Structured and Specialty Lipids in Continuous Packed Column Reactors: Comparison of Production Using One and Two Enzyme Beds
Bibliographic record
Abstract
Abstract In this study, speciality lipids based on fish oil (FO) and capric acid (CA) were produced in packed bed bioreactors using immobilized Lipozyme IM from Rhizomucor miehei in a solvent‐free environment. Our goal was to compare the product quality and yield among reactors consisting of one or two enzyme columns. Response surface methodology (RSM) was used to optimize process variables for maximum incorporation (Inc) of CA for each reactor configuration. The studied process variables were substrate molar ratio (1:1–3:1 CA/FO), temperature (35–55 °C) and flow rate (0.5–1.5 mL/min). All experiments were conducted based on a face‐centered cube design. The maximum predicted Inc of CA into FO (31.7 mol%) using one column was obtained when substrate molar ratio, temperature and flow rate of substrates were 2.70:1 (CA/FO), 55 °C and 0.5 mL/min. The corresponding optimal Inc of CA into FO (22.7 mol%) using two columns was predicted at 2.95:1 (CA/FO), 55 °C and 0.86 mL/min. Analysis of variance (ANOVA) showed that Inc of CA into the one‐bed design was significantly influenced by all experimental conditions, with substrate molar ratio having the greatest impact. In the two‐bed design, only temperature and flow rate had an effect; the molar ratio of substrates was not significant. Coefficients of determination were low for both designs, indicating a poor fit of our data to the model. However, the main purpose of this study was to assess the effect of process factors on Inc of CA into FO, rather than generate a model useful for prediction of responses under conditions not examined with this design. Therefore, the significant ANOVA results are much more important, stressing the real relationship between parameters and response, than the low coefficients of determination.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| 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 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".