High Throughput Analytical Techniques for the Determination and Confirmation of Residues of 653 Multiclass Pesticides and Chemical Pollutants in Tea by GC/MS, GC/MS/MS, and LC/MS/MS: Collaborative Study, First Action 2014.09
Notice bibliographique
Résumé
Thirty laboratories from fom North and South America, Europe, and Asia participated in this AOAC collaborative study (15 from China; five from Germany; two each from Italy and the United States; and one each from the Republic of Korea, Canada, Spain, Japan, Belgium, and India). Participants represented government regulatory, commercial testing, university, research institute, and private laboratories. The single-laboratory validated (SLV) tea method was evaluated in the collaborative study to determine the recovery and reproducibility of the method under multilaboratory conditions. Since there were no restrictions regarding the type of analytical instrumentation to use for the analyses, laboratories used a combination of equipment that included GC/MS, GC/MS/MS, and LC/MS/MS instruments from 22 different manufacturers, 21 brands of GC and LC columns, 13 different GC temperature programming profiles, 11 LC gradient elution programs, and six different vendor manufactured SPE cartridges. Even though all the analytical performance parameters for all the 653 compounds had been determined in the SLV study, guidance was obtained from an expert review panel of the AOAC Method-Centric Committee on Pesticide Residues to conduct the multilaboratory collaborative study based on 20 selected compounds that can be analyzed by GC/MS and 20 compounds that can be analyzed by LC/MS/MS. Altogether, 560 samples covering the 40 selected pesticides were analyzed in the study. These samples included green tea and oolong tea samples fortified typically at the European Union maximum residue limit for regulatory guidance and compliance, aged tea samples incurred with 20 pesticides, and green tea and oolong tea samples incurred with five pesticides. The analysis of the 560 samples generated a total of 82 459 test results by the 30 participating laboratories. One laboratory failed to meet the proficiency requirements in the precollaborative study. Therefore, its data submitted for the collaborative study were excluded from further analysis and interpretation. The results presented are therefore the 6638 analytical results obtained from the 29 remaining laboratories, which included 1977 results generated by GC/MS, 1704 results by GC/MS/MS, and 2957 results by LC/MS/MS. It was determined after application of the Grubbs and Dixon tests for outliers to the data sets that there were 65 outlier results from the 1977 GC/MS results (3.3%), 65 outlier results from the 1704 GC/MS/MS results (3.8%), and 57 outlier results out of 2957 LC/MS/MS results (1.9%), representing 0.98, 0.98, and 0.86%, respectively, of the 6638 results generated in the study. Analysis with the AOAC statistical software package also confirmed that the method is rugged, and average recovery, average concentration, RSDr, RSDR, and HorRat values all meet recovery and reproducibility criteria for use in multiple laboratories. The Study Director is recommending this method for adoption as an AOAC First Action Official MethodSM.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,015 | 0,004 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,002 |
| Bibliométrie | 0,004 | 0,003 |
| Études des sciences et des technologies | 0,002 | 0,001 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,002 | 0,003 |
| Intégrité de la recherche | 0,002 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,001 | 0,001 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».