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Record W3048427649 · doi:10.1093/tas/txaa153

Fiber degrading enzymes increased monosaccharides release and fermentation in corn distillers dried grains with solubles and wheat middlings steeped without or with protease

2020· article· en· W3048427649 on OpenAlexafffund
Youngji Rho, Rob Patterson, Iris J. Joye, Mario M. Martínez, E. James Squires, Elijah G. Kiarie

Bibliographic record

VenueTranslational Animal Science · 2020
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicAnimal Nutrition and Physiology
Canadian institutionsCanadian Bio-Systems (Canada)University of Guelph
FundersNatural Sciences and Engineering Research Council of CanadaOntario Agri-Food Innovation Alliance
KeywordsChemistryMonosaccharideFood scienceXylanaseCellulaseXyloseEnzymeFermentationProteaseArabinoseBiochemistry

Abstract

fetched live from OpenAlex

Abstract Treating fibrous feed ingredients with exogenous feed enzymes may improve their utilization in monogastric animals. An in vitro study was conducted to determine the effects of steeping corn distillers dried grains with solubles (DDGS) or wheat middlings (WM) with exogenous feed enzymes. Four treatments were arranged as follows: 1) co-product steeped with water (CON), 2) CON plus 0.5-g fiber degrading enzymes (FDE), 3) CON plus 0.5-g protease (PRO), and 4) CON plus 0.5-g FDE and 0.5 g PRO (FDEPRO). The FDE contained about 62,000, 37,000, and 8,000 U/g of xylanase, cellulase, and β-glucanase, respectively, whereas activities in PRO amounted to 2,500,000, 1,300,000, and 800,000 U/g of acid, alkaline, and neutral proteases, respectively. Briefly, 50 g of DDGS or WM samples (n = 8) were mixed with 500-mL water with or without enzymes and steeped for 0 to 72 h at 37 °C with continuous agitation. The pH, concentration of monosaccharides, and organic acids in the supernatant and apparent disappearance (AD) of fiber in solids were measured at 0, 12, 24, 48, and 72 h. There was treatment and time interaction (P < 0.005) on monosaccharides concentration. At 12 h, arabinose and glucose concentrations were similar (P > 0.05) between FDE and FDEPRO but higher (P = 0.002) than for CON in DDGS. For WM, FDE, and FDEPRO had higher (P < 0.001) xylose concentration than CON and PRO, whereas glucose concentration was higher (P < 0.001) for enzymes than CON at 12 h. However, FDEPRO had higher (P < 0.001) xylose concentration than CON, whereas xylose concentration for FDE and PRO was intermediate at 24 h. There was an interaction (P < 0.05) between treatment and time effect on lactic acid concentration in DDGS and WM (P < 0.005), and acetic acid concentration in WM (P < 0.001). In general, monosaccharide concentration was higher between 12 and 24 h and decreased after 48 h, whereas the pH decreased, and concentration of organic acids increased continuously over time (P < 0.05). The AD of NDF and ADF in DDGS was greater (P = 0.001) for FDE and FDEPRO than CON and PRO at 72 h. In WM, enzymes increased (P = 0.007) AD of NDF relative to CON at 72 h. Nonetheless, greater (P < 0.05) AD of fiber was observed between 48 and 72 h. In conclusion, although there were differences in responses among co-products, fiber degrading enzymes increased release of fermentable monosaccharides from co-products at 12 to 24 h of steeping and these effects were not extended with the addition of protease.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.002
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.037
GPT teacher head0.237
Teacher spread0.200 · 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 source (direct Gemma or distilled Codex), 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

Citations10
Published2020
Admission routes2
Has abstractyes

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