Application of atomic force microscopy in the characterization of fruits and vegetables and associated substances toward improvement in quality, preservation, and processing: nanoscale structure and mechanics perspectives
Bibliographic record
Abstract
Fruits and vegetables are essential horticultural crops for humans. The quality of fruits and vegetables is critical in determining their nutritional value and edibility, which are decisive to their commercial value. Besides, it is also important to understand the changes in key substances involved in the preservation and processing of fruits and vegetables. Atomic force microscopy (AFM), a powerful technique for investigating biological surfaces, has been widely used to characterize the quality of fruits and vegetables and the substances involved in their preservation and processing from the perspective of nanoscale structure and mechanics. This review summarizes the applications of AFM to investigate the texture, appearance, and nutrients of fruits and vegetables based on structural imaging and force measurements. Additionally, the review highlights the application of AFM in characterizing the morphological and mechanical properties of nanomaterials involved in preserving and processing fruits and vegetables, including films and coatings for preservation, bioactive compounds for processing purposes, nanofiltration membrane for concentration, and nanoencapsulation for delivery of bioactive compounds. Furthermore, the strengths and weaknesses of AFM for characterizing the quality of fruits and vegetables and the substances involved in their preservation and processing are examined, followed by a discussion on the prospects of AFM in this field.
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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 | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 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".