Abstract

We report the use of dual-domain regression models, which were built utilizing a wavelet prism decomposition and paired with transfer by orthogonal projection, for the calibration transfer of near-infrared (NIR) spectra. The new method is based on obtaining specific frequency components for a spectrum via wavelet analysis, projecting the frequency components of the primary instrument onto the subspace orthogonal to the mean instrumental difference between spectra from the primary and the secondary instrument, and weighting each frequency component model according to the cross-validation error of the frequency components of the projected primary instrument’s spectra to generate a stacked ensemble model robust to contributions to the spectra from instrumental variations. The method, which does not require property values from the secondary data set, is tested on three NIR data sets, and is compared with orthogonal projection in the wavelength domain, orthogonal signal correction, and with model updating approaches. For the data sets we examined, we show that the prediction performance of the new method is competitive with orthogonal projections in the wavelength domain, as well as orthogonal signal correction and model updating approaches, both of which require property values for spectra from the secondary instrument. Examination of the spectral data reconstructed from the projected frequency components indicates that aspects of the data that may be attributable to instrumental or physical phenomena (i.e., instrumental baseline shifts or discretized intensity changes which may be attributed to scatter) are suppressed, but those associated with the chemical phenomena are retained. The benefits of orthogonal projection on each individual frequency component are further corroborated by the fact that the models based on frequency component projection generalize better to unseen instruments compared with the other methods.

© 2017 The Author(s)

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