Postharvest Treatments of Hass Avocado (<i>Persea americana</i> Mill.) and Estimation of Its Quality Using Hyperspectral Imaging (HSI)
Bibliographic record
Abstract
Avocados’ shelf life is limited and difficult to monitor. This study evaluated the performance of chitosan coatings (1.5 and 2% w/v, T 1 and T 2 ) on avocados’ quality and shelf life against samples untreated (C) and treated with an ethylene inhibitor (1-MCP, M). Hyperspectral imaging (HSI) coupled with machine learning (ML) techniques was also evaluated to estimate Hass avocados’ quality indicators. Sensorial, physicochemical, and metabolic characteristics were measured using standard procedures. While T 2 samples exhibited undesirable changes (i.e., uneven color and heterogeneous firmness), T 1 behaved similarly to C. However, neither treatment could delay senescence as much as 1-MCP (42 vs ≤ 33 days). In general, Bayesian regularization neural networks (BRNNs) outperformed the other tested ML techniques in estimating quality attributes from HSI features, allowing for real-time nondestructive assessment of food quality. Adverse effects of chitosan coatings on avocados’ physiology were identified, which can inform the development of films with improved performance.
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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 | 0.000 | 0.000 |
| 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 | 0.001 |
| 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".