Virtualization of foods: applications and perspectives toward optimizing food systems
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Food production cannot be decoupled from human and planetary wellbeing. Meeting safety, nutritional, sensorial, and even price requirements entails applying an integral view of food products and their manufacturing and distribution processes. Virtualization of food commodities and products, i.e., their digital representation, offers opportunities to study, simulate, and predict the contributions of internal (e.g., composition and structure) and external factors (e.g., processing conditions) to food quality, safety, stability, and sustainability. Building virtual versions of foods requires a holistic supporting framework composed of instrumental and computational techniques. The development of virtual foods has been bolstered by advanced tools for collecting data, informing and validating modelling, e.g., micro-computed tomography, to accurately assess native food structures, multi-omics approaches, to acquire vast information on composition and biochemical processes, and nondestructive and real-time sensing, to facilitate mapping and tracking changes in food quality and safety in real-world situations. Comprehensive modeling techniques (including heat and mass transfer, thermodynamics, kinetics) built upon physic laws provide the base for realistic simulations and predictions of food processes that a virtual food might undergo. Despite the potential gaps in knowledge, increasing the adoption of food virtualization (data-based, physics-based or hybrid) in manufacturing and food systems evaluation can facilitate the optimal use of resources, the rational design of functional characteristics, and even inform the customization of composition and structural components for better product development. This mini-review focuses on critical steps for developing and applying virtual foods, their future trends, and needs.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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 it