The advantages of applying multivariate analysis to mass spectrometry imaging (MSI) data have been thoroughly demonstrated in recent decades. The identification and visualization of complex relationships between pixels in a hyperspectral data set can provide unique insights into the underlying surface chemistry. It is now recognized that most MSI data contain nonlinear relationships, which has led to increased application of machine learning approaches. Previously, we exemplified the use of the self-organizing map (SOM), a type of artificial neural network, for analyzing time-of-flight secondary ion mass spectrometry (TOF-SIMS) hyperspectral images. Recently, we developed a novel methodology, SOM-relational perspective mapping (RPM), which incorporates the algorithm RPM to improve visualization of the SOM for 2D TOF-SIMS images. Here, we use SOM-RPM to characterize and interpret 3D TOF-SIMS depth profile data, voxel-by-voxel. An organic Irganox^™ multilayer standard sample was depth profiled using TOF-SIMS, and SOM-RPM was used to create 3D similarity maps of the depth-profiled sample, in which the mass spectral similarity of individual voxels is modeled with color similarity. We used this similarity map to segment the data into spatial features, demonstrating that the unsupervised method meaningfully differentiated between Irganox-3114 and Irganox-1010 nanometer-thin multilayer films. The method also identified unique clusters at the surface associated with environmental exposure and sample degradation. Key fragment ions characteristic of each cluster were identified, tying clusters to their underlying chemistries. SOM-RPM has the demonstrable ability to reduce vast data sets to simple 3D visualizations that can be used for clustering data and visualizing the complex relationships within.

中文翻译：

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使用自组织映射关系透视映射分析 3D 高光谱 TOF-SIMS 深度剖面数据

近几十年来，将多元分析应用于质谱成像 (MSI) 数据的优势已得到充分证明。高光谱数据集中像素之间复杂关系的识别和可视化可以提供对底层表面化学的独特见解。现在认识到大多数 MSI 数据包含非线性关系，这导致机器学习方法的应用增加。之前，我们举例说明了使用自组织图 (SOM)，一种人工神经网络，用于分析飞行时间二次离子质谱 (TOF-SIMS) 高光谱图像。最近，我们开发了一种新颖的方法，即 SOM 关系透视映射 (RPM)，该方法结合了 RPM 算法以改进 2D TOF-SIMS 图像的 SOM 可视化。这里，我们使用 SOM-RPM 来表征和解释 3D TOF-SIMS 深度剖面数据，逐个体素。有机 Irganox^™使用 TOF-SIMS 对多层标准样品进行深度剖析，并使用 SOM-RPM 创建深度剖析样品的 3D 相似性图，其中使用颜色相似性对单个体素的质谱相似性进行建模。我们使用此相似性图将数据分割为空间特征，证明无监督方法有效区分了 Irganox-3114 和 Irganox-1010 纳米多层薄膜。该方法还确定了与环境暴露和样品降解相关的表面独特的簇。确定了每个簇的关键碎片离子特征，将簇与其基础化学联系起来。SOM-RPM 具有显着的能力，可将大量数据集简化为简单的 3D 可视化，可用于对数据进行聚类并可视化其中的复杂关系。