Validation and Vitamin C Testing in Crystal Guava (Psidium guajava L.) With Variations of Origin With the HPLC Method (High Performance Liquid Chromatography)
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Crystal guava contains high vitamin C. Vitamin C is contained in different fruits, one of the factors is the altitude of the fruit plant growing areas. This study aims to determine the level of vitamin C in the slurry of crystal guava flesh with variations of origin using the HPLC method. Samples of crystal guava are obtained from the city of Bogor, Malang, and Gunung Kidul. The samples to be analyzed are prepared as a slurry. Qualitative analysis is done by identification using KMnO4 p.a, FeCl3 p.a, AgNO3 p.a as well as comparing the retention time of the sample with vitamin C. Method validation for system suitability, linearity and precision provide results that are eligible according to the applicable regulations. Quantitative analysis using HPLC with C18 stationary phase, water: methanol mobile phase (95: 5) v/v, flow rate of 1 mL/min, and run time of 7.5 minutes. Qualitative analysis by using KMnO4, FeCl3, AgNO3, and retention time (tR) shows positive results of the vitamin C existence. The average levels of vitamin C from Bogor, Malang, and Gunung Kidul, equal to (0.4139 ± 0.004) mg/mL; (0.6746 ± 0.03) mg/mL; and 0.8608 ± 0.002 mg/mL respectively.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 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) | 0.000 | 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 it