Early morning on the 6th August 2012 (GMT), NASA’s largest and most sophisticated ever rover, ’Curiosity’, landed on Mars. We’re very proud that The Unscrambler® X software will play a small but important part in this exciting mission. Officially named the Mars Science Laboratory (MSL), Curiosity’ will explore the mineral-rich Gale Crater region of Mars in an attempt to find traces of water that may have supported life on the red planet.
To analyze the data collected by Curiosity, advanced multivariate data analysis models will be used, developed using The Unscrambler® X software. Using a drill combined with a powerful laser and other equipment, the Curiosity will try to understand the chemical composition of the rocks and soil.
A technique known as Laser-induced breakdown spectroscopy (LIBS) will be used to analyze the rocks and soil for traces of water. A laser is focused onto a sample (solid, liquid or gas) to create a plasma. Emissions from the plasma are then collected and analyzed spectroscopically and the atomic spectral lines are used to determine elemental composition. Multivariate analysis is applied to the LIBS data to classify samples based on their compositional differences.
LIBS data will be collected by the Rover at a distance of up to 7 meters from the Martian samples and multivariate data analysis models developed using The Unscrambler® X will be used by scientists back on Earth to classify the rocks based on their composition. LIBS data, with over 6,000 variables per sample, are highly multivariate. The use of Principal Component Analysis (PCA), a powerful multivariate analytical tool, allows for a rapid visualization of sample groupings, and from this analysis, classification models can be developed to identify Mars rocks in situ during exploration.
Because the LIBS approach collects an enormous amount of data, equally powerful statistical software was required to analyze the data. The Unscrambler® X was chosen to develop the analysis methodology for its leading multivariate capabilities which are ideally suited to analyzing large or complex data sets.
You can read a more detail about LIBS and the analytical methods used in our short case study.