2.16 Principal Component Analysis for Spectroscopy(Pro)

PCA-Spec

Summary

This Principal Component Analysis for Spectroscopy app is used to perform principal component analysis for spectra (IR, Fluorescence, UV-Vis, Raman, etc.).

PCA for Spectroscopy App

Performing Principal Component Analysis for Chemometric Spectra Using Origin's App

Tutorial

1. Start this tutorial with the app Principal Component Analysis for Spectroscopy installed. If you have not installed this app, please click Add Apps button in Apps Gallery to open App Center to search and install the app.
2. Right-click on the app icon to select Show Samples Folder to find the sample file "PCASpecEx.opj". Drag-and-drop this file into Origin workspace to open it.

   

3. In this opened poject, the first sheet is the source worksheet. It contains spectra from twenty oil samples and a column "Time" to store the frequency data.

   

4. Highlight the columns from second column to the last column. Click the Principal Component Analysis for Spectroscopy icon in the Apps Gallery window to open the dialog. In the dialog's Input tab, select the (first) X column in Sheet1 as Frequency/Wavelength. All other Y columns have been selected as Spectra Data. Set Long Name for Spectra Names, and use Comments as Group Info.

   

5. In the Settings tab, choose the Covariance Matrix for Analyze option. And, set Number of Components to Extract to 10. If the Correlation Matrix option is chosen, each row for 20 samples would be normalized.

   

6. In the Plots tab, choose Sample 6 for the Reference Spectrum in Loading with Reference Spectrum Plot, and check the Loading Plot and Score Plot options.

   

7. Click the OK button and a report sheet, a result sheet and a plot data sheet are created. In the report sheet, the Eigenvalues table shows that the first four principal components explained 96% of total variance. The plot "Loading with Reference Spectrum Plot", "Loading Plot" and "Score Plot" will be created and stored in the report sheet.

   

8. You can double-click on the plots to enlarge them to check the details: