Identifying Chemical, Physical, and Instrumental Matrix Matched Samples by Leveraging Spectral Model Regression Vectors

Developing spectroscopic calibration models requires calibration samples that mimic as much as possible new sample compositions as well as measurement conditions. This requirement is known as matrix matching calibration samples to new samples, that is, samples are matrix matched chemically, physically, and instrumentally. To accomplish this task, calibration sets have large sample numbers to span the expected sample matrix variations. This large range of calibration variability can result in poor performance. Preferred is a calibration set distinctly matched to the new samples. However, assessing whether each sample in a particular calibration set is appropriately matched to new samples relative to the specific analyte content and all other constituents is not an easy task. It is well documented that even though calibration samples are spectral matches to new sample spectra (have similar measured spectra), the calibration set is usually not fully matrix matched to new sample compositions. For example, using a spectral similarity measure such as Euclidean distance, the same calibration samples are deemed spectral matches to new samples regardless of the analyte of interest. This work presents a process to assess underlying sample matrix interactions between calibration model regression vectors and new sample spectra allowing fully matrix matched samples to be identified. The process is general and applicable to other situations such as matching historical batch processing data where references values are not known for new samples (unlabeled). Two data sets are used to demonstrate the functionality of the process. One consists of nuclear magnetic resonance spectra for mixtures of three alcohols and the other is near-infrared corn spectra with four prediction properties measured on three instruments. General trends are reported for a few of the possible data situations. Calibration samples identified as matrix matched to new samples are shown to predict the new samples with the lowest prediction errors.