How much do process parameters affect the residual quality attributes of dried fruits and vegetables for convective drying?
Bibliographic record
Abstract
Drying processes reduce the amount of available essential nutrients in dried plant-based foods to a large extent compared to fresh produce. This reduction is much larger than the differences in the final quality of products dried using various processing parameters and, in most cases, different drying methods. This aspect is, however, rarely highlighted. Here, the extent to which different convective drying methods reduce the nutritional content, namely vitamin C, carotenoids and phenolic content of dried fruits and vegetables, compared to fresh produce was quantified using literature data. The impact of different drying process parameters, such as air temperature, airspeed and relative humidity on the nutritional content of fruits and vegetables were compared. Results revealed that convective drying reduced the amount of vitamin C, carotenoids and the phenolic content of dried fruits and vegetables by up to 70%. The reduction in the residual vitamin C and carotenoid content of dried fruits and vegetables due to differences in air temperature (∼40%), airspeed (∼20%), and relative humidity (∼20%) is much less than the nutritional quality losses due to the drying process. The residual vitamin C, carotenoids and phenolic contents in convective-dried fruits and vegetables are ∼30% less than those in freeze-dried products. This study confirms that little absolute gains in nutritional quality can be achieved by opting for either an alternative drying method or optimizing processing parameters since the drying process already results in a low nutritional quality of dried products. As such, the remaining micronutrient concentration of dried products should not necessarily be a decisive criterion in selecting the most appropriate drying method or processing parameters for fruits and vegetables. Instead, other key performance indicators such as the drying time, energy consumption, or sensory properties such as color, texture, and rehydration capacity could eventually have a greater influence on the decision-making process.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 source (direct Gemma or distilled Codex), 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".