Aroma detection and control in passive and dynamic food systems for superior product
Bibliographic record
Abstract
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".