Fruit of Mandacaru: Kinetics of Drying and Physical-Chemical Characterization
Bibliographic record
Abstract
The drying of the mandacaru fruit consists in an alternative way to reduce loss during its harvest and therefore minimizes its waste. The objective of this study was to physicochemically characterize the mandacaru fruit both fresh and dehydrated in an air circulation oven using three drying temperatures (40, 50 and 60 ºC), related to the water content, water activity, pH, acidity and phenolic compounds. Three empirical mathematical models were used to describe the drying (Henderson and Pabis, Midilli and Page). The Midilli’s model had the best statistical indicators, fitting better to the experimental data. The fresh fruit showed water content of 82.75%, water activity of 0.986, titratable acidity of 0.18%, pH of 4.35 and phenolic compounds of 28.35 mg.100-1g. The parameters luminosity (L), intensity of red (+a), yellow intensity (+b), hue angle (hº) and chromaticity (C*) were respectively equal to 59.84±0.08; 5.72±0.06; 89.25±0.08; 5.72±0.06. The physicochemical compounds of the fruit were influenced by the drying, in which water content was 10.83, 8.31 and 7.56%, and the water activity was 3.99. 3.24 and 2.40 for the temperatures of 40, 50 and 60 ºC, respectively. The highest temperature caused a greater removal of water in the product along the drying. The dried mandacaru pulp showed titratable acidity of 0.41, 4.86 and 4.98%, pH of 4.76, 4.86 and 4.98 and phenolic compounds of 252.26, 156.53 and 196.10 mg.100-1g, and the parameters L (19.72, 23.41 and 26.15), a* (3.13, 1.31 and 1.30), b* (7.31, 6.53 and 9.14), H* (66.79, 78.69 and 81.92) and C* (7.95, 6.95 and 9.23) at the temperatures of 40, 50 and 60 ºC, respectively. The results of the characterization suggest the technological exploitation of the dried mandacaru fruit to add nutritional value to other foods or in the development of new products.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".