Notice bibliographique
Résumé
Abstract The recent application of fluorescence spectroscopy to food analysis is reviewed and future trends in fluorometry are discussed. For food proteins, two techniques, i.e. intrinsic fluorometry and extrinsic fluorometry, are contrasted. Changes in the fluorescence intensity due to tryptophan and the anisotropy were measured in food proteins, including β‐lactoglobulin, α‐lactalbumin and lysozyme, to estimate changes in molecular structure during environmental alterations. Major applications of extrinsic fluorometry to food proteins are surface hydrophobicity measurements using hydrophobic probes. For this purpose, this approach is currently the most popular method in food protein chemistry probably because of the simplicity of the analytical technique. Advantages and disadvantages of various fluorescence probes and their applications were compared. Toxins are one of the most appropriate applications of sensitive fluorometry. Examples of the application shown here are mycotoxins and toxins of shellfish poisoning. For the former, capillary electrophoresis was introduced as a new tool. For the latter, the methods of derivatization are critical and therefore compared. For enumerating bacterial infection, bioluminescence using luciferase and direct epifluorescent filter technique (DEFT) are being used. Further, immunofluorescence is useful for specifically detecting pathogenic bacteria such as Salmonella, Listeria and enterotoxigenic Escherichia coli. Fluorescence caused by heating foods and fat oxidation was measured to assess their intensity. The thiobarbituric acid (TBA) reaction for measuring fat oxidation by colorimetry has been replaced frequently by fluorometry to improve the accuracy and specificity. Vitamins have been other popular analytes for fluorometry. Water‐soluble and fat‐soluble vitamins are discussed separately. High‐performance liquid chromatography (HPLC) combined with fluorometric detectors is popular for the simultaneous analysis of multivitamins or multiforms of vitamins. Normal‐phase HPLC of fat‐soluble vitamins eliminated the need for solvent extraction and, in some cases, even the saponification process. As food additives, antibiotics (although they may be considered contaminants), aspartame and salicylates are discussed in this chapter. In amino acids analysis, reversed‐phase high‐performance liquid chromatography (RPHPLC) with fluorometric detectors has replaced the traditional ion exchange–ninhydrin colorimetric detector systems because of simpler, quicker elution and higher sensitivity of detection. In enzyme chemistry, alkaline phosphatase (ALP) determinations of the adequacy of milk pasteurization and proteolytic enzyme activity are the most frequently reported methods in the recent literature. A fluorometric substrate is used for the former, which converts to a fluorescent form upon loss of a phosphate radical due to the action of ALP. For the latter, the same reagent as in amino acid analysis emits fluorescence upon reaction with an α‐amino group yielded from proteins by proteolysis. During the past one or two decades, fluorometry has in many cases replaced traditional colorimetry because of its higher sensitivity and selectivity. However, the modern automated, quicker separation technique achieved by RPHPLC has, in some cases, allowed fluorometric detectors to be replaced by ultraviolet (UV) detectors. Although HPLC fluorometric detector systems will remain as sensitive, versatile methods for analytes at very low concentrations, such as toxins and antibiotics, a substantial enhancement of sensitivity is achieved by using laser‐induced fluorometry. This new trend is worth considering as a replacement for traditional radiolabeling techniques.
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 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) | 0,001 | 0,000 |
| Bibliométrie | 0,000 | 0,001 |
| 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) | 0,022 | 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 ».