A novel route for identifying starch diagenetic products in the archaeological record
Bibliographic record
Abstract
This work introduces a novel analytical chemistry method potentially applicable to the study of archaeological starch residues. The investigation involved the laboratory synthesis of model Maillard reaction mixtures and their analysis through Fourier-Transform Ion Cyclotron Resonance Mass Spectrometry (FTICR-MS). Thus, starch from sixteen plant species were matured while reacting it with the amino acid glycine. The FTICR-MS analysis revealed > 5,300 molecular compounds, with numerous unique heteroatom rich compound classes, ranging from 20 (Zea mays) to 50 (Sorghum bicolor). These classes were investigated as repositories of chemical structure retaining source and process-specific character, linked back to botanical provenance. We discussed the Maillard reaction products thus generated, a possible pathway for the preservation of degraded starch, while also assessing diagenetic recalcitrance and adsorption potential to mineral surfaces. In some cases, hydrothermal experimentation on starches without glycine reveals that the chemical complexity of the starch itself is sufficient to produce some Maillard reaction products. The article concludes that FTICR-MS offers a new analytical window to characterize starchy residue and its diagenetic products, and is able to recognize taxonomic signals with the potential to persist in fossil contexts.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".