A Mid‐Infrared Spectroscopy Method to Determine the Glucosamine, Galactosamine, and Muramic Acid Concentrations in Soil Hydrolysates
Bibliographic record
Abstract
The current method for determining amino sugars in soils involves a derivatization process which is time‐consuming and involves working with and disposing of hazardous chemicals. This study aims to evaluate the potential of mid‐infrared (MIR) spectroscopy to predict amino sugar concentrations in soil. Two separate sets of the same 60 soil samples were hydrolyzed and purified, and then one set of samples was transformed into amino sugar derivatives for gas chromatography (GC) determination and the other set of samples was used for MIR spectra collection without further processing. The GC‐measured concentrations of amino sugars were calibrated, using test‐set data set ( n = 20) and leave‐one‐out data set, against the MIR spectral data with partial least squares (PLS) regression. Based on the values of coefficient of determination ( R 2 ) and residual prediction deviation (RPD), the calibration models for amino sugars were good for both data sets, with R 2 values ranging from 0.82 to 0.98 and RPD values from 2.38 to 7.76. For test‐set validation, predictions were excellent for total amino sugars ( R 2 = 0.96, RPD = 5.14), glucosamine ( R 2 = 0.97, RPD = 6.27), galactosamine ( R 2 = 0.94, RPD = 4.16), and good for muramic acid ( R 2 = 0.84, RPD = 2.52). For leave‐one‐out cross‐validation, predictions were excellent for glucosamine ( R 2 = 0.91, RPD = 3.28), good for total amino sugars ( R 2 = 0.86, RPD = 2.70) and galactosamine ( R 2 = 0.81, RPD = 2.29), but relatively poor for muramic acid ( R 2 = 0.61, RPD = 1.61). We concluded that the MIR spectroscopy has high potential to estimate the concentrations of amino sugars in soil with significant savings in time, cost, and chemicals.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 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".