Spectral correction and pre-processing

The data cube was converted to log10(l/R) as described in the section on data analysis, using background and dark-cube correction. 

Traditional macro-scale 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 tries to create a model that best describes the mathematical relationship between the reference sample data and the concentration matrix, and applies that transformation model 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. In this case, the reference library used to establish the PLS model is 

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 distributed 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. 

It is extremely useful to move beyond a subjective and qualitative analysis of the spatial distribution of sample components, and to begin to explore the quantitative information contained within chemical imaging data sets. One of the most powerful statistical representations of an image does not even maintain spatial information. A chemical image can be represented as a histogram, with intensity along the x-axis and the number of pixels with that intensity along the y-axis. This is a statistical and quantitative 

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