Concentrating on Chromatography

David Oliva
  • 5.0 (1)
  • Chemistry
  • Updated Biweekly

Dive into the frontiers of chromatography, mass spectrometry, and sample preparation with host David Oliva. Each episode features candid conversations with leading researchers, industry innovators, and passionate scientists who are shaping the future of analytical chemistry. From decoding PFAS detection challenges to exploring the latest in AI-assisted liquid chromatography, this show uncovers practical workflows, sustainability breakthroughs, and the real-world impact of separation science. Whether you’re a chromatographer, lab professional, or researcher you'll discover inspiring content!

  1. 1d ago

    Episode 75: Breathomics, Asthma & GC×GC: PH Stefanuto on Fighting the “Proof‑of‑Concept” Trap

    In this episode of Concentrating on Chromatography, David Oliva and co‑host Candi Gokey sit down with Dr. Pierre‑Hugues (PH) Stefanuto to explore how comprehensive GC×GC‑TOFMS breathomics is reshaping asthma research and personalized medicine. PH shares his journey from forensic applications (yes, even the “smell of death”) to non‑invasive exhaled breath analysis for chronic lung inflammation, and why he believes asthma is the perfect playground to translate breath research into real clinical impact.We dig into PH’s recent work on a complete GC×GC‑TOFMS workflow to improve asthma monitoring, including patient breath collection, VOC analysis, biomarker discovery, and robust data processing strategies. He explains how combining targeted markers like FeNO with VOC “breathprints” can improve asthma phenotyping and treatment decisions, and how his group identified potential VOC biomarkers such as hexane, 1‑propanol, 2‑hexanone, undecane, and nonanal.A major theme of this conversation is PH’s call to “fight the breath proof‑of‑concept paradigm.” Instead of stopping at small, under‑powered studies, his group pushes through large cohorts, rigorous validation, and clinically relevant study design so that breath analysis can actually make it into the clinic and, eventually, into patients’ homes. We also talk candidly about skepticism toward breath analysis, how overpromising has hurt the field, and what it takes to rebuild trust with clinicians and medical staff.We also cover:Why the Multidimensional Chromatography Workshop (MDCW) was designed to be a free, open‑access event, and how that philosophy supports open science, networking, and inclusion of early‑career scientists and lab staff.PH’s path from forensic GC×GC to clinical breathomics and chronic inflammation research, and how CSI‑inspired curiosity turned into a career in analytical chemistry.How untargeted molecular screening and rich VOC fingerprints complement conventional single‑marker tests and enable finer‑grained patient phenotyping.Why GC×GC‑MS often achieves higher annotation rates than LC‑MS in certain metabolomics applications, thanks to retention patterns, EI libraries (NIST/Wiley), and structured 2D chromatography.The future vision: patients blowing into a simple device (or even a phone‑connected system) to monitor lung inflammation at home, integrate with wearables and environmental data, and guide day‑to‑day asthma management.How AI and machine learning can help connect small‑molecule breath data to larger metabolic pathways and multi‑omics datasets, inspired in part by platforms like GNPS.PH’s thoughts on chromatography education, why analytical chemistry is underrepresented in curricula, and how we should prepare the next generation of clinicians and scientists for omics‑driven healthcare.If you’re interested in chromatography, mass spectrometry, breath analysis, asthma, or translational metabolomics, this is a deep yet accessible conversation that bridges instrumentation, statistics, and real‑world clinical needs.Keywords: breathomics, exhaled breath analysis, asthma monitoring, GCxGC, GC×GC‑TOFMS, multidimensional chromatography, volatile organic compounds, VOC biomarkers, untargeted metabolomics, personalized medicine, PH Stefanuto, Pierre‑Hugues Stefanuto, MDCW, Multidimensional Chromatography Workshop, FeNO, chronic lung inflammation, clinical mass spectrometry, analytical chemistry education, chromatography podcast, Concentrating on Chromatography, David Oliva, Candi Gokey

