Methyl benzoate as a marker for the detection of mold in indoor building materials
Bibliographic record
Abstract
A convenient analytical method to quantify volatile organic compounds (VOCs) emitted from various building materials has not been addressed yet. This work presents a new and rapid automated method using SPME combined with GC/MS. Methyl benzoate - as a metabolic biomarker for mold growth-was used to indicate VOCs and to determine and assess mold growth on damp samples. Gypsum board and wall-board paper were used as examples of common indoor building materials. Optimized extraction conditions were carried out manually, using a GC/flame ionization detector. Moldy samples were analyzed using an automated SPME-GC/MS analysis under optimized conditions. The amount of methyl benzoate emitted from the studied samples ranged from 32 to 46 ppb, where the density of the fungal biomass was found to be 8 x 10(4) cells/mL. A relationship between the amount of fungal biomass and the emitted concentration of methyl benzoate was found and assessed based upon cultured mold samples taken from indoor building sites. The analytical method shows promise for the compound methyl benzoate, which can easily be identified at low detection limits (LOD = 3 ppb) and good linearity (>0.988), and its extraction and detection can be accomplished cleanly by current extraction techniques. Results suggest that this method with easy sample preparation can be used for quantitation and, of importance, minimal matrix effects are observed.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".