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Record W4384119977 · doi:10.1002/jms.4960

Authenticity study of commercial samples of St. John's wort by paper spray ionization mass spectrometry and chemometric tools

2023· article· en· W4384119977 on OpenAlexaboutno aff
Ana Gabriella Carvalho Miguita, Rodinei Augusti, Marcelo M. Sena, Clésia C. Nascentes

Bibliographic record

VenueJournal of Mass Spectrometry · 2023
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicNatural Compound Pharmacology Studies
Canadian institutionsnot available
FundersFundação de Amparo à Pesquisa do Estado de Minas GeraisConselho Nacional de Desenvolvimento Científico e TecnológicoCoordenação de Aperfeiçoamento de Pessoal de Nível Superior
KeywordsHypericum perforatumChemistryMass spectrometryChromatographyTraditional medicineAgeratum conyzoidesBotanyWeed

Abstract

fetched live from OpenAlex

Hypericum perforatum L. (St. John's wort) is one of the world's most consumed medicinal plants for treating depression and psychiatric disorders. Counterfeiting can occur in the medicinal plant trade, either due to the lack of active ingredients or the addition of substances not mentioned on the labels, often without therapeutic value or even harmful to health. Hence, 43 samples of St. John's wort commercially acquired in different Brazilian regions and other countries were analyzed by paper spray ionization mass spectrometry (PS-MS) and modeled by principal component analysis. Hence, samples (plants, capsules, and tablets) were extracted with ethanol in a solid-liquid extraction. For the first time, PS-MS analysis allowed the detection of counterfeit H. perforatum samples containing active principles typical of other plants, such as Ageratum conyzoides and Senna spectabilis. About 52.3% of the samples were considered adulterated for having at least one of these two species in their composition. Furthermore, out of 35 samples produced in Brazil, only 13 were deemed authentic, having only H. perforatum. Therefore, there is a clear need to improve these drugs' quality control in Brazil.

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)0.001
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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.738
Threshold uncertainty score0.532

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.004
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 head0.035
GPT teacher head0.277
Teacher spread0.242 · 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 designObservational
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

Citations6
Published2023
Admission routes1
Has abstractyes

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