No abstract

No abstract

The Front Cover shows a cartoonish view into polymerase chain reaction: DNA molecules are amplified, resulting in the amplification curve on which they are walking. When analyzing this data, however, such a curve becomes a challenge for baseline correction algorithms. This is due to the vertical offset of datapoints before and after the curve. A newly developed algorithm accounts for this offset and is represented by an elevator, which takes all of the DNA molecules up the offset and to their destination. The left to right transition also represents the effect of transforming noisy data into clean signals, and sad DNA molecules into happy helixes. Data analysis challenges like this are encountered in various situations in chemistry and biology, to a selection of which the algorithm is applied, illustrating its utility. More information can be found in the Full Paper by Jacob Schneidewind et al.

Invited for this month's cover is the group of Jacob Schneidewind from Leibniz‐Institute for Catalysis (Germany). The cover picture shows a cartoonish view into polymerase chain reaction: DNA molecules are amplified, resulting in the amplification curve on which they are walking. When analyzing this data, however, such a curve becomes a challenge for baseline correction algorithms. This is due to the vertical offset of datapoints before and after the curve. A newly developed algorithm accounts for this offset and is represented by an elevator, which takes all of the DNA molecules up the offset and to their destination. The left to right transition also represents the effect of transforming noisy data into clean signals, and sad DNA molecules into happy helixes. Data analysis challenges like this are encountered in various situations in chemistry and biology, to a selection of which the algorithm is applied, illustrating its utility. Read the full text of their Full Paper at 10.1002/cmtd.202000027.

Accurate data analysis is a cornerstone for the meaningful interpretation of measurements in chemistry and biology. To enable accurate analysis, it is often necessary to remove the background from a measurement via baseline correction, as is commonly done for spectroscopy or chromatography. However, no equivalent methods for baseline correction exist for an entire group of measurements, which includes chemical reaction measurements, quantitative polymerase chain reaction and X‐ray absorption spectroscopy. This is because these measurements give rise to a different class of features in their signals, which prevent the application of classical baseline correction methods. In this work, a general method for baseline correction of these features is developed, which is shown to simplify and improve data analysis for these measurements. Through publicly accessible and easy to use software we expect this method to be broadly useful to improve and simplify data analysis for chemists and biologists.