Valorization of Fruits Residues in Saba senegalensis (A. DC. Pichon) Juice Processing: Biochemical and Sensory Analysis of the Product
Bibliographic record
Abstract
During the small-scale processing of fruit juice from Saba senegalensis, the pericarp, seed and seed coat are considered waste and discarded with environmental pollution involvement. Yet, these agricultural by-products or residues often display obvious exploitable nutritional potentials as minerals and antioxidants. This study aims to contribute to the use of fruit residues from S. senegalensis into the processing products, to enhance the value of this agricultural resource and improve its profitability. After incorporating 2%, 5% and 10% (w/v) of ground dried residues, the S. senegalensis juice was filtered, pasteurized and biochemical and sensory traits assessed compared to the raw juice taken as a control. Results showed that the juice becomes thicker (1.01 to 1.05) and viscous (1.85 to 3.85 mPa.s) and its dry matter and ash contents get increasing (3.92 to 5.43% and 0.08 to 0.22%, respectively) with the incorporation of residues up to 10%. The resulting juices display pH of 2.33 to 2.5, which is friendly against nutrients spoilage by fermentation. The presence of carbohydrates and polyphenols also increases (0.91 to 1.69 g/100 mL and 68.81 to 227.14 mg/100 mL, respectively) with the residue’s incorporation into juices. However, the organoleptic traits of the formulations seem more advantageous for raw juice without residue, especially with a greater trend for hedonic acceptance. Incorporating Saba senegalensis fruit’s residues strengthened the nutritional virtues of processed juices and the use of conventional additives such as table sugar and flavorings could also improve the organoleptic ratings.
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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) | 0.001 | 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 | 0.001 |
| 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".