The application of Kohonen neural networks to diagnose calibration problems in atomic absorption spectrometry

In atomic absorption spectrometric measurements calibration lines are measured daily. These lines are not always acceptable. They can, for instance, contain outliers, have a bad precision or can be curved. To evaluate the quality of those lines a method which gives a fast diagnosis is recommended. In this study the use of Kohonen neural networks was examined as an automated procedure to classify these calibration lines. The results were compared with those obtained using a decision support system which uses classical statistical methods to classify the lines. The prediction capabilities of both approaches relative to a visual inspection and classification was found to be comparable, or even slightly better for the Kohonen networks, depending on the training set used. For both techniques a prediction error rate of <10% was obtained, relative to a visual classification.