Compositional Profiling for the Quality Assessment of Canadian Honeys and Their Biotransformation into Functional Sweeteners
Notice bibliographique
Résumé
Honey has been cherished for centuries as a natural sweetener and continues to hold a significant place in the food industry due to its unique taste, nutritional properties, and health benefits. With monofloral honeys attracting increased consumer attention, there is a growing need to develop novel methods for authenticating honeys. It is crucial not only to detect adulteration, such as the addition of syrups, but also to identify the botanical origins of honey, which has become an industrial demand. The most widely adopted technique for authenticating floral type is pollen analysis, which is highly sophisticated. Recent advances in honey authentication methods have primarily focused on identifying biomarkers, such as phenolic compounds. However, previous studies have suggested that both the carbohydrate composition and enzymes in honey have correlations with its botanical sources. Few studies have been conducted on the authentication of Canadian honeys, and no comprehensive profiling for sugars and enzymes were established. Therefore, this study aims to provide insight into the sugar and enzyme composition of four types of monofloral honey commonly found in the Canadian market.The first objective was to conduct the carbohydrate and enzymatic profiling of 163 selected Canadian monofloral honeys, namely buckwheat, clover, blueberry, and goldenrod, and to establish a robust authentication method for botanical origin differentiation, while focusing on identifying potential biomarkers. The activities of five enzymes, namely diastase, invertase, acid phosphatase, glucose oxidase, and catalase, were examined. 2 monosaccharides (fructose and glucose), 6 disaccharides (trehalose, isomaltose, sucrose, maltose, nigerose, and gentiobiose), and 1 trisaccharide (erlose) were successfully identified and quantified among all honey samples using high performance anion exchange chromatography with pulse amperometric detection (HPAEC-PAD) and liquid chromatography coupled with quadrupole time-of-flight mass spectrometry (LC/MS-QToF). Results showed the average enzymatic activity and sugar content varies across the four floral types. Multivariate analysis revealed the potentiality of acid phosphatase and catalase activities as markers for identifying botanical sources. Statistically significant (p<0.0001) negative correlations were observed between glucose content and Isomaltose, gentiobiose, or nigerose content, and between 5-hydroxymethylfurfural (HMF) content and diastase or invertase activities. Additionally, prediction models were generated based on the variables quantified with accuracy scores varing between 80-90%. Agreeing with the previous results, the model suggested that acid phosphatase and catalase activities alongside electrical conductivity, peak area of HMF, invertase activity, pH and erlose content to be the most impactful features.The second objective of this study aimed at lowering the caloric content of honey by optimizing the bioconversion of intrinsic D-fructose into D-allulose via D-allulose-3-epimerase (DAEase). The D-allulose-3-epimerase sequence from Dorea sp. was expressed in Escherichia coli and DAEase was produced. A three-variable central composite rotatable design was created to optimize the initial honey concentration, reaction time and quantity of enzyme addition for maximizing net allulose production as well as the bioconversion yield (%, w/w) using a response surface methodology (RSM). Initial honey concentration as well as the reaction time were found to have the greatest impact on the bioconversion yield of D-allulose. The optimized conditions were then applied in the bioconversion of D-allulose in honey from three selected monofloral origins (buckwheat, clover, blueberry). End-product D-allulose concentration, and bioconversion yield were assessed, and color differences, ŋ50 apparent viscosity, and pH changes were measured. Significantly lower bioconversion rate of 9.55±5.55% (w/w) was observed with buckwheat honey, and a maximum bioconversion rate of 29.5% (w/w) was achieved in clover honey, providing a potential in producing fortified functional honey
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) | 0,000 | 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 ».