  2. Aug 17

    Episode 74: Decoding the Glycocode: AI-Powered Glycoproteomics & Cancer Research | Jacob Russell (Riley Lab, UW)

    In this episode of Concentrating on Chromatography, host David Oliva is joined for the first time by co-host Candice (Candi) Gokey — PhD candidate at UC San Diego / San Diego State University and expert in LC-MS/MS method development for untargeted metabolomics to interview Jacob Russell, a third-year PhD student in the Riley Research Group at the University of Washington (Seattle).Jacob's research sits at the frontier of mass spectrometry based intact glycoproteomics, where the goal is to keep glycans attached to peptides during analysis — preserving the biological information that is lost when glycans are enzymatically removed. His lab, led by Prof. Nick Riley, develops new methods to tackle every stage of glycoproteomic analysis, from sample preparation and enrichment through data acquisition and bioinformatics.🔬 WHAT WE COVER:• What is the glycocalyx, and why does glycoproteomics matter for cancer, immunity, and protein biology?• N-linked vs. O-linked glycosylation: why they require completely different fragmentation strategies (HCD vs. ETD/EThcD)• Autonomous Dissociation-type Selection (ADS): how real-time library searching (RTLS) of oxonium ion ratios (m/z 138 vs. 144) enables on-the-fly selection of the right fragmentation method for N- vs. O-glycopeptides — published in J. Proteome Research (Sutherland, Veth, Russell et al., 2024)• How XGBoost machine learning outperforms RTLS by classifying ~50 oxonium ions simultaneously, recovering Core 2 O-glycopeptides that the simpler ratio-based method misses• Casanovo Foundation: a transformer-based foundation model for tandem mass spectra and how deep learning is reshaping glycopeptide classification (Sanders et al., arXiv 2025)• LacNAc-ase enabled glycoproteomics: using endo-β-galactosidase (EBG) to "trim" poly-LacNAc chains on N-glycans — uncovering previously undetectable glycoforms (ASMS 2026 presentation)• Cutaneous vs. uveal melanoma: comparing poly-LacNAcylated glycoproteins (including galectin-3 binding partners like CD63, LAMP-2, and basigin) between cell lines — and why uveal melanoma has a ~50% distant metastasis rate vs. ~5% for cutaneous • The future of intelligent data acquisition and real-time mass spectrometry• Candi's parallel world of untargeted metabolomics, coral-algae chemical communication, and GNPS/MassQL — and the surprising overlap with glycoproteomics🔗 RESOURCES MENTIONED:• Riley Research Group at UW: https://www.riley-research.com/• ADS Paper: Sutherland, Veth, Russell et al., J. Proteome Res. 2024, 23, 5606–5614• Casanovo Foundation: Sanders et al., arXiv 2025• GlyCounter software (Riley Lab, UW)• XGBoost (Chen & Guestrin, UW)• GNPS / MassQL (Dorrestein Lab, UCSD)👥 GUESTS:Jacob H. Russell — PhD Student, Dept. of Chemistry, University of Washington | Riley Research Group | Glycoproteomics | Mass Spectrometry | Machine LearningCo-host: Candice Gokey — PhD Candidate, UC San Diego / San Diego State University | LC-MS/MS Method Development | Untargeted Metabolomics | Coral Reef Holobiont Research🎙️ HOST: David Oliva |  @ChromatographyTalk  |  @Organomation  |  @SeparationScience  collaborator━━━━━━━━━━━━━━━━━━━━━━━━━━━━━🔔 Subscribe for more interviews with researchers and industry leaders in analytical chemistry, chromatography, and mass spectrometry.#Glycoproteomics #MassSpectrometry #Chromatography #AnalyticalChemistry #MachineLearning #Glycans #Proteomics #Cancer #UVealMelanoma #LCMS #OxoniumIons #ETD #XGBoost #RileyLab #UniversityOfWashington #PodcastScience #Metabolomics```

  3. Aug 10

    Episode 73: How Chromatography Helps Build Better Catalysts | Dr. Madalyn Radlauer (San José State University)

