Evidence map›Paper›PMID 29541159›Full record

ArticleBiotechnology for biofuels2018

Optimising conditions for bioethanol production from rice husk and rice straw: effects of pre-treatment on liquor composition and fermentation inhibitors.

Jia Wu, Adam Elliston, Gwenaelle Le Gall, Ian J Colquhoun, Samuel R A Collins, Ian P Wood, Jo Dicks, Ian N Roberts, Keith W Waldron

Open access · goldAbstract read
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Article in Biotechnology for biofuels, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed
3.1field-weighted citation impact, top 8% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

4 citing papers in PubMed, 92 citations in OpenAlex.

  1. Review
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

9 authors at 1 institution in 1 country.

Jia Wu1The Biorefinery Centre, Quadram Institute Bioscience, Norwich Research Park, Colney, Norwich, NR4 7UA UK.
Adam Elliston1The Biorefinery Centre, Quadram Institute Bioscience, Norwich Research Park, Colney, Norwich, NR4 7UA UK.
Gwenaelle Le Gall2The Analytical Sciences Unit, Quadram Institute Bioscience, Norwich Research Park, Colney, Norwich, NR4 7UA UK.
Ian J Colquhoun2The Analytical Sciences Unit, Quadram Institute Bioscience, Norwich Research Park, Colney, Norwich, NR4 7UA UK.
Samuel R A Collins1The Biorefinery Centre, Quadram Institute Bioscience, Norwich Research Park, Colney, Norwich, NR4 7UA UK.
Ian P Wood1The Biorefinery Centre, Quadram Institute Bioscience, Norwich Research Park, Colney, Norwich, NR4 7UA UK.
Jo Dicks3The National Collection of Yeast Cultures, Quadram Institute Bioscience, Norwich Research Park, Colney, Norwich, NR4 7UA UK.
Ian N Roberts3The National Collection of Yeast Cultures, Quadram Institute Bioscience, Norwich Research Park, Colney, Norwich, NR4 7UA UK.
Keith W Waldron1The Biorefinery Centre, Quadram Institute Bioscience, Norwich Research Park, Colney, Norwich, NR4 7UA UK.ORCID 0000-0001-7461-6703
Norwich Research Park · GB

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundRice straw and husk are globally significant sources of cellulose-rich biomass and there is great interest in converting them to bioethanol. However, rice husk is reportedly much more recalcitrant than rice straw and produces larger quantities of fermentation inhibitors. The aim of this study was to explore the underlying differences between rice straw and rice husk with reference to the composition of the pre-treatment liquors and their impacts on saccharification and fermentation. This has been carried out by developing quantitative NMR screening methods.

resultsAir-dried rice husk and rice straw from the same cultivar were used as substrates. Carbohydrate compositions were similar, whereas lignin contents differed significantly (husk: 35.3% w/w of raw material; straw 22.1% w/w of raw material). Substrates were hydrothermally pre-treated with high-pressure microwave processing across a wide range of severities. 25 compounds were identified from the liquors of both pre-treated rice husk and rice straw. However, the quantities of compounds differed between the two substrates. Fermentation inhibitors such as 5-HMF and 2-FA were highest in husk liquors, and formic acid was higher in straw liquors. At a pre-treatment severity of 3.65, twice as much ethanol was produced from rice straw (14.22% dry weight of substrate) compared with the yield from rice husk (7.55% dry weight of substrate). Above severities of 5, fermentation was inhibited in both straw and husk. In addition to inhibitors, high levels of cellulase-inhibiting xylo-oligomers and xylose were found and at much higher concentrations in rice husk liquor. At low severities, organic acids and related intracellular metabolites were released into the liquor.

conclusionsRice husk recalcitrance to saccharification is probably due to the much higher levels of lignin and, from other studies, likely high levels of silica. Therefore, if highly polluting chemical pre-treatments and multi-step biorefining processes are to be avoided, rice husk may need to be improved through selective breeding strategies, although more careful control of pre-treatment may be sufficient to reduce the levels of fermentation inhibitors, e.g. through steam explosion-induced volatilisation. For rice straw, pre-treating at severities of between 3.65 and 4.25 would give a glucose yield of between 37.5 and 40% (w/DW, dry weight of the substrate) close to the theoretical yield of 44.1% w/DW, and an insignificant yield of total inhibitors.

Indexed as

BioethanolFermentationInhibitorsLignocellulosic biomassPre-treatmentRice huskRice strawSaccharificationXylo-oligomers

Identifiers

PMID29541159
PMCPMC5844111
OpenAlexW2793664704

What OpenQuestion holds

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LicenceCC BY
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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.