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The model obtained an R2p of 0.93 and RPD of 4.09, indicating that the model is adequate for analytical purposes.

The discrimination ability model obtained an AUC (95%% CI) 0.81 (0.72 to 0.898).

The discrimination ability model obtained an AUC (95 % CI) of 0.808 (0.72 to 0.89).

Also, the two factor model obtained an unacceptable standardized root mean residual model fit (RMSR = .09), and also a lower percentage of explained variance (53.99%).

On the training data, our optimal model obtained an accuracy of 80.5%.

When omitting the latter and fitting the local dependence, a unidimensional model obtained an appropriate fit to the data = 0.06 0.05-0.08)).

Based on the model obtained a feedforward as well as a feedback controller were designed.

From the model obtained, a milder reaction condition with a theoretical 98.0% of FAMEs content was created and tested to evaluate its accuracy.

Confirmatory factor analyses showed that the one-factor model obtained a good fit for the GPQ-18 and acceptable for the GPQ-9.

The corrected model obtained a statistical significant result explaining 13.3% of the variance but with moderate effect.

The FOM PCH based regression model obtained a coefficient of determination higher than the other Markov-chain based PCH models.