    Chemistry doesn't end when the reaction is finished. In many research labs, chromatography is what allows scientists to understand whether their chemistry actually worked.In this episode of Concentrating on Chromatography, we sit down with Dr. Madalyn Radlauer, Associate Professor of Chemistry at San José State University, to discuss how her research group combines polymer chemistry, catalysis, and chromatography to answer fundamental questions about catalyst design and reproducibility.We explore how polymer microenvironments influence catalyst performance, why star polymers have become an exciting research area, and how techniques like TLC, silica gel chromatography, gel permeation chromatography (GPC/SEC), and emerging GC-MS methods help guide discoveries in the laboratory.Dr. Radlauer also discusses mentoring undergraduate researchers, the importance of reproducibility in chemistry, and why chromatography is far more than just a purification step—it's a tool for scientific discovery.In this episode:* How polymer-supported catalysts work* Why catalyst microenvironments matter* The role of TLC, column chromatography, SEC, and GPC in research* Developing more reproducible catalytic reactions* Using chromatography to solve research problems* Transitioning from purification to GC-MS analysis* Mentoring undergraduate researchers* The future of polymer-supported catalysisLearn more about Dr. Radlauer and her research🔬 Research Grouphttps://www.sjsu.edu/radlauer/🏛️ San José State University Department of Chemistryhttps://www.sjsu.edu/chemistry/📄 Reproducibility and Autoxidation Publicationhttps://pubs.acs.org/doi/10.1021/acsomega.5c07532📄 Boyer Lab: Arm-First Star Polymers Using RAFThttps://pubs.rsc.org/py/article-abstract/2/8/1671/355906/Optimizing-the-generation-of-narrow-polydispersity📄 Hawker & Fréchet: Acid and Base Organocatalysts in the Same Pothttps://onlinelibrary.wiley.com/doi/10.1002/anie.200502095🎙️ About Concentrating on Chromatography -  @ChromatographyTalk  Concentrating on Chromatography is a podcast exploring chromatography, sample preparation, mass spectrometry, analytical chemistry, and the scientists advancing laboratory research. Each episode features conversations with researchers, instrument experts, and industry leaders working to solve real-world analytical challenges.Subscribe for new interviews covering chromatography techniques, sample preparation, laboratory innovation, and analytical chemistry.#Chromatography #AnalyticalChemistry #Catalysis #PolymerChemistry #GPC #SEC #GelPermeationChromatography #Research #Chemistry #GCMS #STEM #Laboratory #SciencePodcast #OrganometallicChemistry

  4. Aug 3

    Episode 72: PFAS in Water, Food & Packaging: Michelle Taylor on the "Forever Chemicals" Documentary Series

