Time-of-flight secondary ion mass spectrometry (TOF-SIMS) is extensively employed for the structural analysis of the outermost surfaces of organic materials, including biological materials, because it provides detailed compositional information and enables high-spatial-resolution chemical mapping. In this study, a combination of TOF-SIMS and data analysis was employed to evaluate biological materials composed of numerous proteins, including unknown ones. To interpret complicated TOF-SIMS data of human hair, an autoencoder, a dimensionality reduction method based on artificial neural networks, was applied. Autoencoders can be used to perform nonlinear analysis; therefore, they are more suitable than principal component analysis (PCA) for analyzing TOF-SIMS data, which are influenced by the matrix effect. As a model sample data, the TOF-SIMS depth profile of human hair, acquired via argon gas cluster ion beam sputtering and Bi₃²⁺ primary ion beam, was employed. Useful information, including the characteristic distributions of amino acids and permeated surfactants on the outermost surface of the hair, was extracted from the results obtained from the autoencoder. Furthermore, the autoencoder extracted more detailed features than did PCA. Therefore, autoencoders can become a powerful tool for TOF-SIMS data analysis.

中文翻译：

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使用无监督人工神经网络对头发样品进行飞行时间二次离子质谱分析。

飞行时间二次离子质谱（TOF-SIMS）被广泛用于有机材料（包括生物材料）最外表面的结构分析，因为它提供了详细的组成信息并支持高空间分辨率的化学作图。在这项研究中，TOF-SIMS和数据分析相结合被用来评估由许多蛋白质组成的生物材料，包括未知的蛋白质。为了解释复杂的人发TOF-SIMS数据，应用了一种自动编码器，一种基于人工神经网络的降维方法。自动编码器可用于执行非线性分析。因此，它们比主成分分析（PCA）更适合于分析受矩阵效应影响的TOF-SIMS数据。作为模型样本数据，_使用3 ²⁺初级离子束。从自动编码器获得的结果中提取了有用的信息，包括氨基酸和渗透性表面活性剂在头发最外层表面的特征分布。此外，与PCA相比，自动编码器提取了更详细的功能。因此，自动编码器可以成为TOF-SIMS数据分析的强大工具。