Orthogonal signal correction of near-infrared spectra

Near-infrared (NIR) spectra are often pre-processed in order to remove syst ematic noise such as base-line variation and multiplicative scatter effects . This is done by differentiating the spectra to first or second derivative s, by multiplicative signal correction (MSC), or by similar mathematical fi ltering methods. This pre-processing may, however, also remove information from the spectra regarding Y (the measured response variable in multivariat e calibration applications). We here show how a variant of PLS can be used to achieve a signal correction that is as close to orthogonal as possible t o a given Y-vector or Y-matrix. Thus, one ensures that the signal correctio n removes as little information as possible regarding Y. In the case when t he number of X-variables (K) exceeds the number of observations (N), strict orthogonality is obtained. The approach is called orthogonal signal correc tion (OSC) and is here applied to four different data sets of multivariate calibration. The results are compared with those of traditional signal corr ection as well as with those of no pre-processing, and OSC is shown to give substantial improvements. Prediction sets of new data, not used in the mod el development, are used for the comparisons. (C) 1998 Elsevier Science B.V . All rights reserved.