    In this episode of  @ChromatographyTalk  ,  @Organomation  General Manager David Oliva sits down with Michelle Taylor, Editor-in-Chief of LabCompare, to go behind the scenes of their groundbreaking two-part documentary series on PFAS (per- and polyfluoroalkyl substances). The conversation kicks off with a startling revelation: many of the world’s leading PFAS experts have completely stopped eating seafood due to high contamination levels discovered during testing. From there, we dive deep into the technical and regulatory challenges facing today's analytical laboratories.In this episode, we discuss:* The Laboratory Throughput Crisis: Why labs are currently "overwhelmed with samples" and how tiered testing—using single quad instruments for screening before moving to triple quads—is helping to manage capacity without sacrificing sensitivity.* The Battle Against Background Contamination: Why PTFE-free (PFAS-free) laboratory equipment is no longer optional when trying to detect "forever chemicals" at the parts-per-trillion and parts-per-quadrillion levels.* Complex Matrices (Seafood & Packaging): The technical hurdles of analyzing high-lipid samples like fish and the rising demand for migration testing as states like Rhode Island begin to phase out PFAS in food packaging.* Global Regulatory Shifts: A look at the "dual compliance model" required for labs supporting international exporters, especially with the European Union’s stricter enforcement compared to the U.S. monitoring approach.* The "Grand Challenge" of Unknowns: How EPA Method 1621 (Total Organic Fluorine) is helping labs close the mass balance gap and why the next generation of bench chemists must embrace **AI and data science** to manage the massive datasets generated by high-resolution mass spectrometry.* PFAS Destruction vs. Remediation: The promising (and problematic) methods for breaking the carbon-fluorine bond, and why incineration may inadvertently be spreading shorter-chain PFAS into the air.Whether you are a lab manager looking to future-proof your workflow or a chemist navigating the ever-evolving landscape of EPA Method 1633, this episode provides essential insights into the most pressing environmental crisis of our time.Watch the LabCompare Documentary Series:* Part 1: Preparing and Modernizing for Proposed National PFAS Regulations* Part 2: Forever Chemicals Contamination in Food and PackagingResources for PFAS Analysis:* Learn more about PTFE-free sample preparation at Organomation.com: https://www.organomation.com/ptfe-free-nitrogen-evaporators-for-pfas-analysis* Explore more analytical lab resources at LabCompare.com (https://www.labcompare.com)#PFAS #ForeverChemicals #Chromatography #EnvironmentalScience #LabCompare #Organomation #SamplePrep #EPA1633 #AnalyticalChemistry

  5. Jul 27

    Episode 71: In Silico Modeling, 2D-LC & Green Chromatography: Imad Ahmad on the Future of Method Development

    In this episode of  ⁨@ChromatographyTalk⁩  ,  @Organomation  General Manager David Oliva sits down with Imad Haidar Ahmad, Scientific Research Director at Amgen leading separations for Discovery Chemistry and Externalization. Imad's career spans Novartis, Merck, and now Amgen, and his research on computer-assisted method development has landed multiple front covers in Analytical Chemistry.We dig into how his group builds retention models from as few as nine lab experiments to map an entire separation landscape — replacing weeks of trial and error with a resolution map you can query and rebuild from. Imad breaks down where this modeling approach struggles (large molecules, SFC, fully autonomous method development), walks through two-dimensional liquid chromatography in plain terms for anyone who's only worked in 1D, and shares the story behind his group's greenness-score mapping work — including where swapping acetonitrile for methanol worked, and where it didn't.We also talk career advice: what a typical day looks like leading a discovery-stage separations team, how undergrad and grad students can bridge the academia-to-industry gap, and what Imad sees as the field's biggest unsolved problem.Topics covered:- 00:00 – Imad's background: polymer analysis, a postdoc with Peter Carr, and the path through Novartis, Merck, and Amgen- 02:00 – How an undergrad project analyzing bread sparked a career in analytical chemistry- 06:00 – A day in the life leading a discovery-stage separations team- 08:00 – Advice for students bridging academia and industry- 12:00 – In silico modeling explained: how nine experiments can map a full separation- 14:00 – Reading a resolution map- 16:00 – Why trial-and-error gets risky at late-stage development- 20:00 – Where modeling breaks down: large molecules, SFC, and the case for autonomous method development- 24:00 – Two-dimensional LC, explained for anyone who's only run 1D- 27:00 – Why 2D-LC adoption has been slow — and what's changing- 29:00 – Mapping analytical greenness across the separation space- 32:00 – Acetonitrile vs. methanol: balancing green chemistry with performance- 35:00 – Advice for students: don't treat HPLC as a black box- 37:00 – The biggest unsolved problem in separation scienceConnect with Imad Haidar Ahmad: LinkedIn: https://www.linkedin.com/in/imadhaidarahmad 🎧 Listen to Concentrating on Chromatography on Spotify — https://open.spotify.com/show/1wZg67rCjoGju4yhqxI3tL?si=n9GLz9zjSCOrVfXPxFPhng🔔 Subscribe for more conversations on chromatography, sample prep, and analytical chemistry.#chromatography #AnalyticalChemistry #HPLC #2DLC #MethodDevelopment #GreenChemistry #Pharma

