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Retting and degumming of natural fibers by pectinolytic enzymes produced from Bacillus tequilensis SV11-UV37 using solid state fermentation

Overview of attention for article published in SpringerPlus, May 2016
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Title
Retting and degumming of natural fibers by pectinolytic enzymes produced from Bacillus tequilensis SV11-UV37 using solid state fermentation
Published in
SpringerPlus, May 2016
DOI 10.1186/s40064-016-2173-x
Pubmed ID
Authors

Swarupa Rani Chiliveri, Sravanthi Koti, Venkateswar Rao Linga

Abstract

The present study demonstrated the simultaneous production and optimization of pectinolytic enzymes (pectate lyase and polygalacturonase) under SSF from Bacillus tequilensis SV11-UV37 using wheat bran as a substrate, which is commercially viable and cost-effective. Optimization by one variable-at-a-time-approach showed a maximum yield of pectate lyase (1371.25 U/gds) and polygalacturonase (85.45 U/gds) with wheat bran using 80 % (v/w) moisture, 0.7 mm particle size, 20 % (v/w) inoculum, 1 % (w/w) pectin at 37 °C, pH 6 and 72 h of incubation. In addition, optimization using central composite design achieved 1.6-fold improvement in both pectate lyase (1828.13 U/gds) and polygalacturonase (105.55 U/gds) yield at optimum levels of pectin (3 %, w/w), inoculum size (20 %, v/w) and moisture level (80 %, v/w). Further, Retting studies concluded that the enzyme mixture was efficient in separating the whole fiber from kenaf and part (>75 %) from sunn hemp. In degumming of sunn hemp fibers, amount of galacturonic acid released and percentage weight loss was higher in successive alkali and enzymatic treatment than their independent treatments. The scanning electron microscopic analysis also confirmed that alkali followed by enzymatic treatment effectively removed non-cellulosic gummy material from the fiber; hence, this enzyme mixture may find feasible applications in the fiber and textile industry.

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Geographical breakdown

Country Count As %
Unknown 114 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 14 12%
Student > Bachelor 14 12%
Student > Master 9 8%
Researcher 7 6%
Student > Doctoral Student 7 6%
Other 18 16%
Unknown 45 39%
Readers by discipline Count As %
Agricultural and Biological Sciences 22 19%
Biochemistry, Genetics and Molecular Biology 15 13%
Engineering 5 4%
Chemistry 5 4%
Materials Science 3 3%
Other 17 15%
Unknown 47 41%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 1. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 18 October 2017.
All research outputs
#19,331,585
of 24,615,420 outputs
Outputs from SpringerPlus
#1,229
of 1,864 outputs
Outputs of similar age
#210,866
of 304,015 outputs
Outputs of similar age from SpringerPlus
#109
of 159 outputs
Altmetric has tracked 24,615,420 research outputs across all sources so far. This one is in the 18th percentile – i.e., 18% of other outputs scored the same or lower than it.
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