Eclipse ChemClipse

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Eclipse ChemClipse supports the user to analyse data acquired from systems used in analytical chemistry. In particular, chromatography coupled with mass spectrometry (GC/MS) or flame-ionization detectors (GC/FID) is used to identify and/or monitor chemical substances. It's an important task e.g. for quality control issues. Groceries, for example, are under strict control. Producers, traders and retailers try to prevent that groceries contain harmful chemical substances. The presence or absence of those chemical substances is identified, among others, by using GC/MS or GC/FID techniques. Nevertheless, it requires some experience to evaluate the data sets, recorded by the instruments. Hence, ChemClipse supports the chemists to evaluate the analytical data sets and to create reports. Moreover, it offers a rich set of functionality to edit the data sets as well as an easy to use GUI. Its main functionality is listed as follows:


  • Converter (import and/or export of raw data sets)

  • Classifier (non-destructive methods to extract characteristic values)

  • Filter (destructive methods to optimize the data sets)

  • Peak detection (finding peaks – each peak is a chemical substance)

  • Chromatogram/Peak integration (calculation of the chromatogram/peak area)

  • Identification (identification of each peak mass spectrum)

  • Quantitation (use the data for calibration issues)

  • Reporting (report the results for further analytical steps)

  • Processing (automation of the data handling)


Due to its flexible approach, each functionality can be extended by plugins. For this, ChemClipse makes use of the Eclipse extension point mechanism. Therefore, it is best suited for scientists, students and interested persons to write their own extensions. The data model has been well designed, hence it should be no problem to focus on the necessary methods that are needed to write an own extension. Moreover, its graphical user interface can be extended by additional UI parts as well.

Figure 1 – An overview of a chromatogram data set recorded with a mass selective detector.


Figure 2 – Running a principal component analysis (PCA) on chromatographic data.


Latest Releases: 

From September 30th, 2016 to September 30th, 2016

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Commits on this project (last 12 months).