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Status Classification of MVC Objects

Status Classification of MVC Objects. Oxana Rodionova & Alexey Pomerantsev Semenov Institute of Chemical Physics Russian Chemometric Society Moscow. Plan. Introduction Main Features and Definitions of SIC-method Projection Methods (PCR/PLS) and the SIC-method ( examples) Conclusion.

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Status Classification of MVC Objects

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  1. Status Classification of MVC Objects Oxana Rodionova & Alexey Pomerantsev Semenov Institute of Chemical Physics Russian Chemometric Society Moscow

  2. Plan • Introduction • Main Features and Definitions of SIC-method • Projection Methods (PCR/PLS) and the SIC-method ( examples) • Conclusion

  3. 401025 Visualization Water with Oil X- acoustic spectra after FFT transformation (1024 variables) Y- concentration of oil in water (ppm), specially prepared samples. 40 Calibration samples 40 Test samples

  4. Visualization. “2-D Projection Windows” Objectspace Variable space Groups/Clusters Outliers

  5. Simple interval calculation (SIC-method) Gives the result of prediction directly in an interval form Provides wide possibilities for leverage-type object status classification

  6. Normal (–) distribution Finite (–) distributions Main Assumption of SIC-method All errors are limited.

  7. The Region of Possible Values (RPV)

  8. SIC Prediction V-prediction interval U-test interval

  9. bsic RMSEC SIC main steps

  10. SIC-Residual SIC-Leverage SIC Object Status Theory. Definitions

  11. SIC Object Status Plot

  12. Insiders Absolute outsiders Outliers Outsiders Typical SIC- Object Status Plot

  13. PCR / PLS SIC method RPV Scores Prediction Intervals Loadings Boundary Samples Insiders, Outsiders Influence Plots ... ... SIC & Projection Methods

  14. Initial Data Set {X,Y} PLS/PCR model Fixed number of PCs SIC-modeling RESULTS RESULTS Comparison

  15. Water with Oil (Data) X- acoustic spectra after FFT transformation (1024 variables) Y- concentration of oil in water (ppm), specially prepared samples. 40 Calibration samples 40 Test samples PLS-model, 2 PCs SIC-modeling bmin= 0.12 bsic=0.225 8 Boundary Samples y=log(1+yinitial)

  16. SIC Object Status Plot PCs=2 PLS Influence Plot PCs=2 PLS (T1-U1) Water with Oil. Calibration PLS (T2-U2)

  17. Water with Oil. Test

  18. Subset {X,Y} L samples Calibration set 1 {X,Y} N samples Calibration set 1 {X,Y} N samples Quality of prediction ? Calibration set 2 {X,Y} N-L-samples L=? Boundary Samples andRepresentative Subset Selection

  19. PREDICTION Whole Wheat Samples X- NIR Spectra of Whole Wheat (118 wave lengths) Y- moisture content N=139 Calibration Set Data pre- processed. PLS-model, 4PCs SIC-modeling bmin= 1.03 bsic=1.53 Calibration Set-Boundary Set N-Bs(b) Boundary Subset Bs(b)

  20. Whole Wheat Samples

  21. Protein Moisture Gluten 6 PCs RMSEP=0.321 4 PCs RMSEP=0.326 6 PCs RMSEP=0.381 43 BS 41 BS 23 BS Whole Wheat Samples

  22. L=11

  23. Analysis of the Test Set Objects Norwegian Cruise Ship X- Ship/weather characteristics (7 variables) Y- Fuel consumption. 27 Calibration samples 18 Test samples PLS-model, 2PCs SIC-modeling bmin= 28.4 bsic=64.7 8 Boundary Samples

  24. SIC Object Status Plot PCs=2 PLS Influence Plot PCs=2 Ship /Fuel (Calibration) PLS (T2-U2) PLS (T1-U1)

  25. SIC Object Status Plot PCs=2 Ship /Fuel (Test) PLS (T1-U1) PLS (T2-U2)

  26. SIC-Leverage Outlier detection in prediction

  27. DSC Example X- Oxidation Initial Temperature (OIT) at different heating rates. Y- Long Term Heating Aging (days) Total number of samples (n) =15 Number of variable (p) =5 Calibration set = 11 samples Testing set = 4 samples y-data were pre-processed. Y=XaY=Tb PC’s=2 bmin=0.385 bsic=0.47

  28. DSC Example T4 Prediction y Object status plot

  29. The region of absolute outsiders Let D be a set in the X space defined as a linear combination of weighted calibration predictors xi Then all absolute outsiders are to be found exclusively outside this region D. The border of absolute outsiders

  30. Border of Absolute Outsider and Convex Hull DSC Example Object status plot Score plot J.A. Fernandez Pierna, F.Wahl, O.E. de Noord, D.L.Massart Methods of outlier detection in prediction, ChemoLab 63 (2002) 27-39

  31. Border of Absolute Outsider and Convex Hull Norwegian Cruise Ship Object status plot. Test samples Score plot

  32. Influential objects Quality of test objects ? ? Boundary samples Object status plot Outlier detection ? Absolute outsiders Calibration set Test set New objects

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