  6. Jul 20

    Episode 70: AIMS vs. LC-MS for Cancer Diagnostics: ccRCC Metabolomics with Rachel Wood | Queen's University

    What if you could assess a tumor's metastatic potential — without extensive sample prep — directly from tissue? Rachel Wood is working on exactly that.Rachel Wood is a PhD candidate at Queen's University (Kingston, Ontario), working jointly in the labs of Dr. Richard Oleschuk (Chemistry) and Dr. Chris Nicoll (Pathology & Molecular Medicine). Her research applies Ambient Ionization Mass Spectrometry — specifically the Liquid Microjunction Surface Sampling Probe (LMJ-SSP) and Desorption Electrospray Ionization (DESI) — to profile the metabolomics of non-metastatic and metastatic clear cell Renal Cell Carcinoma (ccRCC) cell lines. The long-term goal: a rapid, minimally invasive MS-based prognostic tool for kidney cancer.This is the first reported use of LMJ-SSP to assess metastatic status in ccRCC — and early PCA results are already showing distinct separation between cell line profiles.In this episode, David and Rachel cover:→ Why ccRCC is such a resistant biomarker problem despite active genomic, proteomic, and metabolomic research→ The clinical stakes of "diagnosed by accident" — and what a prognostic biomarker actually needs to do for a clinician→ How AIMS complements (not replaces) LC-MS in a discovery-phase cancer workflow→ LMJ-SSP vs. DESI: what each technique sees that the other misses, including spatial resolution differences (~1mm vs. ~50µm)→ Why Rachel runs LMJ-SSP on both a triple quad and a Q-TOF — and what each instrument contributes to the same research question→ How her lab repurposed a 3D printer as a custom automated XYZ sampling stage with 50µm movement precision→ Managing high-dimensional metabolomics data across two polarities, two cell lines, and two mass analyzers — the pipeline explained→ What unsupervised machine learning (PCA) showed in early results→ The path from cell lines to excised tumor tissue to, ultimately, liquid biopsies→ Advice for analytical chemists considering work at the chemistry–oncology interface🔗 CONNECT WITH RACHEL WOODLinkedIn: https://www.linkedin.com/in/rachel-wood-7b64492a2/Queen's University – Oleschuk Lab: https://oleschuklab.ca/📬 CONCENTRATING ON CHROMATOGRAPHYWebsite: https://concentratingonchromatography.com/Organomation: https://www.organomation.com🔬 ABOUT ORGANOMATIONOrganomation manufactures nitrogen blowdown evaporators, nitrogen generators, and sample concentration instruments for analytical laboratories worldwide#MassSpectrometry #AmbientIonization #Metabolomics #KidneyCancer #ccRCC #DESI #AIMS #CancerResearch #AnalyticalChemistry #Chromatography #Biomarkers #LiquidBiopsy #ReanalCellCarcinoma #MachineLearning #PCA #LabScience #GradSchool #Queen'sUniversity #Oncology #SamplePreparation

  7. Jul 13

    Episode 69: Droplet Cyclic Ion Mobility Mass Spec: Compressing Months of Enzyme Screening Into Just 14 Hours!

    What if you could compress 179 days of grueling laboratory workflow into just 14 hours?In this episode of  @ChromatographyTalk  ,  @Organomation  General Manager David Oliva sits down with analytical chemist Laura Penabad to discuss her groundbreaking new research in biocatalysis and high-throughput screening. Evolving engineered enzymes typically requires checking thousands of variants—a massive bottleneck when traditional Liquid Chromatography-Mass Spectrometry (LC-MS) runs take minutes per sample. Laura reveals how her team built a modular screening platform that shatters this limitation, running samples 128 times faster than standard methods without sacrificing analytical accuracy.Laura breaks down the "orchestra" of technologies making this possible, including coupling nanoliter droplet microfluidics with Cyclic Ion Mobility-Mass Spectrometry (cIM-MS) to separate complex chemical isomers in milliseconds. She also shares the chaotic but triumphant behind-the-scenes stories of her PhD journey—from presenting a "comically bad" early poster at ASMS to recruiting an undergraduate mechanical engineer to build custom automation software from scratch.If you are an early-career researcher, a mass spec enthusiast, or curious about the future of synthetic biology and machine learning in chemistry, this episode is packed with invaluable intuition and advice.#MassSpectrometry #AnalyticalChemistry #Chromatography #Biocatalysis #DirectedEvolution #IonMobility #PhDJourney #LabAutomation #WomenInSTEM #SyntheticBiology

