0.03 0.035 0.04 0.045 0.05 0.055

(1)

(2)

(3)

(4)

(5)

(6)

(7)

0 5 10 15 20 25 30 35 40 45 50 0

200 400 600 800 1000 1200 1400 1600 1800

yi (x) ! ylinei (line)

xi

(8)

(9)

(10)

(11)

(12)

(13)

(14)

(15)

(16)

(17)

(18)

(19)

(20)

(21)

(22)

(23)

O O

O

O O O

OO O

O

O O

PCA PLS

gradient of y

contours of y data cloud

(24)

(25)

(26)

(27)

(28)

(29)

(30)

(31)

(32)

(33)

(34)

(35)

(36)

(37)

1 2 3 4 5 6 7 8 9 1

1.2 1.4 1.6 1.8 2 2.2 2.4

PCR

Number of Components

PRESS/(Min PRESS)

Best number of components

1 2 3 4 5 6 7 8 9

1 1.1 1.2 1.3 1.4 1.5 1.6 1.7 1.8 1.9

PLS

Number of Components

PRESS/(Min PRESS)

Best number of components

(38)

1 2 3 4 0.025

0.03 0.035 0.04 0.045 0.05 0.055

RMSE Prediction RMSE fitting

PCR!5 Components

PLS!4 Components

CCA LS

(39)

(40)

0 2000 4000 6000 8000 10000 12000 14000 16000 18000 2.5

3 3.5

Samples

Magnitude

PCR Model Fitting ! 5 Components ! RMSEF = 0.03379

0 2000 4000 6000 8000 10000 12000 14000 16000 18000

!0.15

!0.1

!0.05 0 0.05 0.1 0.15 0.2

Samples

Residuals

0 2000 4000 6000 8000 10000 12000 14000 16000 18000

2.5 3 3.5

Samples

Magnitude

PLS Model Fitting ! 4 Components RMSEF = 0.03316

0 2000 4000 6000 8000 10000 12000 14000 16000 18000

0

Samples

Residuals

(41)

0 2000 4000 6000 8000 10000 12000 14000 16000 18000 2.5

3 3.5

Samples

Magnitude

CCA Model Fitting ! RMSEF = 0.02988

0 2000 4000 6000 8000 10000 12000 14000 16000 18000

0

Samples

Residuals

0 500 1000 1500 2000 2500 3000 3500 4000

2.85 2.9 2.95 3 3.05 3.1 3.15

Samples

Magnitude

PCR Model Validation ! 5 Components ! RMSEP = 0.04308

0 500 1000 1500 2000 2500 3000 3500 4000

!0.15

!0.1

!0.05 0 0.05 0.1 0.15

Samples

Residuals

0 500 1000 1500 2000 2500 3000 3500 4000

2.85 2.9 2.95 3 3.05 3.1 3.15

PLS Model Validation ! 4 Components ! RMSEP = 0.04106

Samples

Magnitude

0 500 1000 1500 2000 2500 3000 3500 4000

!0.15

!0.1

!0.05 0 0.05 0.1 0.15

Samples

Residuals

(42)

0 500 1000 1500 2000 2500 3000 3500 4000 2.5

3 3.5

Samples

Magnitude

CCA Model Validation ! RMSEP = 0.05728

0 500 1000 1500 2000 2500 3000 3500 4000

!0.5 0 0.5

Samples

Residuals

2.9 3 3.1

!0.2 0

0.2 PCR Measured Output Vs. Residuals

Measured Output

Residuals

(43)

2.9 3 3.1

!0.2 0 0.2

PLS Measured Output Vs. Residuals

Measured Output

Residuals

2.9 3 3.1

!0.2 0

0.2 CCA Measured Output Vs. Residuals

Measured Output

Residuals

(44)

2.85 2.9 2.95 3 3.05 3.1 3.15 3.2 3.25 2.85

2.9 2.95 3 3.05 3.1 3.15 3.2

3.25 PCR ! Measured Output Vs. Predicted & Fitted Output

Measured Output

Predicted (dark) & Fitted (soft) Output

2.85 2.9 2.95 3 3.05 3.1 3.15 3.2 3.25

PLS ! Measured Output Vs. Predicted & Fitted Output

Measured Output

Predicted (dark) & Fitted (soft) Output

(45)

2.85 2.9 2.95 3 3.05 3.1 3.15 3.2 3.25 2.8

2.85 2.9 2.95 3 3.05 3.1 3.15

3.2 CCA ! Measured Output Vs. Predicted & Fitted Output

Measured Output

Predicted (dark) & Fitted (soft) Output

(46)

(47)

(48)

(49)

(50)

(51)

(52)