A comparative study of RSM and ANN models for predicting spray drying conditions for encapsulation of <i>Lactobacillus casei</i>
Why this work is in the frame
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Bibliographic record
Abstract
Abstract Background and Objectives The aim of this study was to develop a wall material using pea protein isolate and pectin to optimize the encapsulation of Lactobacillus casei by spray drying. Response surface methodology (RSM) and artificial neural network (ANN) were used to analyze the effect of processing parameters. Findings The results showed that both RSM and ANN could be used to successfully characterize the experimental data, although ANN demonstrated greater predictive accuracy than RSM due to a higher R 2 and lower mean square error (MSE). Conclusion ANN was observed to show more suitability than RSM. The encapsulation efficiency (90.7%), yield (45.5%), and wettability (169 s) of spray‐dried probiotic powder obtained under optimal spray drying conditions (inlet air temperature (132°C); feed flow rate (9.5 mL/min) and pea protein isolate concentration (7.1%)) were observed to be not significantly different ( p < .05) from predicted values for all three parameters, demonstrating the validity of applied model. Significance and Novelty In this study, production technology of vegan base probiotic powder has been developed using mathematical modeling through the spray‐drying method. Therefore, this data can be useful for food processing industries to develop a high‐quality probiotic powder through spray drying.
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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 it