Data Analysis, Statistics, and Chemometrics

At Pittcon 2023, LCGC spoke with Jim Grinias of Rowan University. This segment is the full-length version of the interview that was conducted at the conference.

The workshop will take place 28–29 June 2023 in Liège, Belgium.

A newly published review reveals insights into the history of capillary electrophoresis, from its development to its current state-of-the-art applications, through a comprehensive bibliometric analysis of publications between 1980 and 2021.

A desorption electrospray ionization mass spectrometry (DESI-MS) method has been developed to effectively analyze historical synthetic dye samples.

A new study tested different solvents and buffer additives for ion mobility spectrometry to test their impact on results.

Scientists have discovered that combining imaging mass spectrometry and microscopy can enhance molecular visualization.

Separation science will be at the core of the artificial intelligence (AI) revolution in science.

Serial coupling of different column types can provide several important benefits to the chromatographer, including fine-tuning selectivity to separate complex mixtures.

On a fundamental level, equilibrium and diffusion define chemical separations. Practical applications of partition coefficients to chromatographic sample preparation illustrate this point.

Understanding the relationship between selectivity and retention is key to realizing excellent gas chromatographic separations.

In this extended special feature to celebrate the 35th anniversary edition of LCGC Europe, leading figures from the separation science community explore contemporary trends in separation science and identify possible future developments.

This instalment of the LCGC Blog investigates fundamental relationships between the structures of molecules and their interaction with different materials, in the context of online supercritical fluid extraction–supercritical fluid chromatography (SFE–SFC).

Paul Bohn, the Arthur J. Schmitt Professor of Chemical and Biomolecular Engineering at the University of Notre Dame, is the 2022 recipient of the Society for Electroanalytical Chemistry (SEAC) Charles N. Reilley Award.

Examples from food science illustrate the usefulness of chemometrics techniques like pattern recognition, regression, and classification.

L. Scott Ramos will receive the 2021 Eastern Analytical Symposium (EAS) Award for Outstanding Achievements in Chemometrics at the EAS Symposium taking place November 15–17, 2021, in Plainsboro, New Jersey.

A panel discussion on the latest advances and future developments in data handling.

The topic of chromatographic integration and data interpretation raises a lot of questions about data integrity. Is it acceptable to integrate data? What are the limits?

Can we see the wood for the trees?

We have entered a new stage in the era of accelerations. Moore’s law continues its expansion, increasing exponentially the computer power available. Other accelerations are remarkable, particularly easy access to cloud computing and the expansion and influence of artificial intelligence to practically all sectors of our society.

Derringer desirability functions are a great favorite of mine because they are very simple and flexible. They may be applied to a variety of problems: whenever you need to select the “best” (sample, method, operating conditions etc.) from a set. It is also a convenient way to compare apples and oranges, whenever totally unrelated features must be ranked. They were first described by Derringer and Markham1 to select polymeric materials based on varied properties.

Part V of this series takes a closer look at discriminant analysis (DA). Discriminant analysis is a supervised method, meaning that it involves some previous knowledge of your samples.

Part IV of this series takes a closer look at clustering. Clustering can be very useful at observing your data when the sample dimensionality is large. This is a barbarian term meaning that diversity among your samples may be wide. In that case, the space reduction provided by principal component analysis (PCA) is not always convincing, because the simplification provided by a single two-dimensional plot erases too much information. Clustering allows you to preserve more information.

Part III of this series takes a closer look at principal component analysis (PCA). PCA can be very useful for observing your data when the observations you wish to compare are described by many variables. It is a relatively easy way to obtain a simplified image of the data, while trying to maintain as much information as possible.

This is part two of a series of tutorials that explain, in the simplest manner, how statistics can be useful, even to chromatographers who normally find statistics difficult, with a minimal understanding of its features. This part explains linear regressions.

This is part one of a series of tutorials that explain, in the simplest manne, how statistics can be useful, even to chromatographers who normally find statistics difficult, with a minimal understanding of its features. Part I explains how to collect and examine your data.

Jan Christensen from the University of Copenhagen, Denmark, and LCGC Europe Editorial Advisory Board member, spoke to The Column about his research in chemometrics and its practical applications in oil spill analysis.

The concluding part of the article from last month's "Question of Quality" on what "complete data" actually means in practice.

This article will explore what is meant by "complete data" in the context of a chromatography data system and, with an analysis of FDA 483 citations, explore the problems when a laboratory fails to understand this phrase.

The requirements for GPC/SEC data analysis to obtain precise, reproducible and accurate results are very specialized. Therefore, often dedicated software tools offering flow-rate correction or different calibration options need to be employed. This instalment of Tips & Tricks looks at these requirements.

This new data-management method is specifically designed to handle the large amounts of data produced in proteomics studies. Experimental methods and data are presented from a recent study conducting proteomics profiling of Schizosaccharomyces pombe to demonstrate the effectiveness of the data management solution. The results demonstrate enhanced data search speeds as well as effective data analysis.