Data Analysis, Statistics, and Chemometrics

Interest in applying artificial intelligence (AI) and machine learning (ML) to chromatography is greater than ever. In this article, we discuss data-related barriers to accomplishing this goal and how rethinking chromatography data systems can overcome them.

A two-step workflow to extract pure mass spectra from comprehensive two-dimensional liquid chromatography high-resolution mass spectrometry (LC×LC–HRMS) data has been created and tested.

Reinforcement learning was tested as a means of improving liquid chromatography method development.

A research group has developed an improved workflow for constructing machine learning (ML) models in oligonucleotide separation.

Physics-informed neural networks were tested for their capabilities in predicting concentration profiles in gradient liquid chromatography.

The company reported $1.68 billion in revenue at a 25.1% operating margin across its businesses in America, Europe, and Asia.

During Waters Corporation’s 2025 Investor Day, the company’s top executives highlighted the company’s growth and hints at future points of development.

Ardiana Kajtazi discusses her research identifying organic micropollutants in water using liquid chromatography–high-resolution mass spectrometry (LC–HRMS). She highlights the standardized filtration approach her team has developed based on intersection principles, utilizing retention indices from two reversed-phase liquid chromatography (RPLC) columns.

A two-step workflow to extract pure mass spectra from comprehensive two-dimensional liquid chromatography high-resolution mass spectrometry (LC×LC–HRMS) data has been created and tested.

A team of researchers has evaluated approaches in exposome research, highlighting sample preparation, MS-based platforms, and data acquisition strategies.

Researchers have produced a review of chromatography media and purification processes for handling complex and super-large biomolecules.

ACD/Labs recently released their 2025 Structure Elucidation & Verification Report, discussing how informatics trends have shifted since 2022.

Here is some of the most popular content posted on LCGC International this week.

Scientists at the Indian Institute of Technology Delhi recently created a new machine learning (ML)-based tool for process verification during process chromatography unit operations.

Here is some of the most popular content posted on LCGC International this week.

Here is some of the most popular content posted on LCGC International this week.

Following the HPLC 2024 Conference in Denver, Colorado, LCGC International spoke with Anne Marie Smith of ACD/Labs about the new ICH Q14 guidelines and how they impact analytical scientists and their work.

In this edition of “Inside the Laboratory,” Jillian Goldfarb of Cornell University discusses her laboratory’s work with using gas chromatography–mass spectrometry (GC–MS) to characterize compounds present in biofuels.

In this LCGC Blog, Katelynn Perrault Uptmor discusses her time teaching undergraduates separation science, and what she and her students have learned through their time together.

During RAFA 2024, Michel Suman of Barilla Spa and Catholic University Sacred Heart talked with us about his food safety and authenticity research, focusing on contaminants, adulterants, and authenticity markers in food processing.

Saer Samanipour from the Van ‘t Hoff Institute for Molecular Sciences (HIMS) at the University of Amsterdam spoke to LCGC International about the benefits of a data-driven reversed-phase liquid chromatography (RPLC) approach his team developed.

In a new study, scientists introduced no-code automation as a strategic response for addressing talent shortages in the chemical industry.

Analyzing functional foods reveals numerous health benefits. These foods are rich in bioactive compounds that go beyond basic nutrition, boosting the immune system and improving overall wellness. However, analyzing these compounds can be challenging. This article discusses AI algorithms to support automated method development for liquid chromatography, simplifying the process, enhancing labor efficiency, and ensuring precise results, making it accessible to non-experts for tea analysis.

As part of our ISC 2024 coverage, we recently interviewed Bram Huygens of Vrije Universiteit Brussel about his scientific background and his thoughts on receiving our Rising Stars of Separation Science Award for Liquid Chromatography.

Here is some of the most popular content posted on LCGC International this week.

MS-SMART integrates three intelligent algorithms: automatic mining of diagnostic ions, rapid filtration of alkaloids from untargeted MS/MS data, and structural recommendations for filtered components.

In this LCGC Blog, Kevin Schug discusses the potential machine learning has in helping predict physicochemical properties of molecules and optimizing chemical analysis processes.

Universidad Católica del Maul scientists reviewed advancements made in using machine learning and MALDI-TOF-MS for predicting antimicrobial resistance in different materials.

Shangdong University and Shandong Energy Group scientists recently used HS-GC-IMS was used alongside machine learning algorithms to detect the geographic origins of coal associated with larceny cases.

ACD/Labs, a leading informatics company specializing in software for digitalizing R&D, has announced the release of version 2024 of its Spectrus Platform applications.