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A simple transformation that uses the half-range and central value has been used as a data pre-treatment procedure for principal component analysis (PCA) and pattern recognition techniques. The results obtained have been compared with the results from classical normalisation of data (mean normalisation, maximum normalisation and range normalisation), autoscaling and the minimum-maximum transformation. Three data sets were used in the study. The first was formed by determining 17 elements in 53 tea samples (901 pieces of data). The second and third data sets arose from two long-term drift studies performed to examine instrumental stability at standard and robust conditions. The instruments used were an inductively coupled plasma atomic emission spectrometer and an inductively coupled plasma mass spectrometer. Each drift diagnosis experiment consisted of replicate determinations of a test solution containing 15 analytes at 10 mg l-1 over 8 h without recalibration. Twenty-nine emission lines were determined 99 times, thus, each data set was formed by 2881 pieces of data. Data pre-treatment was applied to the three data sets prior to the use of principal component analysis, cluster analysis, linear discrimination analysis and soft independent modelling of class analogy. The study revealed that the half-range and central value transformation resulted in a better classification of the tea samples than that achieved using the classical normalisation. The loadings in the PCA for the long-term stability study, under both standard and robust conditions, were found to be similar to the drift trends only when the minimum-maximum transformation and the mean or maximum normalizations were used as data pre-treatments.

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Although major advantages have been made in developing robust, easy-to-use ICP-AES instruments offering sub mg g 21 detection limits and relative interference free operation, long-term drift of the analytical signal continuous to be problematic and necessitates regular re-calibration. The work presented here focuses on the effect of two instrumental parameters, i.e. the rf power and the nebuliser gas ¯ow rate, on the robustness of the signals. The effects on the long-term stability when varying these two factors was systematically studied using an experimental design protocol. A ``drift diagnosis'' on thirty emission lines was performed at 12 different sets of operating conditions by repeated determination of a multi-element solution over several hours. The results were studied using standard parameters, i.e., Mg ratio, sensitivity, drift error, drift patterns and multi-way analysis. Parallel factor analysis (PARAFAC) was employed to analyse the 3-way data array generated: ``emission lines 6 replicates 6 operating conditions''. The physical interpretation of the new PARAFAC-factors is shown to enable a better understanding of the drift phenomenon by mathematically characterising the causes of long-term instability. Finally, the robustness of the technique using different operating conditions is evaluated and the appropriate use of internal standards to correct for drift is discussed.

A method is reported for correction of long-term drift in ICP-AES measurements. The change in the intensity of thirty emission lines was monitored over eight hours without recalibration of the instrument. Drift values were found to give errors of up to 20% with respect to the first measurement. The suggested procedure utilises the drift pattern of an intrinsic plasma line, Ar 404.597 nm, and the results of a principal component analysis to remove the drift error. After correction, the drift values drop to less than ±2%.