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Record W2326837510 · doi:10.1021/jf2026007

Rapid NMR Method for the Quantification of Organic Compounds in Thin Stillage

2011· article· en· W2326837510 on OpenAlexaff
Kornsulee Ratanapariyanuch, Jianheng Shen, Yunhua Jia, Robert T. Tyler, Youn Young Shim, Martin J. T. Reaney

Bibliographic record

VenueJournal of Agricultural and Food Chemistry · 2011
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicMetabolomics and Mass Spectrometry Studies
Canadian institutionsUniversity of Saskatchewan
Fundersnot available
KeywordsStillageChemistryBetaineLactic acidHigh-performance liquid chromatographyAcetic acidChromatographyGlycerolProton NMRCarbon-13 NMROrganic acidFermentationOrganic chemistryBacteria

Abstract

fetched live from OpenAlex

Thin stillage contains organic and inorganic compounds, some of which may be valuable fermentation coproducts. This study describes a thorough analysis of the major solutes present in thin stillage as revealed by NMR and HPLC. The concentration of charged and neutral organic compounds in thin stillage was determined by excitation sculpting NMR methods (double pulse field gradient spin echo). Compounds identified by NMR included isopropanol, ethanol, lactic acid, 1,3-propanediol, acetic acid, succinic acid, glycerophosphorylcholine, betaine, glycerol, and 2-phenylethanol. The concentrations of lactic and acetic acid determined with NMR were comparable to those determined using HPLC. HPLC and NMR were complementary, as more compounds were identified using both methods. NMR analysis revealed that stillage contained the nitrogenous organic compounds betaine and glycerophosphorylcholine, which contributed as much as 24% of the nitrogen present in the stillage. These compounds were not observed by HPLC analysis.

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)0.001
metaresearch head score (Gemma)0.001
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: Methods · Consensus signal: Methods
Teacher disagreement score0.002
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0020.001

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 head0.023
GPT teacher head0.241
Teacher spread0.218 · 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
GenreMethods

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

Citations36
Published2011
Admission routes1
Has abstractyes

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