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Record W2196903988 · doi:10.3168/jds.2015-10059

Incorporation of whey permeate, a dairy effluent, in ethanol fermentation to provide a zero waste solution for the dairy industry

2015· article· en· W2196903988 on OpenAlexafffund
Archana Parashar, Yiqiong Jin, Beth Mason, Michael Chae, David C. Bressler

Bibliographic record

VenueJournal of Dairy Science · 2015
Typearticle
Languageen
FieldEngineering
TopicBiofuel production and bioconversion
Canadian institutionsCape Breton UniversityUniversity of Alberta
FundersNatural Sciences and Engineering Research Council of CanadaAlberta Livestock and Meat Agency
KeywordsFermentationChemistryEthanol fuelFood scienceBiofuelLactoseHydrolysisEnzymatic hydrolysisLactic acidPulp and paper industryBiotechnologyBiochemistryBacteriaBiology

Abstract

fetched live from OpenAlex

This study proposes a novel alternative for utilization of whey permeate, a by-product stream from the dairy industry, in wheat fermentation for ethanol production using Saccharomyces cerevisiae. Whey permeates were hydrolyzed using enzymes to release fermentable sugars. Hydrolyzed whey permeates were integrated into wheat fermentation as a co-substrate or to partially replace process water. Cold starch hydrolysis-based simultaneous saccharification and fermentation was done as per the current industrial protocol for commercial wheat-to-ethanol production. Ethanol production was not affected; ethanol yield efficiency did not change when up to 10% of process water was replaced. Lactic acid bacteria in whey permeate did not negatively affect the co-fermentation or reduce ethanol yield. Whey permeate could be effectively stored for up to 4 wk at 4 °C with little change in lactose and lactic acid content. Considering the global abundance and nutrient value of whey permeate, the proposed strategy could improve economics of the dairy and biofuel sectors, and reduce environmental pollution. Furthermore, our research may be applied to fermentation strategies designed to produce value-added products other than ethanol.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.158
Threshold uncertainty score0.260

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.041
GPT teacher head0.278
Teacher spread0.237 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations111
Published2015
Admission routes2
Has abstractyes

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