<i>Hornification</i> : Lessons learned from the wood industry for attenuating this phenomenon in plant‐based dietary fibers from food wastes
Bibliographic record
Abstract
A significant amount of waste is annually generated worldwide by the supply chain of the food industry. Considering the population growth, the environmental concerns, and the economic opportunities, waste recovery is a promising solution to produce valuable and innovative ingredients for food and nonfood industries. Indeed, plant-based wastes are rich in dietary fibers (DF), which have relevant technical functionalities such as water/oil holding capacity, swelling capacity, viscosity, texture, and physiological properties such as antioxidant activity, cholesterol, and glucose adsorption capacities. Different drying technologies could be applied to extend the shelf life of fresh DF. However, inappropriate drying technologies or process conditions could adversely affect the functionalities of DF via the hornification phenomenon. Hornification is related to the formation of irreversible hydrogen bindings, van der Waals interactions, and covalent lactone bridges between cellulose fibrils during drying. This review aims to capitalize on the knowledge developed in the wood industry to tackle the hornification phenomenon occurring in the food industry. The mechanisms and the parameters affecting hornification as well as the mitigation strategies used in the wood industry that could be successfully applied to foods are summarized. The application of conventional drying technologies such as air or spray-drying increased the occurrence of hornification. In contrast, solvent exchange, supercritical drying, freeze-drying, and spray-freeze-drying approaches were considered effective strategies to limit the consequences of this phenomenon. In addition, incorporating capping agents before drying attenuated the hornification. The knowledge summarized in this review can be used as a basis for process design in the valorization of plant-based wastes and the production of functional DF that present relevant features for the food and packaging industries.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".