Soft Sensor for Snack Food Textural Properties Using On-Line Vibrational Measurements
Bibliographic record
Abstract
It is common in the food industry to produce solid-phase products that are not amenable to ready characterization on the production line via traditional on-line instrumentation. This motivates research into novel sensor technologies. In this work, an on-line vibrational sensor is used to develop a soft sensor for real-time prediction of product texture in a commercial snack food process. This in turn enables real-time multivariate statistical process control (MSPC) and indicates the opportunity for automated feedback control. An accelerometer is used to record the vibrational signature generated by a snack food product falling onto a metal surface. The frequency distribution of the acoustic signature is obtained via the discrete Fourier transform. This provides a predictor space from which the textural properties are modeled using partial least-squares (PLS). Excellent results are obtained, with R 2 upward of 96% on an independent test set for two properties that fully span the texture space of this product.
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How this classification was reachedexpand
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".