Ed Ahearne highlights the benefits of ML-driven chromatography for the development of biopharmaceutical products
|Articles|November 1, 2016
- LCGC North America-11-01-2016
- Volume 34
- Issue 11
Vol 34 No 11 LCGC North America November 2016 Regular Issue PDF
Click the title above to open the LCGC North America November 2016 regular issue, Vol 34 No 11, in an interactive PDF format.
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Articles in this issue
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Reader’s Question: Early Eluted Peakalmost 10 years ago
Gas Cylinder Safety, Part II: Setup and Usealmost 10 years ago
Supporting Bioanalysis with Dried Blood Spotsalmost 10 years ago
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In this sixth part of " Conversations with Michael Dong," a series on fundamentals of HPLC in pharmaceutical analysis, LCGC International’s "Perspectives in HPLC" column editor compares the chromatographic properties of acetonitrile and methanol.
Ed Ahearne combined automation, machine learning, and Bayesian optimization to speed up method development for siRNA medicines.
Data-independent acquisition (DIA) liquid chromatography-tandem mass spectrometry (LC–MS/MS) reveals shared and site-specific proteins across the thighbone.
Laura Righetti explains why quantification, not instrumentation, still keeps mass spectrometry imaging (MSI) out of routine food safety testing.
Large language models help chromatographers build LC pressure troubleshooting apps in hours.
Magnetic molecularly imprinted polymer extraction with liquid chromatography-high resolution mass spectrometry (LC–HRMS) finds patulin in store-bought juice.
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