A correlation principal component regression analysis of NIR data

Abstract The use of principal component regression (PCR) as a multivariate calibration method has been discussed by a number of authors. In most situations principal components are included in the regression model in sequence based on the variances of the components, and the principal components with small variances are rarely used in regression. As pointed out by some authors, a low variance for a component does not necessarily imply that the corresponding component is unimportant, especially when prediction is of primary interest. In this paper we investigate a different version of PCR, correlation principal component regression (CPCR). In CPCR the importance of principal components in terms of predicting the response variable is used as a basis for the inclusion of principal components in the regression model. Two typical examples arising from calibrating near‐infrared (NIR) instruments are discussed for the comparison of the two different versions of PCR along with partial least squares (PLS), a commonly used regression approach in NIR analysis. In both examples the three methods show similar optimal prediction ability, but CPCR performs better than standard PCR and PLS in terms of the number of components needed to achieve the optimal prediction ability. Similar results are also seen in other NIR examples.