Food surface characterization by scanning electron microscopy and fractal analysis: A review
Bibliographic record
Abstract
Surface characterization of food products is critical for understanding their physical, chemical, and biological properties, which have a direct impact on quality, safety, and consumer acceptability. Scanning electron microscopy (SEM) paired with "fractal analysis" has developed as an effective method for studying food surfaces at the micro- and nanoscales. This review investigated the use of SEM to examine the topographical features of various food items, such as fruits, vegetables, grains, and processed foods, emphasizing the importance of surface morphology in determining texture, moisture retention, and overall product quality during consumption and storage. Fractal analysis, together with SEM, is a quantitative approach for describing the complexity of foods surface structures. Fractal and multifractal analysis using SEM images yields a numerical description of surface roughness, complexity, and heterogenicity. The combination of SEM with fractal analysis not only allows for a more in-depth study of food surface characteristics, but it also helps to optimize food processing procedures. Positive correlations between processing (drying, frying, freezing) time, fractal dimension, and surface openings have been established. Fractal dimension of various food items (fruit, vegetable, crispy product, powder, extrudate product, puffed food, frozen product, fish, etc.) and their characteristics quality attributes have been found expressively interlined. The outcomes of multifractal analysis (Singularity and Rényi spectra) successfully characterized heterogenicity in foods surface micro-structure. SEM image-based fractal analysis, which reveals the subtle links between surface morphology and food attributes, showed potential to significantly expand food science and technology, paving the door for advances in food processing and quality management.
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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) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".