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A machine learning framework to analyze hyperspectral stimulated Raman scattering microscopy images of expressed human meibum
Author(s) -
AlfonsoGarcía Alba,
Paugh Jerry,
Farid Marjan,
Garg Sumit,
Jester James,
Potma Eric
Publication year - 2017
Publication title -
journal of raman spectroscopy
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.748
H-Index - 110
eISSN - 1097-4555
pISSN - 0377-0486
DOI - 10.1002/jrs.5118
Subject(s) - hyperspectral imaging , principal component analysis , cluster analysis , random forest , pattern recognition (psychology) , classifier (uml) , artificial intelligence , raman scattering , data set , set (abstract data type) , raman spectroscopy , basis (linear algebra) , microscopy , biological system , computer science , chemistry , analytical chemistry (journal) , mathematics , chromatography , optics , physics , biology , geometry , programming language
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, 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. Copyright © 2017 John Wiley & Sons, Ltd.