Use of High Performance Liquid Chromatography (HPLC) for the Analysis of Amino Acid of Sulawesi and Local Clone Cocoa Bean Fermentation
Bibliographic record
Abstract
Fermentation is a very vital stage of processing mechanism to ensure the chocolate products have good taste. This study was conducted to obtain an optimal result of chocolate fermentation by determining the concentration and types of amino acid of Sulawesi and local clone cacao beans using HPLC (High Performance Liquid Chromatography) method. A Randomized Completely Design was used in this study. Two types of cocoa clones, Sulawesi clone and Local clone, were divided into 5 groups of treatment: without fermentation (control), fermentation for 3 days, 4 days, 5 days and 6 days. The analysis of amino acids was conducted using HPLC separation method based on the procedure at Marino et al. (2010), Nollet (1996). The measurement of amino acid performed in two phases, liquid hydrolysis, and derivatization proceeded by chromatographic analysis. Condition of HPLC was measured at 37 °C. Mobile phase contains of 60% acetonitril - AccqTag Eluent A, gradient system and the flow rate was 1.0 ml per minute. Fluorescence detector has 250 nm excitation and 395 nm emission. Injecting volume was 5 uL. The results of this study show that cocoa beans of Sulawesi clone with 6-days fermentation has higher products of aspartic acid, glutamic acid, hydrophobic amino acids (Alanine, leucine, proline, valine, isoleucine) and amino acids such as serine, glysine, histidine, treonine and lysine, while local clones of cocoa beans with 3-days fermentation produce more amino acids such as aspartic, glutamic, hydrophobic (isoleucine, leucine, valine) and amino acids such as histidine, threonine, glysine, serine and lysine.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".