Evaluation of Physicochemical, Nutritional and Sensory Quality Characteristics of New Papaya Hybrids Fruits Developed in JKUAT
Bibliographic record
Abstract
The world is faced with a lot of challenges including lack of sustainable development and inability to feed its growing population leading to malnutrition. The issue of accessing high quality nutritious foods such as fruits has become a major challenge for many African people. Papaya (Carica papaya L.) is among the most popular fruits grown in Kenya and excellent in nutritional content. However, its potential has remained unexploited due to inadequate quality planting materials, high post- harvest losses and prevalence of pests and diseases. Researchers at Jomo Kenyatta University of Agriculture and Technology developed promising papaya hybrids whose physicochemical, nutritional content and sensory characteristics have not been evaluated. Thus, the aim of this study was to determine the physicochemical, nutritional content and sensory quality characteristics of the new papaya hybrids and their control, Sunrise Solo. The physicochemical attributes evaluated included; total soluble solids, total titratable acid, pH and total soluble solid/total titrable acid ratio. While the nutritional content evaluated included (Vitamin C and β-carotene content) and sensory quality characteristics were evaluated. The standard AOAC methods was used to determine nutritional content and an effective analysis with 9- point hedonic scale was used on sensory evaluation. There was significant difference (P< 0.05) in nutritional content of new hybrids papaya fruits and Sunrise solo. The maximum and minimum Vitamin C content of 131.63 mg/100g and 52mg/100g were exhibited by line 6 and 8 respectively. β-carotene content ranged between 1.69 and 3.39 mg/100g as exhibited by line 1 and lines 2 and 8 respectively. The findings of this study revealed that the nutritional content of the new papaya hybrids exceeded the one of Sunrise Solo while their sensory quality characteristics compared favourably to the one of Sunrise Solo. Based on these findings, lines 1, 2,5,6,7 can be recommended for commercialization.
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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".