Lactic Acid Bacteria and the Food Industry - A Comprehensive Review
Bibliographic record
Abstract
Recently, more people are showing interest in knowing what the packaged food they buy contains, and not just that, they want to also know the different processes those foods went through, at least to a certain extent. This rising interest can be traced to the rising need for special diets and nutritional requests of certain groups of people who wants to either just live a healthier lifestyle or are made to turn to healthy eating due to health challenges occasioned by illnesses. The importance of the lactic acid bacteria to the food industry cannot be over-emphasized and as such, more awareness about it should be created for informational purpose. Although there are numerous literature on lactic acid bacteria online, mostly dealing with their identification and purification, there is a gap in getting these technical information together in a form that can be understood by anyone who wishes to learn more about lactic acid bacteria. This review article explores the numerous benefits of lactic acid bacteria in the food industry, highlighting its key uses for fermentation and preservation. More so, this review pinpoints the health benefits of certain strains of lactic acid bacteria in form of probiotics in addition to its well-known antimicrobial properties. Lactic acid bacteria confers numerous beneficial characteristics to foods ranging from enhanced taste to provision of healthy probiotics. Lactic acid bacteria may also experience a decrease in viability and functionality when stored while controlling and optimizing their metabolic activity is another major concern in the food industry. Key words: Lactic acid bacteria, probiotics, fermentation, food preservation, antimicrobial, starter cultures.
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 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 | 0.007 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 teacher head, 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".