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Record W3045203992 · doi:10.1016/j.jafr.2020.100060

A spectrophotometric method for determining the amount of folic acid in fortified salt

2020· article· en· W3045203992 on OpenAlexaff
Oluwasegun Modupe, Julie Bloquet Maurras, Levente L. Diósady

Bibliographic record

VenueJournal of Agriculture and Food Research · 2020
Typearticle
Languageen
FieldMedicine
TopicFolate and B Vitamins Research
Canadian institutionsUniversity of Toronto
FundersBill and Melinda Gates Foundation
KeywordsFolic acidSalt (chemistry)ChemistryDegradation (telecommunications)ChromatographyHigh-performance liquid chromatographyGlutamic acidBiochemistryOrganic chemistryAmino acidMedicine

Abstract

fetched live from OpenAlex

Analytical methods for quantifying and monitoring the degradation of micronutrients added to food are crucial to food fortification programs. In the case of folic acid in fortified salts, there are difficulties in developing an effective analytical method due to interference of salt in the standard HPLC methods, as salt precipitates in the HPLC column. To circumvent the problem, a spectrophotometric method was developed to quantify folic acid and monitor its degradation in salt. A distinct absorption wavelength was selected for folic acid in sodium carbonate solution. Of the three wavelengths where maximum absorption was observed for folic acid, 285 nm was selected as being selective for folic acid in the presence of pteroic acid, glutamic acid, aminobenzoic acid, and other products of degradation of folic acid. The method was calibrated for 1-25 μg/mL folic acid (R2 = 1). The recovery was 100 ± 1.2% and 100 ± 1.8% for folic acid in salt and solution, respectively. The limit of detection and quantification for this method is 0.011 μg/mL and 0.033 μg/mL, respectively. The method is accurate, precise, and selective for folic acid in the presence of potential products of folic acid degradation, and is suitable for monitoring folic acid degradation in fortified salt.

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)0.001
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 score0.450
Threshold uncertainty score0.387

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.002
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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 head0.121
GPT teacher head0.418
Teacher spread0.297 · 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

Citations40
Published2020
Admission routes1
Has abstractyes

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