Multi-Modal Sensing Platform for Continuous Analysis of Maple Syrup in Production Process
Bibliographic record
Abstract
In this work, we propose a new multimodal method/platform for continuous maple syrup °Brix monitoring and color grading during the production process. It is based on different detection methods such as electrical impedance, electrochemical sensing and optical sensing. First, using electrochemistry sensing, the results of maximum detected current in obtained Voltammogram with cyclic-voltammetry (CV) analysis and generated currents for chronoamperometry experiments presents a high standard deviation higher than 50%. In addition, we report the impact of the temperatures on previously mentioned sensing techniques. We have observed that electrochemistral sensor with commercial electrodes in our experimental conditions did not provide reliable measurement for maple syrup industrial process for °Brix monitoring. When using electrical impedance sensing method, a polynomial fitting relationship was established between the electrical impedance and °Brix with a high fitting index (R2) of 0.895. Furthermore, an impedance offset must be considered when temperature is changing. Also, an optical sensor was used to detect the maple syrup grade. As the grade depends on the light transmission percentage through a known thickness of solution, a photodiode detector and a LED were used as grade sensor. Many LEDs with different wavelengths (green, yellow, red, blue and infrared) were tested on different maple syrup grades. Obtained results show that green LED is the most suitable one for maple syrup grade detection which can lead to a linear fit with high fitting index (R2) of 0.963 between voltage response of the photodiode detector and the light transmission which is converted to a maple syrup grade.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".