1. Multi-block analysis of olive oil data
Datasets: MIR, NIR, AG, C16_C24 & Origins

2. Data pretreatment
1) Eliminate samples with NaNs -reduced to 106 samples in each dataset
2) MIR spectra : cut out CO2 peak
3) NIR spectra : use first 1000 points
4) NIR & MIR spectra : SNV transform
5) NIR & MIR spectra : 43-point Savitsky-Golay derivative
6) C16_C24 (18,4) in reduced data set changed from 0.7 to 0.07

3. MIR NIR

4. Variable selection using PLS_ComDim
Between MIR & C16_C24 :
171 X blocks (106 x 5)
1 Y block (106 x 14)
6 Common Dimensions
Plot scores vs. concentrations
Saliences
R scores/concentration

8. Variable selection using PLS_ComDim
Between NIR & C16_C24 :
180 X blocks (106 x 5)
1 Y block (106 x 14)
6 Common Dimensions
Plot scores vs. concentrations
Saliences
R scores/concentration

12. Variable selection using PLS_ComDim
Between MIR & AG :
171 X blocks (106 x 5)
1 Y block (106 x 20)
6 Common Dimensions
Plot scores vs. concentrations
Saliences
R scores/concentration

16. Variable selection using PLS_ComDim
Between NIR & AG :
180 X blocks (106 x 5)
1 Y block (106 x 20)
6 Common Dimensions
Plot scores vs. concentration
Saliences
R scores/concentration

20. Variable selection using PLS_ComDim
Between MIR & Origin :
171 X blocks (106 x 5)
1 Y block (106 x 3)
6 Common Dimensions
Plot scores vs. concentrations
Saliences
R scores/concentration

25. Variable selection using PLS_ComDim
Between NIR & Origin :
180 X blocks (106 x 5)
1 Y block (106 x 3)
6 Common Dimensions
Plot scores vs. concentration
Saliences
R scores/concentration

30. Variable selection using PLS_ComDim
Between subsets of MIR1, MIR2, NIR & Origin:
3 X blocks (106 x 200; 100; 356)
1 Y block (106 x 3)
6 Common Dimensions
Plot scores vs. concentration
Saliences
R scores/concentration

36. Variable selection using PLS_ComDim
Between subsets of MIR1, MIR2, NIR, C16_C24, AG & Origin:
5 X blocks (106 x 200; 100; 356; 14; 20)
1 Y block (106 x 3)
6 Common Dimensions
C16_C24, AG were not scaled
Plot scores vs. concentration
Saliences
R scores/concentration

42. Confusion matrices (3 LVs)
MIR Confusions =
NIR Confusions =
MIRNIR Confusions =
All Confusions =
PLSDA Confusions =