Non-destructive spectroscopy-based technologies for meat and meat product discrimination - A review
Bibliographic record
Abstract
Consumers' confidence in products of animal origin is highly subjected to the quality guarantees offered by the manufacturing and retail industries. Traditionally, meat quality evaluation has been conducted through destructive, time-consuming and chemical-dependent protocols. Smart methodologies based on the non-destructiveness and/or non-contact with the samples, such as spectroscopy-based technologies, arise as an alternative promising tool. This comprehensive overview includes literature published in the last decade applying spectroscopy-based techniques in the Visible (Vis) and near-infrared (NIR) regions of the spectrum (Vis-NIR), either individually or combined with imaging (hyperspectral imaging, HSI), to classify meat and meat products based on ante- or postmortem factors. First, a brief introduction to the fundamentals of Vis-NIRS and HSI is included. Secondly, the main applications of Vis-NIRS and HSI technologies for meat qualitative purposes only are discussed. The Vis-NIRS and HSI have been successfully used in lab scale studies (> 90 % overall accuracy) to discriminate meat and meat products according to antemortem (feeding system, species, origin and breed) and postmortem (freshness, meat quality, label claims) factors. Recently, spectral data collected with handheld Vis-NIR equipment have become more frequent, although the use of portable HSI has not been widely explored. From the studies reviewed, the main concern regarding spectral data is to shorten modelling handling times, including strategies to both extract optimal wavelengths from NIR and compress spectral data from HSI. Despite the efforts made to overcome instrumentation and data processing challenges, a gap remains to be covered up to a real-time implementation in industrial line quality control.
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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 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.003 |
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".