Impact of Radio Frequency Treatment on Structural and Anti-Nutritional Components of Canadian Pulses
Bibliographic record
Abstract
This study investigates the application of 50 Ω radio frequency (RF) heating as a sustainable and efficient thermal processing technology for enhancing the nutritional and functional quality of peas and pinto beans. Pulses are recognized for their rich nutrient profile, yet their adoption in mainstream diets is limited by the presence of anti-nutritional factors (ANFs) such as trypsin inhibitors, phytic acid, and total phenolics. This study explores RF heating as an innovative alternative to conventional thermal treatments, targeting ANF reduction and microstructural improvements in both whole seed and flour forms of peas and pinto beans. Experimental trials were conducted using a pilot-scale 50 Ω RF system under varying conditions of moisture content, electrode design, power input, and treatment duration. The square hollow-type applicator exhibited improved heating uniformity, achieving effective dielectric heating at 12.5% moisture content and 100°C. Under these optimized conditions, trypsin inhibitor activity was reduced by over 80%, while effects on phytic acid and total phenolics were modest due to low moisture retention. Advanced imaging techniques, specifically synchrotron-based X-ray micro-computed tomography (µCT), were employed to characterize micropore formation in RF-treated samples. This non-destructive analysis validated that RF-induced thermal gradients promote micro-explosions and pore development, facilitating moisture migration and potential enhancement of nutrient bioavailability. Additionally, the research developed predictive models and MATLAB simulations to estimate top electrode voltage and optimize electric field distribution. Comparative modeling showed that voltage prediction excluding transient heating effects improved accuracy and correlated better with experimental data. Overall, this thesis integrates experimental processing, computational modeling, and structural characterization to establish a foundational framework for adopting RF heating in pulse processing. The findings contribute to the development of standard operating procedures (SOPs) for RF systems and support broader industrial applications aimed at enhancing the nutritional profile, processing efficiency, and commercial viability of pulse-based foods.
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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 | 0.000 | 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".