The detection of unmodeled interferences in new samples, a convenient way to extend the model is to re-calibrate it using new incoming samples. However, it may be difficult and cost effecting to collect a large number of new samples. If the spectral shape of the unmodeled spectral component can be obtained and mathematically added in variable amounts to the original calibration spectra, then a new synthetic multivariate calibration model can be generated from the augmented data to accommodate the presence of the unmodeled source of spectral variation. This method is demonstrated for simultaneous determination of new fuchsine, crystal violet and malachite green in the presence of unmodeled interferences which are present in the unknown samples. The synthetic accommodation of unknown interferents during partial least square regression eliminates the need for expensive regeneration of new calibration samples and collection of their spectra. The results indicate the successful applicability of the proposed method in complex samples.

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