New Software Tool Generates Data Analysis for Complex ToF-SIMS

The Science  

Time-of-flight secondary ion mass spectrometry (ToF-SIMS) is a specialized mass spectrometry technique. It can be used to identify the composition of solid surfaces, providing elemental, isotopic, and molecular information with part per million (ppm) sensitivity. ToF-SIMS spectra, however, may contain numerous individual ion signals, which can be hard to distinguish without dedicated statistical tools. Statistical techniques, such as principal component analysis (PCA), have been widely used to visualize differences of ToF-SIMS spectra and have shown great success. But chemically understanding of these differences, especially for less experienced users, has long been challenging. To solve this issue, a new and flexible software tool based on Python was developed where artificial intelligence (AI)–machine learning functions are incorporated to help users chemically understand complex spectra generated from ToF-SIMS. In this work, AI–machine leaning functions were incorporated to a traditional PCA analysis tool, showing great progress in resolving this issue.

Pacific Northwest National Laboratory / Freepik: A new and flexible software tool based on the Python computing language was developed by Pacific Northwest National Laboratory researchers where artificial intelligence–machine learning functions are incorporated to help users chemically understand complex spectra generated from time-of-flight secondary ion mass spectrometry.

The Impact 

Compared to other software packages available, the advantage of the new Python-based package is that a detailed Microsoft Word format data analysis report can be automatically generated, greatly facilitating less experienced users in understanding chemical differences among samples. This saves users a lot of time in data report processing. The new package is free, open-source software that comes with a detailed manual, making it user-friendly and straightforward to understand. The new package is flexible, powerful, and extensible. For example, most exported parameters can be customized. Additionally, some basic AI functions have been integrated into the software package, and many other AI/machine learning functions in the Python system are available for exploring more powerful capabilities. In principle, the software package can be used to treat many types of spectra data, such as Fourier-transform ion cyclotron resonance mass spectrometry (FTICR-MS), matrix-assisted laser desorption/ionization mass spectrometry (MALDI-MS), infrared spectroscopy (IR), and X-ray photoelectron spectroscopy (XPS). 

Summary 

So far, several software tools have been developed for PCA of ToF-SIMS spectra; however, none of them are freely available. Such a situation leads to some difficulties in extending applications of PCA to various research fields. More importantly, it has long been challenging for common researchers to understand PCA plots and extract chemical differences among samples. In this work, a team of researchers from Pacific Northwest National Laboratory and the Environmental Molecular Sciences Laboratory, a Department of Energy Office of Science user facility, developed a new and flexible software tool called “Advanced Spectra PCA Toolbox.” The software is based on Python for PCA of complex ToF-SIMS spectra and features an easy-to-read manual. It can generate data analysis reports automatically to explain chemical differences among samples, helping less experienced researchers easily understand tricky PCA results. Moreover, it is expandable and compatible with AI/machine learning functions. Pure goethite and different lignin-adsorbed goethite samples were used as a model system to demonstrate the software tool, showing that it can be readily used in complex spectra data processing. Our software tool is open-source, flexible, and expandable. The research team expects this open-source tool will benefit not only the ToF-SIMS community, but also other spectrometry techniques (such as FTICR-MS, MALDI-MS, IR, and XPS). 

PNNL Contact

Zihua Zhu, Pacific Northwest National Laboratory and Environmental Molecular Sciences Laboratory, [email protected] 

Funding 

This research was supported by the Laboratory Directed Research and Development program in the Earth and Biological Sciences Directorate at Pacific Northwest National Laboratory and was performed on a project award from the Environmental Molecular Sciences Laboratory, a Department of Energy Office of Science user facility sponsored by the Biological and Environmental Research program.