Chemometrics in Biospectroscopy

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AbstractVibrational spectroscopic techniques have been used for decades for predicting chemical composition of biological samples. For the establishment of prediction models, multivariate calibration techniques are often applied, since the use of single variables or combinations of few variables are not sufficient or robust enough. Multivariate techniques need to handle covariation among variables. We observe an especially high degree of covariation between variables in vibrational spectroscopy: Covariation is present (i) among neighbouring channels providing signals from the same spectral band, (ii) among spectral bands that are due to the same chemical bond, and (iii) finally among chemical bonds that arise from the same biomolecule. Consequently, the underlying dimensions in a given data set are rather few compared to the number of variables (channels) measured, also in cases where the number of samples is quite large. Multivariate methods based on latent variables that relate directly to underlying phenomena and mechanisms, are especially popular in vibrational spectroscopy. By using latent variables, the variations of objects can be studied with respect to these underlying phenomena and variation patterns can be studied in the light of chemical bonds and biomolecules.Another data modeling issue relates to the preprocessing of spectra: Vibrational spectra of biological materials are plagued by unwanted variability effects such as scatter effects in infrared spectroscopy or fluorescence effects in Raman spectroscopy. Calibration models often become simpler and even better when these effects are removed from the spectra before the calibration model is established. In addition, the interpretation of the results in terms of chemical bands is simpler and more reliable when the spectra are preprocessed.In this chapter, several issues related to data analysis of vibrational spectra of biological samples are discussed: (i) model‐based preprocessing techniques based on extended multiplicative signal correction (EMSC) are presented. Different variants of EMSC are discussed and a new extension of EMSC especially suited for the correction of fluorescence effects in Raman spectroscopy is introduced. (ii) Different aspects related to multivariate calibration and classification are discussed with a special focus on partial least squares regression (PLSR) and validation.