Table 3.

Predictive in-line monitoring accuracy for five regression models.

Model	Q2	Q	MSE	MAE	MAPE
Aggregates (HMW%)
KNN	0.86	0.93	0.37	0.53	20%
CNN	0.81	0.90	0.75	0.76	27%
SVR	0.81	0.90	1.36	1.00	24%
PCR	0.09	0.31	3.39	1.47	31%
PLS	0.29	0.53	4.65	1.78	39%
Fragments (LMW%)
KNN	0.65	0.80	0.33	0.35	13%
CNN	0.68	0.82	0.53	0.58	34%
SVR	0.66	0.81	0.35	0.44	15%
PCR	0.12	0.34	0.78	0.75	24%
PLS	0.49	0.70	0.74	0.74	24%

Following the results shown in Figure 2a, different metrics of model predictive performance are shown, including Q: the predictive correlation coefficient, MSE: mean squared error, MAE: mean absolute error, MAPE: mean absolute percent error.