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Record W3044200099 · doi:10.1038/s41598-020-69189-3

Color and molecular structure alterations of brazilein extracted from Caesalpinia sappan L. under different pH and heating conditions

2020· article· en· W3044200099 on OpenAlexaff
Luxsika Ngamwonglumlert, Sakamon Devahastin, Naphaporn Chiewchan, G. S. V. Raghavan

Bibliographic record

VenueScientific Reports · 2020
Typearticle
Languageen
FieldMedicine
TopicBiological Stains and Phytochemicals
Canadian institutionsMcGill University
FundersKing Mongkut's University of Technology ThonburiRoyal Golden Jubilee (RGJ) Ph.D. ProgrammeThailand Research Fund
KeywordsChemistryAqueous solutionDeprotonationThermal stabilityFourier transform infrared spectroscopyNuclear chemistryFlavorOrganic chemistryFood scienceChemical engineering

Abstract

fetched live from OpenAlex

Brazilein extract from sappan wood (Caesalpinia sappan L.) has potential for use as natural food colorant since it has no unique flavor and taste. Although brazilein has long been applied in several traditional foods and beverages, information on its stability, which is of importance for practical application, is still limited. In this work, brazilein was isolated from sappan wood; its purity was confirmed by nuclear magnetic resonance spectroscopy. Relations between molecular structures and color as well as thermal stabilities of brazilein in aqueous solutions at pH 3, 7 and 9 were for the first time investigated. At the lowest pH, zero net-charge structure of brazilein, which exhibited yellow color, was predominantly found. The deprotonated and fully deprotonated structures of brazilein, which exhibited orange and red colors, respectively, were found when pH of the aqueous solutions increased. The forms of brazilein existing at the higher pH suffered extensive degradation upon heating, while the form existing at the lowest pH possessed higher stability. Heat-induced deprotonation and degradation were confirmed by UV-visible and Fourier-transform infrared spectra as well as losses of brazilein content.

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)0.000
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 score0.277
Threshold uncertainty score0.298

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 head0.018
GPT teacher head0.276
Teacher spread0.257 · 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

Citations73
Published2020
Admission routes1
Has abstractyes

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