Microbial enzymes and major applications in the food industry: a concise review
Bibliographic record
Abstract
Abstract The use of enzymes in the production of food products is an ancient practice. Microbes provide several enzymes that are involved in improving the taste, texture, as well as aroma of food items, offering several benefits to the food industry. Subsequently, the ease of availability of these microbial enzymes has increased their utilization in the food industry. This cost-effectiveness and ease of commercial-scale production make enzymes ideal tools for various industrial uses. Microbial enzymes are utilized in processing food products such as those associated with the brewery, dairy and bakery industries. In addition, the nutritional value, color, aroma and texture of food products can be improved by using microbial enzymes. With the progress in technology, several novel enzymes in various applications of the food and beverages industry have been developed and demand is constantly increasing. The present review provides a comparative narrative of the applications of some of the predominating enzymes, such as phytases, lipases, lactases, pectinases, and laccases, commonly used as processing aids in the food industry. Graphical Abstract
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".