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Record W1984607949 · doi:10.1080/19440049.2010.521772

Pyrrolizidine alkaloids in honey: comparison of analytical methods

2010· article· en· W1984607949 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

VenueFood Additives & Contaminants Part A · 2010
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicPlant Toxicity and Pharmacological Properties
Canadian institutionsIntertek (Canada)
Fundersnot available
KeywordsPyrrolizidineChemistryChromatographyBee pollenPollenChemometricsFood scienceBiologyBotanyStereochemistry

Abstract

fetched live from OpenAlex

Pyrrolizidine alkaloids (PAs) are a structurally diverse group of toxicologically relevant secondary plant metabolites. Currently, two analytical methods are used to determine PA content in honey. To achieve reasonably high sensitivity and selectivity, mass spectrometry detection is demanded. One method is an HPLC-ESI-MS-MS approach, the other a sum parameter method utilising HRGC-EI-MS operated in the selected ion monitoring mode (SIM). To date, no fully validated or standardised method exists to measure the PA content in honey. To establish an LC-MS method, several hundred standard pollen analysis results of raw honey were analysed. Possible PA plants were identified and typical commercially available marker PA-N-oxides (PANOs). Three distinct honey sets were analysed with both methods. Set A consisted of pure Echium honey (61-80% Echium pollen). Echium is an attractive bee plant. It is quite common in all temperate zones worldwide and is one of the major reasons for PA contamination in honey. Although only echimidine/echimidine-N-oxide were available as reference for the LC-MS target approach, the results for both analytical techniques matched very well (n = 8; PA content ranging from 311 to 520 µg kg(-1)). The second batch (B) consisted of a set of randomly picked raw honeys, mostly originating from Eupatorium spp. (0-15%), another common PA plant, usually characterised by the occurrence of lycopsamine-type PA. Again, the results showed good consistency in terms of PA-positive samples and quantification results (n = 8; ranging from 0 to 625 µg kg(-1) retronecine equivalents). The last set (C) was obtained by consciously placing beehives in areas with a high abundance of Jacobaea vulgaris (ragwort) from the Veluwe region (the Netherlands). J. vulgaris increasingly invades countrysides in Central Europe, especially areas with reduced farming or sites with natural restorations. Honey from two seasons (2007 and 2008) was sampled. While only trace amounts of ragwort pollen were detected (0-6.3%), in some cases extremely high PA values were detected (n = 31; ranging from 0 to 13019 µg kg(-1), average = 1261 or 76 µg kg(-1) for GC-MS and LC-MS, respectively). Here the results showed significantly different quantification results. The GC-MS sum parameter showed in average higher values (on average differing by a factor 17). The main reason for the discrepancy is most likely the incomplete coverage of the J. vulgaris PA pattern. Major J. vulgaris PAs like jacobine-type PAs or erucifoline/acetylerucifoline were not available as reference compounds for the LC-MS target approach. Based on the direct comparison, both methods are considered from various perspectives and the respective individual strengths and weaknesses for each method are presented in detail.

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 score0.126
Threshold uncertainty score0.563

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 head0.047
GPT teacher head0.358
Teacher spread0.311 · 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