ArticleJournal of Raman spectroscopy : JRS2017
A machine learning framework to analyze hyperspectral stimulated Raman scattering microscopy images of expressed human meibum.
Article in Journal of Raman spectroscopy : JRS, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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Who cites it
11 citing papers in PubMed.
- Machine learning empowered coherent Raman imaging and analysis for biomedical applications.Communications engineering · 2025Review
- Chemical Distribution Uniformity Assessment of "Intra-Tablet" by Hyperspectral Raman Imaging Analysis.Pharmaceutical research · 2024Article
- On machine learning analysis of atomic force microscopy images for image classification, sample surface recognition.Physical chemistry chemical physics : PCCP · 2024Review
- Label-Free Imaging of Lipid Droplets in Prostate Cells Using Stimulated Raman Scattering Microscopy and Multivariate Analysis.Analytical chemistry · 2022Article
- Quantitative Stimulated Raman Scattering Microscopy: Promises and Pitfalls.Annual review of analytical chemistry (Palo Alto, Calif.) · 2022Review
- Surface enhanced Raman scattering for the multiplexed detection of pathogenic microorganisms: towards point-of-use applications.The Analyst · 2021Review
- Article
- Mammalian cell and tissue imaging using Raman and coherent Raman microscopy.Wiley interdisciplinary reviews. Systems biology and medicine · 2020Review
- Eicosapentaenoic acid (EPA) activates PPARγ signaling leading to cell cycle exit, lipid accumulation, and autophagy in human meibomian gland epithelial cells (hMGEC).The ocular surface · 2020Article
- Machine Learning based Analytical Framework for Automatic Hyperspectral Raman Analysis of Lithium-ion Battery Electrodes.Scientific reports · 2019Article
- Characterization of expressed human meibum using hyperspectral stimulated Raman scattering microscopy.The ocular surface · 2019Review
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Authors and funding
6 authors.
Funding
Abstract
We develop and discuss a methodology for batch-level analysis of hyperspectral stimulated Raman scattering (hsSRS) data sets of human meibum in the CH-stretching vibrational range. The analysis consists of two steps. The first step uses a training set (n=19) to determine chemically meaningful reference spectra that jointly constitute a basis set for the sample. This procedure makes use of batch-level vertex component analysis (VCA), followed by unsupervised k-means clustering to express the data set in terms of spectra that represent lipid and protein mixtures in changing proportions. The second step uses a random forest classifier to rapidly classify hsSRS stacks in terms of the pre-determined basis set. The overall procedure allows a rapid quantitative analysis of large hsSRS data sets, enabling a direct comparison among samples using a single set of reference spectra. We apply this procedure to assess 50 specimens of expressed human meibum, rich in both protein and lipid, and show that the batch-level analysis reveals marked variation among samples that potentially correlate with meibum health quality.
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