NIR calibration in non-linear systems: different PLS approaches and artificial neural networks

The frequent non-linearity of the calibration models used in infrared reflectance spectroscopy (NIRSS) is the main source of large errors in analyte determinations with this technique. Non-linearity in this type of system arises from factors such as the multiplicative effect of differences in particle size among samples or an intrinsically non-linear absorbance–concentration relationship resulting from interactions between components, hydrogen bonding, etc. In this work, calibration methods including partial least-squares (PLS) regression, linear quadratic PLS (LQ-PLS), quadratic PLS (QPLS) and artificial neural networks (ANNs) were used in conjunction with the NIRRS technique to determine the moisture content of acrylic fibres, the wide variability in linear density of which results in differential multiplicative effects among samples. Based on the results, PC-ANN is the best choice for the intended application. However, the joint use of an effective spectral pretreatment and computational methods such as PLS and LQ-PLS, the optimization of which is much less labour-intensive, provides comparable results. Standard normal variate (SNV) was found to be the best of the spectral pretreatments compared with a view to reducing the non-linearity introduced by scattering. The subsequent application of PLS provides accurate results with linear systems (absorption band at 1450 nm). A non-linear calibration model must be applied instead, however, if the system concerned is intrinsically non-linear. Under these conditions, the three methods tested for this purpose (LQ-PLS, QPLS and ANN) provide comparable results.