  8. Jul 6

    Episode 68: Peak Fitting, Deconvolution & AI in Chromatography

    Chromatography software hasn't fundamentally changed in decades — but the peaks it's trying to measure have gotten a lot harder to solve. In this episode of  @ChromatographyTalk  I sit down with Ron Brown, creator of PeakFit and PeakLab, and returning guest Dr. M. Farooq Wahab, analytical chemist and research engineering scientist at the University of Texas at Arlington, to dig into the real science (and art) of peak modeling.We cover why baseline subtraction and denoising matter before you ever touch a peak model, the difference between true mathematical deconvolution and the "deconvolution" most chromatographers mean when they say it, and why mass balance is the line between a valid peak model and bad science. Ron walks through the historical roots of peak modeling going back over a century, and explains how his HVL and generalized moment models build on pioneers whose work rarely gets the recognition it deserves.We also get into resolving completely co-eluting peaks using 3D PDA/DAD data, multivariate curve resolution, and derivative-based zero-crossing methods — techniques that go well beyond what a single chromatographic dimension can tell you. And we close with a candid conversation about generative AI's growing role in analytical chemistry: where it genuinely accelerates good science, where it's eroding foundational skills in newer chemists, and why Farooq argues AI is only as useful as the prepared mind using it.In this episode:- What peak modeling actually solves that chromatography alone can't- Why baseline correction and denoising come before any modeling step- The historical origins of peak fitting, from Carl Clarison to Van der Linden- True deconvolution vs. what the field commonly (and incorrectly) calls deconvolution- Why mass balance is non-negotiable in a valid peak model- Using the F-statistic and information criteria to choose the right model complexity- Resolving fully co-eluting peaks with 3D spectral data and MCR-ALS- Generative AI's expanding role in chromatographic data processing — and its risks for early-career scientistsAbout our guests:Ronald E. Brown is a chemical engineer trained at Purdue University with 36 years of experience developing chromatography and spectroscopy peak modeling software. He is the sole developer of PeakFit and PeakLab, used by spectroscopists, chromatographers, and defense researchers worldwide. His paper on detecting outliers in nonlinear regression using the false discovery rate has been cited more than 1,950 times.Dr. M. Farooq Wahab is an analytical chemist and research engineering scientist at the University of Texas at Arlington. He earned his PhD from the University of Alberta and focuses his research on data processing, non-linear denoising, deconvolution, and generative AI applications in analytical chemistry.🎧 Listen to more episodes of Concentrating on Chromatography: https://open.spotify.com/show/1wZg67rCjoGju4yhqxI3tL?si=n9GLz9zjSCOrVfXPxFPhng🔗 Learn more about Organomation's nitrogen blowdown evaporators and sample prep instruments: https://www.organomation.com/#Chromatography #PeakFitting #AnalyticalChemistry #MassSpectrometry #Deconvolution #ChemometricsAI #HPLC #LabScience

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Dive into the frontiers of chromatography, mass spectrometry, and sample preparation with host David Oliva. Each episode features candid conversations with leading researchers, industry innovators, and passionate scientists who are shaping the future of analytical chemistry. From decoding PFAS detection challenges to exploring the latest in AI-assisted liquid chromatography, this show uncovers practical workflows, sustainability breakthroughs, and the real-world impact of separation science. Whether you’re a chromatographer, lab professional, or researcher you'll discover inspiring content!

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