Examination of beauty ingredient distribution in the human skin by time-of-flight secondary ion mass spectrometry

Abstract

In this study, the authors evaluated the distribution of low-abundance beauty ingredients in human skin tissues. The distribution of collagen tripeptide, a beauty ingredient, in the human skin was evaluated by applying multivariate curve resolution (MCR) to the time-of-flight secondary ion mass spectrometry mapping data, including reference information. The intensity of the secondary ion peaks was increased by the accumulation of secondary ion intensity in the depth direction obtained by argon cluster sputtering. Consequently, the collagen tripeptide distribution in the skin was evaluated by separating it from collagen peptide, although the conventional analysis was difficult because of the dilution of the collagen tripeptide in the skin. Additionally, the distribution of the collagen tripeptide in the skin may be determined with an autoencoder. In this study, the distribution of small amounts of beauty ingredients (the collagen tripeptide) in the skin was observed by integrating the secondary ion intensity in the depth direction with Ar cluster sputtering and applying MCR or autoencoder.

Quantitative analysis of ToF‐SIMS data of a two organic compound mixture using an autoencoder and simple artificial neural networks

“…An autoencoder provides data features without prior information. 20,21,24 The simplest autoencoder has just one hidden layer. The encoder and decoder weights are adjusted to reconstruct the input data at the output layer.…”

“…14 Machine and deep learning methods have been employed for interpreting complex mass spectral and imaging data. [15][16][17][18][19][20][21][22][23][24][25] For example, an autoencoder is an ANN-based unsupervised method. It has been applied to interpret complex ToF-SIMS data 20,21,24 and to extract important sample features.…”

“…[15][16][17][18][19][20][21][22][23][24][25] For example, an autoencoder is an ANN-based unsupervised method. It has been applied to interpret complex ToF-SIMS data 20,21,24 and to extract important sample features.…”

Development of Machine-Learning Techniques for Time-of-Flight Secondary Ion Mass Spectrometry Spectral Analysis: Application for the Identification of Silane Coupling Agents in Multicomponent Films

“…Recently, machine learning was applied to analyze ToF-SIMS spectra to improve their interpretability. In particular, unsupervised learning methods such as principal component analysis, − multivariate curve resolution, − non-negative matrix factorization, , the self-organizing map, , and the autencoder , have been useful in detecting the distributions of specific components in a mixture sample. However, the identification of components in a uniform mixture film without distribution remains an issue.…”

Evaluation of Time-of-Flight Secondary Ion Mass Spectrometry Spectra of Peptides by Random Forest with Amino Acid Labels: Results from a Versailles Project on Advanced Materials and Standards Interlaboratory Study

“…Data analysis methods such as multivariate analysis have been applied to TOF-SIMS data to manage this issue. − Multivariate analysis techniques such as principal component analysis (PCA) and non-negative matrix factorization (NMF) are powerful tools for the interpretation of TOF-SIMS data. − PCA is one of the best unsupervised learning methods to understand the outline of an unknown TOF-SIMS data. Moreover, new methods including machine learning and deep leaning techniques were also applied to TOF-SIMS data to classify highly similar materials. − However, the identification of mass peaks in TOF-SIMS spectra to identify completely unknown samples remains one of the most important issues.…”