30] and chitinases for application in fungal protoplast formation [22].…”

Section: Enzyme Production In Ssfmentioning

confidence: 99%

Modeling Schemes for Control of Bed-Temperature Gradients in Solid-State Fermentation

Aguilar

2017

AIBM

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Mathematical modeling is a useful tool to represent true processes on solid-state fermentation, more employed on scale-up and for better automatic control strategy designs. In the case of temperature profiles during the growth of microorganisms, models reproduce the events well, given valuable information to prevent or know the maximum temperature along the process. The election of differential mathematical models over others are better to use because of the simplicity and further modifications on the models, according to different bioreactor design, they could improve solid fermentation processes. In the present work, a review on the mathematical models for heat transfer in packed bed bioreactor is showed. In order to evaluate the best models for control design effects, the main characteristics for the models reported in the literature are discussed. Also, the analysis of the models can be useful to define an optimal bed-temperature profile that improves the solid-state fermentation.

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Chitinase Production in Solid-State Fermentation from Oerskovia xanthineolytica NCIM 2839 and its Application in Fungal Protoplasts Formation

Cited by 17 publications

References 16 publications

Screening factors influencing chitinase production by Trichoderma virens using two level factorial design

Screening factors influencing chitinase production by Trichoderma virens using two level factorial design

Intergeneric fusant development using chitinase preparation of Rhizopus stolonifer NCIM 880

Modeling Schemes for Control of Bed-Temperature Gradients in Solid-State Fermentation

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