Emissivity and moisture-temperature response of yellow peas in the mid-infrared region
Bibliographic record
Abstract
One of the modern tools used in designing of food processing equipment is the application of mathematical modelling and computer simulations, which normally lead to optimization of design or process parameters.One of the challenges in a successful application of developed mathematical models in food processing by high intensity infrared is a shortage oreven lack of certain basic parameters which describe processed product orthe process itself.Two such essential parameters (emissivity of peas and moisture-temperature response of peas to infrared (lR) heating) were not available in a mathematical model developed in the Department of Biosystems Engineering, University of Manitoba, for infrared processing of peas.The lack of these parameters hindered the validation process and, therefore became a focus of this work.The spectral emissivity of yellow peas in the mid-infrared region was determined experimentally using two methods: i) reference emitter method and ii) surface response method.Both methods depended upon the accuracy of the dynamic temperature measurements using infrared pyrometer wth a spectral response of I to14 zm.The reference emitter method was based on the relationship between emitted and reflected energy.The surface response method was based on the fact that radiation emitted by the sample surface is related to the temperature to the power four and is proportional to the emissivity.The results indicated that the spectral emissivity of yellow peas was entirely dependent on the temperature and increased with temperature from 0.7 to 0.95.-t-I thank Prof, W. E. Muir and Prof, S. D
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 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 | 0.000 | 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) | 0.001 | 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".