What Does a Negative Q² Value Indicate in 2D Score Plots of PLS-DA/OPLS-DA?
In the two-dimensional score plots generated by PLS-DA (Partial Least Squares Discriminant Analysis) or OPLS-DA (Orthogonal Partial Least Squares Discriminant Analysis), the Q² value (predictive squared correlation coefficient) evaluates the model’s predictive performance based on cross-validation. Ideally, a Q² value close to 1 indicates strong predictive ability. A negative Q² value implies inadequate predictive performance, typically suggesting that the model fails to capture the systematic variation relevant to class separation, or that overfitting may have occurred. This indicates that the model's capacity to generalize or make accurate predictions on unseen data is compromised. In practical applications, encountering a negative Q² value warrants a thorough re-evaluation of model selection, parameter optimization, and the validity and integrity of the dataset used.
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