Performance of electrode materials during food processing by pulsed electric fields
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Bibliographic record
Abstract
Food processing by pulsed electric fields (PEFs) is a promising non-thermal method for increasing the shelf-life of liquid food products such as milk, juices and beer. Maintaining both the fresh-like taste and the original nutritional value makes PEF processing advantageous over conventional thermal pasteurization <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> . However, physical contact between the liquid food and the metallic electrodes during the PEF process is unavoidable and causes some metallic ions to be released from the electrodes into the processed food. The released metallic ions represent a challenging problem for this technology because they may affect the taste of the processed food and/or they reduce the life-time of the electrodes.This study is to evaluate the performance of various metals used as the electrodes by comparing the rates of release of metallic ions from chromium, nickel, silver and titanium as well as conventional stainless steel during PEF processing. The concentration of metallic ions in PEF-processed food products are measured using inductively coupled plasma - atomic emission spectrometry (ICP-AES). The first objective of the work is to rank the materials by taking into account both the price and the life-time of each material. This ranking is carried out for a variety of food products having different values of pH and electrical conductivity. Both the rate and price of replacing the electrodes should be considered when assessing the economic feasibility of the PEF process. By investigating the physical and chemical properties of the electrode materials used, the second objective of the work aims to understand more about the mechanism by which the metallic ions are released. Under the extreme conditions of voltage and current applied during typical PEF processing, the classical electrochemical theories may not be sufficient. Understanding more about the mechanism may provide a new basis for better selection of electrode materials.
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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 | 0.000 |
| 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 | 0.001 | 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 it