Resolving Water, Proteins, and Lipids from In Vivo Confocal Raman Spectra of Stratum Corneum through a Chemometric Approach
| Autor: | Yueqing Niu, Zigang Xu, Karl Shiqing Wei, Ning Su, Lesheng Zhang, Tom Cambron, Hongyan Zheng, Paula J. Ray |
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| Rok vydání: | 2019 |
| Předmět: |
Adolescent
General Chemical Engineering Confocal Spectrum Analysis Raman General Biochemistry Genetics and Molecular Biology Chemometrics symbols.namesake Humans Preprocessor Child Skin Principal Component Analysis Data collection General Immunology and Microbiology business.industry General Neuroscience Resolution (electron density) Proteins Water Pattern recognition Lipids Forearm ComputingMethodologies_PATTERNRECOGNITION Child Preschool Multivariate Analysis Principal component analysis Outlier symbols Artificial intelligence Raman spectroscopy business |
| Zdroj: | Journal of Visualized Experiments. |
| ISSN: | 1940-087X |
| Popis: | Development of this in vivo confocal Raman spectroscopic method enables the direct measurement of water, proteins, and lipids with depth resolution in human subjects. This information is very important for skin-related diseases and characterizing skin care product performance. This protocol illustrates a method for confocal Raman spectra collection and the subsequent analysis of the spectral dataset leveraging chemometrics. The goal of this method is to establish a standard protocol for data collection and provide general guidance for data analysis. Preprocessing (e.g., removal of outlier spectra) is a critical step when processing large datasets from clinical studies. As an example, we provide guidance based on prior knowledge of a dataset to identify the types of outliers and develop specific strategies to remove them. A principal component analysis is performed, and the loading spectra are compared with spectra from reference materials to select the number of components used in the final multivariate curve resolution (MCR) analysis. This approach is successful for extracting meaningful information from a large spectral dataset. |
| Databáze: | OpenAIRE |
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