The application of piecewise direct standardisation with variable selection to the correction of drift in inductively coupled atomic emission spectrometry

When an instrument response is measured over a period of time changes in the signal intensity can occur. If this happens following the calibration of an instrument, in this instance using a multivariate model, subsequent use of the calibration model will most probably produce erroneous results, posing severe restrictions on the successful application of such models. However, it is possible to solve this problem by applying multivariate techniques that attempt to find a transformation function that acts on the measured response from a drifted instrument transforming it to that which would be obtained on the same instrument at the time of calibration. One multivariate technique capable of this, piecewise direct standardisation (PDS) allows the full spectrum to be utilised without restriction, however it is shown here that it is possible to reduce the number of wavelengths and combine drift correction with variable selection using a previously published method. The synthetic calibration and test solutions used contained varying concentrations of the analytes Pt, Pd, Rh and the matrix elements Al, Mg, Ce, Zr, Ba, In, Sc and Y. Piecewise direct standardisation was then applied to a dataset comprising a set of variables selected on the basis of the importance of their respective partial least squares (PLS) regression coefficients. For Pt, Pd, and Rh, respectively, the relative root mean square percentage error (RRMSE%) after the application of PDS, was 4.14, 3.03 and 1.88%, compared to 73.04, 44.39 and 28.06% without correction; it was evident that there was a clear bias in the uncorrected concentrations for all three analytes. Confidence intervals for the analytes also showed a significant improvement with the application of PDS, and was most noticeable for Rh and Pt.