Noninvasive, Nondestructive Measurement of Tomato Concentrate Spoilage in Large‐Volume Aseptic Packages
Bibliographic record
Abstract
Low frequency nuclear magnetic resonance (NMR) is used to noninvasively and nondestructively detect spoiled tomato concentrate stored in >200 L metal-lined containers. It is shown that longitudinal and transverse NMR relaxation times change as the tomato concentrate spoils. A rapid, viscosity-dependent spoilage detection method that takes advantage of the inherent inhomogeneity in single-sided NMR instruments is proposed. Here, the effective transverse magnetization decay rate is used as a parameter to determine tomato concentrate spoilage. Three different low frequency, single-sided NMR instruments are described and compared to determine the optimum sensor for spoiled tomato concentrate detection in large-format, metal-lined, aseptic containers. The most effective NMR sensor for this application is temperature stable and has large magnetic field gradients and a homogeneous magnetic field region offset >0.5 cm from the magnet surface. PRACTICAL APPLICATION: This manuscript describes a noninvasive and nondestructive tomato concentrate spoilage detector for application to large-format, sealed, commercial storage bins.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".