Limitation of using silver ion solid‐phase extraction for animal lipids with a low <i>trans</i> content
Bibliographic record
Abstract
Abstract Silver ion solid‐phase extraction (Ag + ‐SPE) was reported to provide effective separations compared to other Ag + techniques but at a fraction of cost and time. Ag + ‐SPE cartridges resolved fatty acid methyl esters (FAMEs) with different number and/or geometric configuration of double bonds. Here we attempted to determine the trans fatty acids (FA) contained in a low total trans FA sample, horse lipids; lamb was used as a control having a markedly higher total trans content. Gas chromatographic assessment of the fractions showed a good separation of the cis and trans monounsaturated FA (MUFA) fractions, but the relative high content of contaminants that coeluted with these FA impaired the identification of the latter in horse lipids. In lamb trans MUFA isomers could be identified since their abundance relative to impurities was greater. Several attempts were made to remove the contaminants from the SPE cartridges including an extensive prewash with acetone and hexane, a prewash with solvents that would elute the cis MUFA fraction, and a complete prewash of all solvents used in the fractionation, hexane, acetone, and acetonitrile. The prewash using all elution solvents removed most contaminants but subsequently impaired the separation of trans and cis MUFA fractions. The same samples were subjected to Ag + ‐HPLC fractionation that showed no impurities demonstrating that they were derived from the Ag + ‐SPE separation. The trans MUFA fraction collected from Ag + ‐HPLC allowed for the identification of the trans 16:1 and 18:1 FA in horse lipids and is recommended for samples with low trans levels. Practical applications: The commercially available silver ion solid‐phase extraction (Ag + ‐SPE) cartridges contain appreciable amounts of contaminants that can interfere with the subsequent GC‐FID elution of low levels of trans fatty acid methyl esters (FAMEs), especially when contaminants are leaked by the SPE tube in quantities comparable to the trans MUFA content. The contaminants could not be quantitatively removed by prewashing the cartridges with acetone and hexane. Acetonitrile removed most contaminants but altered the Ag + ‐SPE tube ability to resolve the trans and cis MUFA fractions. Ag + ‐HPLC fractionation is recommended for isolation of low levels of trans MUFA, since the washing and conditioning of the chromatographic column can be extended as needed. Ag+‐SPE cartridges are used to separate FAMEs based on number and geometric configuration of double bonds to aid in the identification of complex GC separations.
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How this classification was reachedexpand
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".