Microencapsulation Quality and Efficiency of Lactobacillus casei by Spray Drying Using Maltodextrin and Vegetable Extracts
Bibliographic record
Abstract
<p>Survival and quality efficiency of <em>Lactobacillus casei </em>microencapsulated by spray drying using different vegetable extracts (asparagus, artichoke, orange or grapefruit peel) were evaluated. Aqueous suspensions of the vegetable extracts with or without maltodextrin (adjusting to 25% w/w) were prepared for the microencapsulation of <em>L. casei</em>. The evaluated spray drying conditions were at a fixed air inlet temperature (Tin) of 145 °C and varying the aqueous suspensions flux (Q) of 10 or 15 g/min. Survival of <em>L. casei</em> was evaluated after the spray drying process and after 60 days of storage at 25 °C. The quality efficiency of the microencapsulated <em>L. casei</em> was evaluated by measuring in the product, physicochemical properties (moisture content, a<sub>w</sub>), determining moisture gain and modeling adsorption isotherms, besides analyzing micrographs. Results demonstrated that moisture content of the different spray drying powders was less than 2% wb and less than 0.30 of a<sub>w</sub>. It was evidently that the use of maltodextrin reduced 50% the powders moisture gain (hygroscopicity) therefore reducing stickiness problems during storage. The Scanning Electron Microscopy (SEM) confirmed individual particles formation with a homogeneous coat when using vegetable extracts+maltodextrin and hence better powder quality than without it. The microbial reduction of <em>L. casei</em> after the spray drying process was of one log cycle and significantly different (p &lt; 0.05) with the presence of maltodextrin when using orange or grapefruit peel. A microbial population over 10<sup>7</sup> cfu/g of <em>L. casei</em> microencapsulated was maintained after 60 days of storage which guarantees its use to develop functional food.</p>
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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 | 0.000 |
| 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 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".