Comparative analysis of different measurement techniques for assessing porous structure of food products dehydrated by several technologies
Bibliographic record
Abstract
Several techniques are used to characterize the porous structure of dried foods, including helium pycnometry, mercury intrusion porosimetry (MIP), X-ray microtomography (micro-CT), and scanning electron microscopy. The strengths and weaknesses of each technique are critical to consider when selecting the appropriate one. Each method has strengths and limitations, yet discrepancies in measurements remain poorly understood. This study aimed to investigate the agreement and differences among these techniques in characterizing dried apples and pears dehydrated using hot air-drying (HAD), blast freeze-drying (BFD), and liquid nitrogen freeze-drying (FDN). The bulk density of freeze-dried apples and pears was approximately 0.14 and 0.18 g/ml, respectively, while HAD apples and pears averaged 0.54 g/ml and 1.12–1.29 g/ml. Particle densities measured by MIP and pycnometry were similar, but micro-CT produced different values. For HAD apples, porosity values were 62.3% (pycnometry), 59.6% (MIP), and 54.6% (micro-CT). For FDN pears, the values were 83.5%, 83.3%, and 70.9%, respectively, while for BFD pears, they were 86.1%, 86.0%, and 72.1%. The discrepancies in micro-CT results may stem from resolution limitations or image processing techniques. The findings of this study suggest that care should be taken when selecting and applying micro-CT to characterize dried food porous microstructures. To ensure the comprehensive characterization of dried food porosity, micro-CT should be combined with pycnometry or MIP. This combination provides a more accurate quantification of pore volume and size and a deeper and more reliable understanding of porous structures, leading to improved food quality, efficiency in production, and innovation in food processing technologies.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".