Optimizing the fermentation conditions of fermented goji using sensory analysis and the biomass of <i>Lactiplantibacillus plantarum</i> <scp>NCU137</scp>
Bibliographic record
Abstract
The main objective of this study was to optimize the fermentation conditions of goji and to analyze the sugar, organic acid, amino acid, and flavor compounds before and after the fermentation of goji. Based on the single-factor experiment, orthogonal array design and the range analysis, the optimized conditions for fermented goji were 30% raw material concentration, 28 h fermentation time and 0.05% inoculum size, respectively. Under optimal conditions, the biomass of Lactiplantibacillus plantarum NCU137 and sensory score of fermented goji reached to 8.84 ± 0.05 log CFU/g and 81.3 ± 1.5, respectively. After fermentation, the content of malate, succinic acid, and lactate increased, while the content of pyruvate, glucose, fructose, and the overall free amino acid decreased. After fermentation, volatile components except for the phenols and acids are declining. This study would provide theoretical basis and experimental data for the industrialization of Lycium barbarum fermentation. Novelty impact statement After Lactiplantibacillus plantarum NCU137 fermentation, the contents of organic acids and total acidity acid in fermented goji significantly increased, which could inhibit microbiological spoilage and growth. Moreover, we get a delicious fermented goji juice through the optimization of fermentation conditions. Therefore, the data obtained in this study provides a better understanding of the effects of raw material content, fermentation time, and inoculum size on the quality of fermented goji, which would be very beneficial for the production of high-quality fermented goji for subsequent consumption.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".