Best of the Week: Career Opportunities for PhD Graduates, Human Expertise and Machine Learning

This past week, LCGC International published a variety of articles on hot topics in separation science. First, Isabelle Kohler discusses the wide range of career opportunities that PhD graduates have. We also continue our recap of a recent roundtable discussion of women in chromatography, explore how artificial intelligence (AI) is reshaping the scientific method, and conclude with a couple of news stories on frankfurters and cannabinoid profiles.

This is the Best of the Week.

Translating Scientific Skills Into Impact: Careers Beyond Research and Academia

In this feature article, Isabelle Kohler, an assistant professor at Vrije Universiteit Amsterdam and CEO and Founder of NextMinds, highlights the wide range of career opportunities available to PhD graduates in the analytical sciences beyond traditional academia and industry research and development (R&D). She emphasizes in her article that the true value of a PhD lies not only in technical expertise, but also in transferable skills such as critical thinking, problem-solving, communication, and project management (1). Kohler’s article explores diverse roles in regulation, publishing, medical affairs, technical sales, field service, marketing, product management, non-academic research institutes, and startups. She also provides practical guidance on reframing PhD experience, identifying opportunities, networking strategically, and developing complementary skills to pursue impactful, nontraditional careers (1).

The Essential Skills Needed for Leading a Lab

In this part of a roundtable discussion focused on the experiences, challenges, and contributions of women in chromatography, LCGC International spoke to Susanne Boye, Daniela Held, and Claudia Zielke about the practical skills needed to lead a laboratory (2). Their insights touch upon important topics such as building inclusive laboratory cultures, mentorship, and the emerging technologies that are changing analytical chemistry and propelling it into the future.

Shaping Science Through Human Expertise and Machine Learning

In this LCGC International video interview, Andrew Anderson, vice president of innovation and informatics strategy at ACD/Labs, discusses how AI and digital integration are reshaping the scientific method through the AI–digital–physical DMTA cycle (3). He highlights that while AI enhances data-driven collaboration and efficiency, human creativity and scientific intuition remain central to discovery (3). Looking ahead, Anderson predicts a closer, more symbiotic relationship between scientists and intelligent systems over the next five years, with informatics platforms increasingly guiding experimental design, accelerating decision-making, and enabling more integrated, predictive workflows in pharmaceutical research and development (3).

Chromatography-Driven Insights into Fat Replacement Effects in Frankfurters

A recent study published in the journal Foods evaluated lard-derived diacylglycerol (DG) as a homologous fat replacer in frankfurters to improve nutritional quality without compromising flavor (4). Using gas chromatography-mass spectrometry (GC–MS) and electronic nose analysis, researchers identified 50 volatile compounds and showed that replacing pork fat with purified glycerolized lard (PGL) altered aroma profiles while increasing the variety and content of key flavor compounds (4). Aldehydes, alcohols, and terpenes were major contributors to flavor differences. DG substitution also improved water content, color attributes, and protein thermal stability, supporting lard-derived DG as a promising fat alternative for healthier, high-quality meat products (4).

Advancing Cannabinoid Profiling with UHPLC-MS Chromatography

Researchers from Brazilian academic institutions and the Federal Police developed and fully validated a robust ultrahigh-pressure liquid chromatography (UHPLC)–low-resolution MS method for qualitative and quantitative analysis of cannabinoids in cannabis oil and marijuana (5). The team optimized extraction conditions, identifying ethyl acetate as the most effective solvent, and validated the method for seven cannabinoids, achieving low limits of detection and quantification, high recoveries, and strong precision across analysts, days, and laboratories (5). The method showed no matrix effects and effectively separated key cannabinoid isomers. Overall, the study demonstrates a fast, sensitive, and selective approach suitable for forensic, medicinal, and regulatory cannabinoid analysis.

References

1. Kohler, I. Translating Scientific Skills Into Impact: Careers Beyond Research and Academia. The Column 2025, 21 (4), 7–12. Available at: https://www.chromatographyonline.com/view/translating-scientific-skills-into-impact-careers-beyond-research-and-academia
2. Jones, K. The Essential Skills Needed for Leading a Lab. The Column 2025, 21 (4), 13. Available at: https://www.chromatographyonline.com/view/the-essential-skills-needed-for-leading-a-lab
3. Jones, K. Shaping Science Through Human Expertise and Machine Learning. The Column 2025, 21 (4), 5–6. Available at: https://www.chromatographyonline.com/view/shaping-science-through-human-expertise-and-machine-learning
4. Chasse, J. Chromatography-Driven Insights into Fat Replacement Effects in Frankfurters. LCGC International. Available at: https://www.chromatographyonline.com/view/chromatography-driven-insights-into-fat-replacement-effects-in-frankfurters (accessed 2025-12-18).
5. Chasse, J. Advancing Cannabinoid Profiling with UHPLC-MS Chromatography. LCGC International. Available at: https://www.chromatographyonline.com/view/advancing-cannabinoid-profiling-with-uhplc-ms-chromatography (accessed 2025-12-18).