Surface and Microanalysis Science Division, National Institute of Standards and Technology, Gaithersburg, MD, USA.
Analyst. 2012 Aug 7;137(15):3479-87. doi: 10.1039/c2an16122b. Epub 2012 May 8.
We present a novel method for correlating and classifying ion-specific time-of-flight secondary ion mass spectrometry (ToF-SIMS) images within a multispectral dataset by grouping images with similar pixel intensity distributions. Binary centroid images are created by employing a k-means-based custom algorithm. Centroid images are compared to grayscale SIMS images using a newly developed correlation method that assigns the SIMS images to classes that have similar spatial (rather than spectral) patterns. Image features of both large and small spatial extent are identified without the need for image pre-processing, such as normalization or fixed-range mass-binning. A subsequent classification step tracks the class assignment of SIMS images over multiple iterations of increasing n classes per iteration, providing information about groups of images that have similar chemistry. Details are discussed while presenting data acquired with ToF-SIMS on a model sample of laser-printed inks. This approach can lead to the identification of distinct ion-specific chemistries for mass spectral imaging by ToF-SIMS, as well as matrix-assisted laser desorption ionization (MALDI), and desorption electrospray ionization (DESI).
我们提出了一种新的方法，通过将具有相似像素强度分布的图像进行分组，来关联和分类多光谱数据集内的离子特异性飞行时间二次离子质谱（ToF-SIMS）图像。通过使用基于 k-均值的自定义算法创建二值质心图像。使用新开发的相关方法将质心图像与灰度 SIMS 图像进行比较，该方法将 SIMS 图像分配到具有相似空间（而不是光谱）模式的类别中。无需图像预处理（例如归一化或固定范围质量分箱）即可识别具有大空间和小空间范围的图像特征。在每次迭代中增加 n 个类别的多次迭代中，后续的分类步骤跟踪 SIMS 图像的分类分配，提供有关具有相似化学性质的图像组的信息。在介绍使用 ToF-SIMS 在激光打印油墨模型样品上获得的数据时，将详细讨论这些方法。这种方法可以通过 ToF-SIMS、基质辅助激光解吸电离（MALDI）和解吸电喷雾电离（DESI）来识别特定的离子特异性质谱成像化学物质。
