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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.

Statistics for Analysts Who Hate Statistics, Part IV: Clustering

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.

Statistics for Analysts Who Hate Statistics, Part III: Principal Component Analysis

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.

Statistics for Analysts Who Hate Statistics, Part II: Linear Regression and Quantitative Structure–Retention Relationships

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.

Statistics for Analysts Who Hate Statistics, Part I: 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.

Data Analysis, Statistics, and Chemometrics