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Record W2891359098 · doi:10.1021/acs.jafc.8b03985

New Method for Accurate Determination of Polyphenol Oxidase Activity Based on Reduction in SERS Intensity of Catechol

2018· article· en· W2891359098 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueJournal of Agricultural and Food Chemistry · 2018
Typearticle
Languageen
FieldChemistry
TopicSpectroscopy and Chemometric Analyses
Canadian institutionsUniversity of Manitoba
Fundersnot available
KeywordsCatecholPolyphenol oxidaseChemistryCatechol oxidasePolyphenolIntensity (physics)Reduction (mathematics)ChromatographyBiochemistryEnzymeMathematicsAntioxidantPeroxidaseOptics

Abstract

fetched live from OpenAlex

Rapid and accurate measurement of polyphenol oxidase (PPO) activity is important in the food industry as PPOs play a vital role in catalyzing enzymatic reactions. The aim of this study was to develop surface-enhanced Raman scattering (SERS) approach for accurate determination of PPO activity in fruit and vegetables using the reduction in SERS intensity of catechol in reaction medium. Within a certain catechol concentration, when a purified PPO solution was analyzed, the reduction in SERS intensity (ΔI) was linear to PPO activity (Ec) in a wide range of 500–50 000 U/L, and a linear regression equation of log ΔI/Δt = 0.6223 log Ec + 0.8072, with a correlation coefficient of 0.9689 and a limit of detection of 224.65 U/L, was obtained. The method was used for detecting PPO activity in apple and potato samples, and the results were compared with those obtained from colorimetric assay, which demonstrated that the proposed method could be successfully used for detecting PPO activity in food samples.

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.

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)0.000
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 score0.004
Threshold uncertainty score0.355

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.019
GPT teacher head0.295
Teacher spread0.276 · 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