The human-based multi-sensor fusion method for artificial nose and tongue sensor data
Bibliographic record
Abstract
Presently, an increased interest is apparent for the development of integrated human-like smell and taste sensing capabilities, e.g. for chemical, paper pulp, food, and medicine applications. This paper will present an original sensor fusion method based on human expert opinions about smell and taste and measurement data from artificial nose and taste sensors. The "electronic nose" consists of an array of gas sensors with different selectivity patterns, signal handling and a sensor signal pattern recognition and decision strategy. The "electronic tongue" which was developed for the taste analysis of liquids is based on pulse voltammetry. Measurement data from the artificial smell and taste sensors are used to produce sensor-specific opinions about these two human-like sensing modalities. This is achieved by a team of artificial neural networks and conventional signal handling which approximates a Bayesian decision strategy for classifying the sensor information. Further, a fusion algorithm based on the maximum likelihood principle provides a combination of the smell and respectively taste opinions, into an overall integrated opinion similar to human beings. The proposed integrated smell- and taste-sensing method is then illustrated by an application of real world measurements in the food industry.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| 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".