A new procedure is presented for wavelength interval selection with a genetic algorithm in order to improve the predictive ability of partial least squares multivariate calibration. It involves separately labelling each of the selected sensor ranges with an appropriate inclusion ranking. The new approach intends to alleviate overfitting without the need of preparing an independent monitoring sample set. A theoretical example is worked out in order to compare the performance of the new approach with previous implementations of genetic algorithms. Two experimental data sets are also studied: target parameters are the concentration of glucuronic acid in complex mixtures studied by Fourier transform mid‐infrared spectroscopy and the octane number in gasolines monitored by near‐infrared spectroscopy.
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Goicoechea et al. (2003) studied this question.
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