Table 4.

Summary of the modeling quality of OPLS analysis.

Name	Noa	R 2 X cum b	R 2 Y cum c	Q 2 Y cum d
1A	1P + 1Oe	0.24	0.97	0.91
1B	1P + 1O	0.16	0.96	0.78
1C	1P + 1O	0.17	0.98	0.93
2A	1P + 2O	0.50	0.89	0.70
2B	1P + 3O	0.56	0.90	0.48
2C	1P + 3O	0.49	0.91	0.57

^aNo represents the number of components.

^b,c R ² X _cum and R ² Y _cum represent the cumulative sum of squares (SSs) of all the X's and Y's explained by all extracted components.

^d Q ² Y _cum is an estimate of how well the model predicts the Y's.

^e1P + 1O: one predictive component and one orthogonal component for establishing the OPLS model.