Osmotic membrane distillation for retention of antioxidant potential in Nagpur mandarin (<i>Citrus reticulata</i> Blanco) fruit juice concentrate
Bibliographic record
Abstract
Abstract Consumers desire for processed fruit juices which are of higher quality, nutritionally rich, and convenient to prepare and store. Membranes have found many applications throughout the food processing industry. The present study describes the potential use of osmotic distillation process for concentrating Nagpur mandarin (Citrus reticulata Blanco) juice for the first time and to study its impact on ascorbic acid content and antioxidant activity assessed by ABTS, DPPH, FRAP, and TPC assays. The flux and concentration factor was also determined to evaluate the performance of the OD process. The juice clarified by the ultrafiltration technique having a TSS of 9.0°Brix was concentrated up to 60.4°Brix. The ascorbic acid content (15.5 mg/100 mL) and antioxidant activity by ABTS (9.69 mmolL−1 Trolox), DPPH (2.77 mmolL−1 Trolox), FRAP (1.33 mmolL−1 Trolox), and TPC content (13 mg GAE L−1) was retained, thus suggesting the use of membrane technology for concentrating fruit juice providing an alternative to the traditionally employed thermal evaporation processes. Practical applications This work offers an alternative to the thermal evaporation method for concentrating Nagpur mandarin juice with less energy consumption, low equipment cost, and less damage to the product and retention of antioxidant compounds, for example, ascorbic acid, which plays an important role in human health. The Nagpur mandarin juice was transformed to concentrate with the osmotic membrane distillation (OD) technique. The concentrate so produced is significantly potential to the food processing industry with retained nutritional qualities, and is suitable for many purposes due to its reduced weight and volume, less handling and storage costs and reduced in water activity with enhancement of product stability. Production of Nagpur mandarin juice concentrate also broadens its potential applications in food industry and its good quality renders it easily acceptable to consumers.
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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) | 0.001 | 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".