A Low Cost Compact Measurement System Constructed Using a Smart Electrochemical Sensor for the Real-Time Discrimination of Fruit Ripening
Bibliographic record
Abstract
Ethylene as an indicator for evaluating fruit ripening can be measured by very sensitive electrochemical gas sensors based on a high-resolution current produced by a bias potential applied to the electrodes. For this purpose, a measurement system for monitoring ethylene gas concentrations to evaluate fruit ripening by using the electrochemical ethylene sensor was successfully developed. Before the electrochemical ethylene sensor was used to measure the ethylene gas concentrations released from fruits, a calibration curve was established by the standard ethylene gases at concentrations of 2.99 ppm, 4.99 ppm, 8.01 ppm and 10 ppm, respectively, with a flow rate of 0.4 L·min(-1). From the calibration curve, the linear relationship between the responses and concentrations of ethylene gas was obtained in the range of 0-10 ppm with the correlation coefficient R² of 0.9976. The micropump and a novel signal conditioning circuit were implemented in this measurement, resulting in a rapid response in detecting ethylene concentrations down to 0.1 ppm in air and in under 50 s. In this experiment, three kinds of fruits-apples, pears and kiwifruits-were studied at a low concentration (under 0.8 ppm) of trace ethylene content in the air exhaled by fruits. The experimental results showed that a low cost, compact measurement system constructed by using an electrochemical ethylene sensor has a high sensitivity of 0.3907 V·ppm(-1) with a theoretical detection limit of 0.413 ppm, and is non-invasive and highly portable.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".