Spectral correction and preprocessing

Traditional macroscale NIR spectroscopy requires a calibration set, made of the same chemical components as the target sample, but with varying concentrations that are chosen to span the range of concentrations possible in the sample. A concentration matrix is made from the known concentrations of each component. The PLS algorithm is used to create a model that best describes the mathematical relationship between the reference sample data and the concentration matrix. The model is applied to the unknown data from the target sample to estimate the concentration of sample components. This is called concentration mode PLS . 

These same analysis techniques can be applied to chemical imaging data. Additionally, because of the huge number of spectra contained within a chemical imaging data set, and the power of statistical sampling, the PLS algorithm can also be applied in what is called classification mode as described in Section 8.4.5. When the model is applied to data from the sample, each spectrum is scored relative to its membership to a particular class (i.e. degree of purity relative to a chemical component). Higher scores indicate more similarity to the pure component spectra. While these scores are not indicative of the absolute concentration of a chemical component, the relative abundance between the components is maintained, and can be calculated. If all sample components are accounted for, the scores for each component can be normalized to unity, and a statistical assessment of the relative abundance of the components made. 

A partial least square type two (PLS 2) analysis was employed, based on a library of the three API pure components. Applying the model in classification mode to the sample data set results in PLS score images that show the spatial distribution of the three API components. 

Qualitatively, acetaminophen and aspirin appear to be more abundant than caffeine, which concurs with the label concentration values. Caffeine shows very distinctive areas of localized high concentration domains while aspirin is relatively evenly disnibuted on the spatial scale of the image. Acetaminophen appears to be somewhat in the middle, showing up as large domains that blend into one another. 

In addition, taking the ratio of pixels calculated to belong to each component to the total number of pixels yields the following abundance estimates 41% for acetaminophen, 42% for aspirin and 12% for caffeine. 

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