In recent years, Raman spectroscopy has become very popular due to the advancement in instrumentation and have allowed us to focus on their application rather than on the operation and limitations of the instrument. The large scale information provided by a single Raman spectrum includes the molecular structure, qualitative and quantitative information of the analyte. This work leverages this data to quantify the amount of constituents in the analyte and also simultaneously generate a spatial distribution of the same thereby providing a agile reverse engineering process. The study is based on simple linear models and matrix computations like linear regression, multivariate curve resolution which have been moulded according to the requirement of the problem. We further use statistical F-test and statistics such as R^2 in order to evaluate the results and filter the unwanted from the data. The proposed pipeline relies on the basis of Beer-Lambert law however without any reliability on any form of calibration data. Moreover, our framework allows the quantification and spatial distribution to be specific to a particular layer or region of the analyte. All the work will be consolidated into an UI enabling users to analyze with a few clicks.
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Quantitative Tablet Characterisation Based on Multi-component Image Analytics & Pattern Matching
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