Organic acids, sugars, phenolic compounds, and some horticultural characteristics of black and white mulberry accessions from Eastern Anatolia
Bibliographic record
Abstract
Mulberries, Morus spp., grow in different growing regions of Turkey with a high diversity providing an opportunity to select superior accessions. The goal of the study was to determine the differences in phytochemical components and horticultural characteristics between white and black mulberry accessions in the Igdir province. Fruits of black and white mulberries collected from Igdir province were assayed for various horticultural characteristics including berry width, berry length, berry weight, and berry peduncle length. Samples were also assayed for vitamin C, organic acids (citric, tartaric, malic, succinic, and fumaric), sugars (glucose and fructose), phenolic acids (catechin, rutin, quercetin, chlorogenic, ferulic, o-coumaric, p-coumaric, caffeic, syringic, vanillic, and gallic acids), and antioxidant capacity [Trolox equivalent antioxidant capacity (TEAC) assay]. Differences (P < 0.05) were noted between accessions in berry length and berry weight. Malic acid was found to be the predominant organic acid regardless of species. Rutin (for black mulberry) and chlorogenic acid (for white mulberry) were the predominant phenolic acids. Black mulberry had higher glucose and fructose content than white mulberry. Analysis showed that black mulberry had higher content of tartaric acid, malic acid, TEAC, fructose, glucose, catechin, rutin, quercetin, o-coumaric acid, and caffeic acid compared with white mulberry (P < 0.01); however, white mulberry had higher content of succinic acid, vitamin C, chlorogenic acid, ferulic acid, p-coumaric acid, syringic acid, and gallic acid (P < 0.01). Our results provide a reference for further studies of mulberry fruits in detection of organic acids, sugars, antioxidant capacity, and phenolic compounds. Also, it is clear that we need standardized testing, extraction, and analysis protocols to compare genotypes grown in different countries.
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How this classification was reachedexpand
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 | 0.000 | 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 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".