Invited review: Engineering aspects of physical treatments to increase fruit and vegetable phytochemical content
Bibliographic record
Abstract
Vigneault, C., Leblanc, D. I., Goyette, B. and Jenni, S. 2012. Engineering aspects of physical treatments to increase fruit and vegetable phytochemical content. Can. J. Plant Sci. 92: 373–397. The levels of phytochemicals in fruit and vegetables are affected by many preharvest and postharvest factors, such as cultivars, farming practices, environmental conditions, harvest techniques, and postharvest handling and treatment. Postharvest factors are generally the easiest to manage since produce handling takes place mainly under controllable conditions. Although specific physical treatments, such as heat and ultraviolet radiation, have been developed to increase the phytochemical content of horticultural produce, very little information is available on the engineering aspects of these treatments. A review of the engineering aspects related to phytochemical-enhancing physical treatments was undertaken to identify the process parameters required to obtain repeatable results, the basic information required for scale-up of the process, and the key parameters required to ensure appropriate monitoring and control of commercial applications. The uniformity, efficiency, efficacy, ease of control and ease of scale-up of various physical treatments were compared to support the development of a new phytochemical-enhancing treatment for potential commercial application. These treatment methods were considered independently of the physical characteristics of the produce treated (type of produce, size, shape, and positioning) to reduce the number of parameters to be studied with a view to scale-up processes, following identification of the optimal processing conditions through laboratory-scale testing.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.000 | 0.000 |
| 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".