Maximum likelihood principal components regression on wavelet-compressed data.

Maximum likelihood principal component regression (MLPCR) is an errors-in-variables method used to accommodate measurement error information when building multivariate calibration models. A hindrance of MLPCR has been the substantial demand on computational resources sometimes made by the algorithm, especially for certain types of error structures. Operations on these large matrices are memory intensive and time consuming, especially when techniques such as cross-validation are used. This work describes the use of wavelet transforms (WT) as a data compression method for MLPCR. It is shown that the error covariance matrix in the wavelet and spectral domains are related through a two-dimensional WT. This allows the user to account for any effects of the wavelet transform on spectral and error structures. The wavelet transform can be applied to MLPCR when using either the full error covariance matrix or the smaller pooled error covariance matrix. Simulated and experimental near-infrared data sets are used to demonstrate the benefits of using wavelets with the MLPCR algorithm. In all cases, significant compression can be obtained while maintaining favorable predictive ability. Considerable time savings were also attained, with improvements ranging from a factor of 2 to a factor of 720. Using the WT-compressed data in MLPCR gave a reduction in prediction errors compared to using the raw data in MLPCR. An analogous reduction in prediction errors was not always seen when using PCR.