Optimization of Sensor Array for Detection of Abalone Freshness Based on Electronic Tongue
Bibliographic record
Abstract
Considering the shortcomings of traditional detection methods of abalone freshness, such as cumbersome operation and low accuracy, this study established a rapid detection method of abalone freshness by using an electronic tongue.A sensor array consisting of eight inert metal electrodes (Au, Pd, Pt, Ag, Ti, Al, Ni, W) was used for detection.However, the detection data of redundant sensors will affect the efficiency and accuracy of detection, so the sensor array needs to be optimized.The original sensor array consisting of eight inert metal electrodes was used to detect four kinds of abalone meat (refrigerated at 4℃ for 1 day, 4 days, 7 days and 10 days) with different freshness.The detection data were analyzed by one-way analysis of variance.According to the analysis results, Al and Pt electrodes with poor stability and differentiation were eliminated.Multiple comparative analysis of variance was used to conduct a significant analysis of the data of the remaining six electrodes.According to the significant difference, the electrodes without significant difference were divided into different groups to obtain four combinations.By combining principal component analysis and support vector classification, the performance of the original array composed of eight electrodes and the four groups of sensor arrays obtained after grouping were analyzed, and the most suitable sensor array for abalone freshness detection was selected.The results showed that the group III sensor array composed of Ag, Pd and Ti electrodes had the best effect on the freshness identification of abalone.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".