Advanced Data Analysis

With our 30 years of experience in research and development we have the software that lets you analyze the data for your chemical sensors.
Data analysis for chemical sensors requires expert knowledge about the properties of the various gas sensors, the requirements of the specific application and the capabilities of the various algorithms. Let our analysis software show you how we used our expertise to make programs that combine powerful algorithms, interactive analysis with a user-friendly interface.

Easy user interface, powerful algorithms, interactive analysis – suitable to your research needs!

We have developed a broad range of algorithms that are especially suitable for multi sensor systems. A complete set of these algorithms is available via our desktop software packages.
Check out MultiSens and MultiSens Analyzer. Embedding these algorithms into our instruments is done for specific applications in close collaboration with our customers.
Are you interested in a customized version of our analysis software specific to your needs? Talk to us!
Our in-house services include software development for Windows, embedded systems and mobile platforms.
Feature extraction

Feature extraction is used to extract a limited amount of values from a sensor response curve. Typical features are the maximum signal, baseline corrected signals, derivative signals or relative signals, but may also be the time needed to reach the maximum or other dynamic properties.

Filters

Filters are typically used to remove noise from measurement data. Filters may also be used to remove unwanted frequency domains from a sensor signal, adapt to changing baseline or convert data into other domains (e.g. time/frequency domains).

Multivariate data analysis

Multivariate data analysis algorithms are used to visualize data, classify or predict based on reference data. Typical algorithms that we offer are:

  • Principal Component Analysis (PCA)
  • Linear Discriminant Analysis (LDA)
  • Principal Component Regression (PCR)
  • Partial Least Squares (PLS)
  • Backpropagation Neural Networks