Assessment of Thiol Compounds from Garlic by Automated Headspace Derivatized In-Needle-NTD-GC-MS and Derivatized In-Fiber-SPME-GC-MS
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Bibliographic record
Abstract
This study investigates the analysis of thiol compounds using a needle trap device (HS-NTD) and solid-phase microextraction (HS-SPME) derivatized headspace techniques coupled to GC-MS. Thiol compounds and their outgassed products are particularly difficult to monitor in foodstuffs. It was found that with in-needle and in-fiber derivatization, using the derivatization agent N-phenylmaleimide, it was possible to enhance the selectivity toward thiol, which allowed the quantitation of butanethiol, ethanethiol, methanethiol, and propanethiol compounds found in fresh garlic. A side-hole NTD was prepared and packed in house and utilized mixed DVB and Carboxen polymer extraction phases made of 60-80 mesh particles. NTD sampling was accomplished in the exhaustive sampling mode, where breakthrough was negligible. This work demonstrates a new application for a side-hole NTD sampling. A commercial mixed polymer phase of polydimethylsiloxane (PDMS) and divinylbenzene polymer (DVB) SPME fiber was used for SPME extractions. Under optimized derivatization, extraction, and analysis conditions for both NTD-GC-MS and SPME-GC-MS techniques, automated sampling methods were developed for quantitation. Both methods demonstrate a successful approach to thiol determination and provide a quantitative linear response between <0.1 and 10 mg L(-1) (R(2) = 0.9996), with limits of detection (LOD) in the low micrograms per liter range for the investigated thiols. Addition methods using known spiked quantities of thiol analytes in ground garlic facilitated method validation. Carry-over was also negligible for both SPME and NTD under optimized conditions.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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 it