Aroma detection and control in passive and dynamic food systems for superior product
Notice bibliographique
Résumé
Passive (static) and dynamic studies have shown aroma to be an important aspect of food quality, which can be used to differentiate, classify, and grade foodstuffs, and in some cases it can be used to predict other quality characteristics. Monitoring and control of food aroma changes during food processing can significantly improve the quality of the final product in terms of flavour, color, taste, and overall appearance. Hence, it is a prominent and urgent field of study in the post production systems. Passive aroma detection of unprocessed foods and dynamic aroma detection during food processing was undertaken using a fast GC analyzer -- zNose. During the study on the passive aroma detection, the aroma of Chinese spirits (Fenjiu) and mango (Mangifera indica L.) fruits, (i.e., liquid and solid states, respectively) was analyzed. In the study of Chinese spirits, aroma profiles of Fenjiu liquor samples of different quality levels were acquired and used for quality classification and prediction. Measurements of dielectric properties of the samples were also conducted to estimate alcohol concentration. In the study of mango fruits, aroma changes of mango samples were monitored during their shelf life and used to evaluate mango quality. Ripening and rots were detected with 80% and 93% accuracy, respectively. During the study of dynamic aroma detection, a real-time aroma monitoring and control system was developed for use during microwave drying. Aroma signals of a processed food item were detected with zNose and analyzed with a fuzzy logic algorithm to determine the optimal food drying temperature. Phase control was used to adjust the microwave power level to meet temperature requirements. Carrot (Daucus carota L.) and apple (Malus domestica Borkh) were selected as representatives of vegetables and fruits. In carrot drying, samples could be dried in a short time at high temperatures but the interior of some sample cubes was burnt. Drying at a lower temperature extended the drying process, but led to a great loss of aroma in the finished product.' The best results were obtained at 60°C. Based on these results, a fuzzy logic controller was designed and employed to control the drying process according to carrot aroma changes. To investigate the possibility of aroma improvement without zNose assistance, a linear control method was developed whereby a temperature control profile imitated the fuzzy logic control, but aroma control was not included. With these new control strategies, the carrot color and flavour were significantly improved and less time and power were consumed. Similar results were achieved when apple was microwave-dried. Apple aroma was monitored online during microwave drying processes and controlled with similar fuzzy and linear control strategies. Apple color, aroma, and overall appearance remained intact with the new strategies and less time and power were consumed. In contrast to the carrot drying, a different linear temperature profile was required for apple drying in terms of aroma retention.
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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 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 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 | 0,001 | 0,001 |
| 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 ».