Review of partial least squares regression prediction error in Unscrambler

Three expressions for estimating the prediction uncertainty in partial least squares regression (PLSR) have been evaluated, using synthetic datasets and Monte Carlo simulations. The simulations revealed that the original expression used in the old Unscrambler program needed a correcting factor, as pointed out in a recently published article. With low noise levels, the corrected uncertainty estimator used in the latest version of Unscrambler (7.0) performed reasonably well as an estimator of the actual prediction error. A third estimate proposed in another recently published article seemed to lack a term to differentiate between the prediction objects, and thus did not perform satisfactorily.