Validation of the Biofish-300 SUL Enzymatic Biosensor for the Detection of Sulfite in Crustacean
Notice bibliographique
Résumé
Abstract Background: Sulfites are some of the oldest and most widespread preservatives in our food supply. They are food additives that have antioxidant properties, but they are also recorded as allergens by the main international regulatory bodies on food safety because of their adverse healtheffect. Hence, sulfites maximum concentration in foodstuff is regulated and they must be ensured by the agro-food processing industries. The most widely used technique for the quantification of sulfites is the Modified Monier-Williams (AOAC Official Method 990.28). Objective:In this method, SO2 is released from sulfites and some bound compounds when the sample is mixed with an acid (normally hydrochloric acid, but sometimes phosphoric acid) and heated. The SO2 is distilled using a stream of nitrogen gas,which carries the gaseous SO2 into an absorbing solution of hydrogen peroxide (H2O2) where it is oxidised to sulphuric acid. The amount of SO2 distilled into the H2O2 is determined by titration with 0.1M sodium hydroxide. Apart from being time consuming (at least 2 h) and the usage of toxicsolvents, the method presents some other disadvantages that make it inappropriate as a routine-controltechnique for the agro-food industry. Hence, the industry demands simple, fast and accurate methods for sulfite level monitoring. Methods: BIOLAN is a SME that develops and commercializesbiosensors for quantitative analysis of food quality and safety parameters, based on its proprietary enzyme-based electrochemical biosensor technology platform. This technology enables high accurate and robust analysis with a compact device that help the users to control the quality in an easy and safety manner. Biofish-300 SUL method is a highly specificenzimatic biosensor for the rapid quantification ofsulfite, measured as SO2 content, in crustaceans. It consists on the extraction of sulfitein an aqueous based solution, by the aid of an Ultra-turrax or similar, and its subsequent quantification by the biosensor after previous calibration (3 min). Results: Sulfite in raw shrimp head-on, raw shrimp head-off, and boiled shrimp was analyzed, and performance was examined using naturally contaminated and spiked samples by comparisons with AOAC Official Methods of AnalysisSM (OMA) 990.28. Linearity, selectivity, matrix, consistency, and robustness were evaluated. All results were within acceptable ranges except robustness, which reflected deviation in the sample volume and ultraturrax time compared with the standard assay procedures describedin the Biofish 300 SUL Instruction Manual. Accuracy, assessed as a comparison of the Biofish results with the OMA results, ranged from 82 to 115% in all samples except for fortified raw shrimp head-on, inwhich the low level yielded an accuracy of 138%. The method bias was in general negative in both incurred and fortified high levels, and slightly positive in incurred low levels. Repeatability was very good as shown by the low RSDr values, demonstrating acceptable repeatability precision with results <10% in most of the evaluated values. Regression analyses showed a good correlation between the Biofish and OMA methods with R2 = 0.99 in all cases. Conclusions: As awhole, accuracy, recovery and bias within range results indicate that the kit provides accurate and precise sulfite quantification for all the evaluated matrices, confirming that sample preparation and assay procedures produce acceptable results. Biofish 300 SUL has proved to be a suitable tool for monitoring sulfite levels in quality control routines dueto its high accuracy, precision, rapid response andease of use. Highlights: With a simple sample preparation, results are obtained in approximately 3 min, making a big difference with other technologies that require specific skills or tedious sample pretreatments and analysis procedures.
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Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
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 tête enseignante, 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 ».