Comparative metabolomics reveals differences in primary and secondary metabolites between “Shixia” and “Chuliang” longan (<i>Dimocarpus longan</i> Lour.) pulp
Bibliographic record
Abstract
Abstract Longan was a characteristic fruit for both medicine and food in China, which was rich in primary and secondary metabolites. Comprehensive high‐throughput identification and comparison of metabolites in longan pulp among different varieties were still lacked. “Shixia” (SX) and “Chuliang” (CL) were the biggest major cultivars of longan in China. In this study, the content of total soluble solid, total flavonoid, and total phenolics indicated the difference of sweetness and bioactive compound content between the SX and CL pulp. Through a widely targeted metabolome, a total of 514 metabolites were identified and categorized into 23 groups mainly including flavonoids, amino acids & derivatives, lipids, phenolic acids, nucleotides & derivatives, alkaloids, organic acids and sugars & derivatives. A total of 89 metabolites with significantly differential accumulation (variable importance in projection (VIP) value ≧1, p ‐value <.05) over 1.2 fold were found between SX and CL, which were mainly enriched into pathways including flavone and flavonol biosynthesis, glycolysis/gluconeogenesis, and arginine and proline metabolism. Higher leveled hexose and hexose‐phosphate (i.e., β‐D‐glucose, D(+)‐glucose, glucose‐1‐phosphate and glucose‐6‐phosphate), dominant organic acids (i.e., citric acid, succinic acid, D‐malic acid, and citramalate), and essential amino acids (L‐threonine, L‐valine, L‐isoleucine, L‐leucine, L‐phenylalanine and L‐lysine) in SX pulp might be contributed to the taste and flavor difference between SX and CL. Moreover, the greatly differential accumulated secondary metabolites especially flavonoids and phenolic acids might result in different medicinal and nutritional characteristic between SX and CL. In conclusion, this study provided a systemic metabolic basis for understanding the nutritional differences between SX and CL and would help deepen the molecular biology and pharmacology research on characteristic metabolites in longan pulp.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| 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".