Time-of-flight secondary ion mass spectrometry analysis of hair samples using unsupervised artificial neural network

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.