Application of mid-infrared spectroscopy for the quantitative and qualitative analysis of organic matter in Holocene sediment records
Bibliographic record
Abstract
The organic matter composition of lake sediments influences important in-lake biogeochemical processes and stores information on environmental changes. Extracting this information is notoriously difficult because of the complexity of the organic matter matrix, which routinely imposes trade-offs between high temporal and analytical detail in the selection of methods of analysis. Here, we demonstrate the potential of diffuse reflectance Fourier transform infrared spectroscopy (DRIFTS) for achieving both of these objectives using untreated bulk samples from two Holocene lake-sediment cores from central Sweden. We develop quantitative models for sediment total organic carbon (TOC) with the same predictive abilities as models based on samples diluted with KBr and qualitatively characterize the organic matter using a spectra processing-pipeline combined with principal component analysis. In the qualitative analysis we identified four organic matter sub-fractions and the interpretation of these is supported and further advanced with molecular data from pyrolysis-gas chromatography/mass spectrometry (Py-GC/MS). Within these organic fractions, compound groups such as aromatics, lignin, aliphatics, proteins and polysaccharides were identified by means of DRIFTS and the analyses and processes outlined here enables rapid and detailed quantitative and qualitative analysis of sediment organic matter. The DRIFTS approach can be used as stand-alone method for OM characterization with high temporal resolution in Holocene sediment records. It may also function as a screening process for more specific analyses of sample subsets, such as when coupled with pyrolysis-GC/MS to further tease apart the OM composition, identify sources and determine degradation status.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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 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".