Freshness in Salmon by Hand-Held Devices: Methods in Feature Selection and Data Fusion for Spectroscopy
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
High Resolution Image Download MS PowerPoint Slide Salmon fillet was analyzed via hand-held optical devices: fluorescence (@340 nm) and absorption spectroscopy across the visible and near-infrared (NIR) range (400–1900 nm). Spectroscopic measurements were benchmarked with nucleotide assays and potentiometry in an exploratory set of experiments over 11 days, with changes to spectral profiles noted. A second enlarged spectroscopic data set, over a 17 day period, was then acquired, and fillet freshness was classified ±1 day via four machine learning (ML) algorithms: linear discriminant analysis, Gaussian naïve, weighted K -nearest neighbors, and an ensemble bagged tree method. Dual-mode data fusion returned almost perfect accuracies (mean = 99.5 ± 0.51%), while single-mode ML analyses (fluorescence, visible absorbance, and NIR absorbance) returned lower mean accuracies at greater spread (77.1 ± 10.1%). Single-mode fluorescence accuracy was especially poor; however, via principal component analysis, we found that a truncated fluorescence data set of four variables (wavelengths) could predict “fresh” and “spoilt” salmon fillet based on a subtle peak redshift as the fillet aged, albeit marginally short of statistical significance (95% confidence ellipse). Thus, whether by feature selection of one spectral data set, or the combination of multiple data sets through different modes, this study lays the foundation for better determination of fish freshness within the context of rapid spectroscopic analyses.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.007 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 it