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Spectra_​analysis-PCA

The input is a CSV file with 101 samples measurements of the infrared spectra of a mix of 3species with unknown concentration.The goal is to decompose the spectra in order to obtain the main component analysis andcompare it with known spectra of different species measured in pure solutions. Which is the main species of the mixture? Upload all themeasurementsfrom a CSV file. Plot all themeasurements atonce.Species areindistinguishable. The PCA Compute node calculates thecovariance matrix of the input datacolumns and its eigenvectors, identifyingthe directions of maximal variance in thedata space. The node outputs thecovariance matrix, the PCA model, and thePCA spectral decomposition of the originaldata columns along the eigenvectors. The PCA Applynode transforms adata row from theoriginal space intothe new PCspace, using theeigenvectorprojections in thePCA model. Plot the maincomponent of thespectra vs thewavelenghts. Read CSVCompute PCA from Col1Apply PCA model from Col1Keep 2 main components99.8%Plot Col0 (x) vs PCA-1Filter only Col0RejoinPlot all spectraFilter Col0 (x values)CSV Reader PCA Compute PCA Apply Line Plot Column Filter Joiner Line Plot Column Filter The input is a CSV file with 101 samples measurements of the infrared spectra of a mix of 3species with unknown concentration.The goal is to decompose the spectra in order to obtain the main component analysis andcompare it with known spectra of different species measured in pure solutions. Which is the main species of the mixture? Upload all themeasurementsfrom a CSV file. Plot all themeasurements atonce.Species areindistinguishable. The PCA Compute node calculates thecovariance matrix of the input datacolumns and its eigenvectors, identifyingthe directions of maximal variance in thedata space. The node outputs thecovariance matrix, the PCA model, and thePCA spectral decomposition of the originaldata columns along the eigenvectors. The PCA Applynode transforms adata row from theoriginal space intothe new PCspace, using theeigenvectorprojections in thePCA model. Plot the maincomponent of thespectra vs thewavelenghts. Read CSVCompute PCA from Col1Apply PCA model from Col1Keep 2 main components99.8%Plot Col0 (x) vs PCA-1Filter only Col0RejoinPlot all spectraFilter Col0 (x values)CSV Reader PCA Compute PCA Apply Line Plot Column Filter Joiner Line Plot Column Filter

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Created by: dariatombolelli
Created at: 2019-12-12
On NodePit since: 2019-12-14
Last update: 2024-11-21
Created with KNIME version: v4.1.0

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