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The orthogonal projection approach (OPA), a stepwise approach based on an orthogonalization algorithm, is proposed. The performance of OPA for the assessment of peak purity in HPLC-DAD is described and compared with that of SIMPLISMA. The occurrence of artifacts in both approaches under nonideal situations is discussed.

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SUMMARYA modification of a technique proposed by Lorber and Kowalski for the estimation of prediction errors is presented. The method is applied to five data sets. The results show that for some data sets the estimated prediction errors are close to the actual prediction errors for samples within the calibration range, while samples outside the calibration range must be background corrected before quantification of the prediction error.