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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. 

Paul Bohn Receives SEAC Charles N. Reilley Award

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

Enter the Matrix: Improving the Interpretation of Separations Data Using Chemometrics in Analytical Investigations

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. 

L. Scott Ramos Wins 2021 EAS Award for Outstanding Achievements in Chemometrics

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

Trends and 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?

The Question of Chromatographic Integration in Data Integrity

Are You Producing Data or Information?

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.

A New Era for Big Data and Chromatography

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.

Statistics for Analysts Who Hate Statistics, Part VI: Derringer Desirability Functions

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.

Statistics for Analysts Who Hate Statistics, Part V: Discriminant Analysis

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.

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