Abstract
High surface sensitivity and lateral resolution imaging make time‐of‐flight secondary ion mass spectrometry (ToF‐SIMS) a unique and powerful tool for biological analysis. However, with the leaps forward made in the capabilities of the ToF‐SIMS instrumentation, the data being recorded from these instruments has dramatically increased. Unfortunately, with these large, often complex, datasets, a bottleneck appears in their processing and interpretation. Here, an application of peak picking is described and applied to ToF‐SIMS images allowing for large compression of data, noise removal and improved contrast, while retaining a high percentage of the original signal. Peak picking is performed to locate peaks within ToF‐SIMS data. By using this information, signal arising from the same distribution can be summed and overlapping signals separated. As a result, the data size and complexity can be dramatically reduced. This method also acts as an effective noise filter, discarding unwanted noise from the data set. Peak picking and separation are evaluated against the conventional methods of mass binning and manually selecting regions of a peak to image on a model data set. Copyright © 2012 John Wiley & Sons, Ltd.
Highlights
The size of ToF-SIMS datasets has become an issue in recent times
Peak picking and separation is evaluated against the conventional methods of mass binning and manually selecting regions of a peak to image on a model data set
This paper describes a method of compression proposed as an alternative to the classical practices of binning data, where mass resolution is lost, and manual selection of peaks to image
Summary
The size of ToF-SIMS datasets has become an issue in recent times. Though computers are increasing in capability, the data sets being acquired from the SIMS instrumentation have been increasing dramatically. This paper describes a method of compression proposed as an alternative to the classical practices of binning data, where mass resolution is lost, and manual selection of peaks to image. We use the term ‘peak picking’ to describe the exercise of determining a ‘discrete spectrum from continuous data’‡ This is in contrast to arbitrary peak selection based on a priori knowledge of the sample [4] and/or by applying a simple intensity threshold to the data. In this paper peak picking is applied to locate peaks, fit a peak shape and use this information to perform separation of overlapping signal and selective alignment of pixel data to achieve compression of an image or an image stack. A peak list can be formulated and each of the constituent pixel spectra can be queried as to their values under the peak's distribution
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