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#chromatography

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Nina Davtian

My first first-authored #scientific #publication as a former #postdoc at ICTA-UAB, published in #Journal of #Chromatography A one year and a half ago, deserves a #thread on #Mastodon as well!
We selectively extract #levoglucosan and its isomers from complex matrices, such as #sediments, for quantification by liquid chromatography-electrospray ionization-tandem mass spectrometry (LC-ESI-MS/MS)!
#Science #ScienceMastodon #Paper
#LiquidChromatography #MassSpectrometry
doi.org/10.1016/j.chroma.2023. (1/28)

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Public
Sunnie

This was my first time working with ube extract, and DANG is that stuff strong!

I was incredibly amused to find that when I wiped up some spilled extract with a damp paper towel, it did the same kind of chromatography as my fountain pen inks.

It's so pretty! I couldn't quite get it to show up right but I did my best to tweak it a little - the actual color is that vibrant shade of purple the camera can never quite capture.

2/2

A white paper towel with a large splotch of maroon edged with fading pink and vibrant blues around the edges
A folded white paper towel on a cutting board, with a large splotch of maroon edged with fading pink and vibrant blues around the edges
#ube#chromatography#CrepeCake
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C. Anthony Lewis

@FaithfullJohn I, very successfully, used the cut and weigh method to quantify chromatographic peaks. In an attempt to increase the mass of each peak (and therefore, I reasoned, precision) I tried photocopying onto OHP sheets but quickly found the precision was worse... I figured the thickness of the OHP sheets was less consistent than paper. But this was 40 years ago...
#chromatography #ChemicalAnalysis #quantification

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Eric R. Scott

I spent SO MANY HOURS in grad school manually adjusting peak baselines and deciding what should or shouldn't count as a peak and then going back and doing it all again because I called it a peak in one sample but not the other. I'm SO happy that progress is being made on this problem and very excited to read this preprint!

biorxiv.org/content/10.1101/20

#GCMS #LCMS #XCMS #AnalyticalChemistry #Chromatography
#Metabolomics

bioRxivPicky with peakpicking: assessing chromatographic peak quality with simple metrics in metabolomicsChromatographic peakpicking continues to represent a significant bottleneck in automated LC-MS workflows. Uncontrolled false discovery rates and the lack of manually-calibrated quality metrics require researchers to visually evaluate individual peaks, requiring large amounts of time and breaking replicability. This problem is exacerbated in noisy environmental datasets and for novel separation methods such as hydrophilic interaction columns in metabolomics, creating a demand for a simple, intuitive, and robust metric of peak quality. Here, we manually labeled four HILIC oceanographic particulate metabolite datasets to assess the performance of individual peak quality metrics. We used these datasets to construct a predictive model calibrated to the likelihood that visual inspection by an MS expert would include a given mass feature in the downstream analysis. We implemented two novel peak quality metrics, a custom signal-to-noise metric and a test of similarity to a bell curve, both calculated from the raw data in the extracted ion chromatogram and found that these outperformed existing measurements of peak quality. A simple logistic regression model built on two metrics reduced the fraction of false positives in the analysis from 70-80% down to 1-5% and showed minimal overfitting when applied to novel datasets. We then explored the implications of this quality thresholding on the conclusions obtained by the downstream analysis and found that while only 10% of the variance in the dataset could be explained by depth in the default output from the peakpicker, approximately 40% of the variance was explained when restricted to high-quality peaks alone. We conclude that the poor performance of peakpicking algorithms significantly reduces the power of both univariate and multivariate statistical analyses to detect environmental differences. We demonstrate that simple models built on intuitive metrics and derived from the raw data are more robust and can outperform more complex models when applied to new data. Finally, we show that in properly curated datasets, depth is a major driver of variability in the marine microbial metabolome and identify several interesting metabolite trends for future investigation. ### Competing Interest Statement The authors have declared no competing interest.
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