Luminescence-based techniques to monitor pre-, post-harvest and processing changes in fresh produce and derivatives: Principles, applications and trends
Bibliographic record
Abstract
Background Maintaining and ensuring food safety and quality of fresh or minimally processed produce is a priority for producers, distributors, suppliers and consumers. The transient nature and dynamism of the produce supply chain and the high perishability of the products require rapid, cost-effective and noninvasive methods for monitoring food integrity (quality, safety and identity) along the supply chain. Scope and approach This review covers luminescence-based methods that target intrinsic or extrinsic fluorophores as reporters of food safety, freshness, and authenticity and the requirements for assessment of these attributes in produce at pre- and post-harvest stages. Key findings Steady-state fluorescence spectroscopy, excitation-emission matrices (EEMs), and hyperspectral imaging in fluorescence mode (HSI) offer in-line capabilities due to advances in data acquisition and processing. This includes relevant feature extraction, classification, and modeling using machine learning and mechanistic approaches. Advances in data integration, wireless networks and cloud-based computing can enhance the applicability of these methods to monitor and inform continuous and effective decision-making along the supply chain and assist in providing feedback to virtual objects/systems such as digital twins. The use of spatially resolved techniques other than HSI, such as Fluorescence Lifetime Imaging Microscopy (FLIM) or super-resolution microscopy (SRM), although underexplored in foods, offers a powerful option to explore mechanisms that affect food integrity in fresh produce at pre- and post-harvest stages. Significance and conclusions Luminescence-based methods can significantly improve supply chain transparency and inform advanced supply chain modeling by providing quick, low-cost, in-line capabilities to identify and mitigate future safety and quality breaches.
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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 | 0.001 |
| 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 | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".