Advancement and Innovations in Drying of Biopharmaceuticals, Nutraceuticals, and Functional Foods
Bibliographic record
Abstract
Drying is a crucial unit operation within the functional foods and biopharmaceutical industries, acting as a fundamental preservation technique and a mechanism to maintain these products' bioactive components and nutritional values. The heat-sensitive bioactive components, which carry critical quality attributes, necessitate a meticulous selection of drying methods and conditions backed by robust research. In this review, we investigate challenges associated with drying these heat-sensitive materials and examine the impact of various drying methods. Our thorough research extensively covers ten notable drying methods: heat pump drying, freeze-drying, spray drying, vacuum drying, fluidized bed drying, superheated steam drying, infrared drying, microwave drying, osmotic drying, vacuum drying, and supercritical fluid drying. Each method is tailored to address the requirements of specific functional foods and biopharmaceuticals and provides a comprehensive account of each technique's inherent advantages and potential limitations. Further, the review ventures into the exploration of combined hybrid drying techniques and smart drying technologies with industry 4.0 tools such as automation, AI, machine learning, IoT, and cyber-physical systems. These innovative methods are designed to enhance product performance and elevate the quality of the final product in the drying of functional foods and biopharmaceuticals. Through a thorough survey of the drying landscape, this review illuminates the intricacies of these operations and underscores their pivotal role in functional foods and biopharmaceutical production.
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.
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 | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 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".