How Does Computational Pre-processing Affect Spectral Analysis? An Investigation on Simulated Spectra

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dc.contributor Aalto-yliopisto fi
dc.contributor Aalto University en Bertinetto, Carlo Vuorinen, Tapani 2018-02-09T10:08:37Z 2018-02-09T10:08:37Z 2013
dc.identifier.citation Bertinetto , C & Vuorinen , T 2013 , ' How Does Computational Pre-processing Affect Spectral Analysis? An Investigation on Simulated Spectra ' Scandinavian Symposium on Chemometrics , Stockholm , Sweden , 17/06/2013 - 20/06/2013 , . en
dc.identifier.other PURE UUID: fbe185b6-6c27-4530-9ee6-b09103412f70
dc.identifier.other PURE ITEMURL:
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dc.description.abstract The computational recognition and resolution of spectra is usually preceded by Pre-Processing (PP) operations to improve the signal quality and highlight the information of interest. However, little systematic study has been carried out on how the combined use of different PP methods affects the result, often leaving the researcher to rely on common sense when deciding the computational strategy. This work addresses the issue through a simulation experiment. Fictitious spectra of mixtures of five components at varying concentrations and corrupted by different types of noise were processed by various combinations of PP techniques: smoothening, baseline correction, normalization and reduction to Principal Components (PC). The original mixtures were then recognized by k-means Cluster Analysis and the quality of this recognition as a function of the PP procedure and the distance metric was quantified in terms of the Rand and silhouette coefficients. These simulated spectra were designed emulating data commonly encountered in Raman imaging, but these results are applicable to other types of spectroscopy as well. Among the considered PP combinations, the one that yielded the best Rand coefficient employed a polynomial baseline correction method [1], Whittaker smoother, Manhattan normalization, PC transformation explaining 80% of variance and clustering using either Euclidean or city-block distance. A few other combinations had a very similar outcome, whereas certain PP sequences produced a clearly incorrect clustering. The robustness of each PP combination with respect to particular types of noise is also discussed. [1] Carlo G. Bertinetto, Tapani Vuorinen. “Automatic Baseline Recognition for Fast Correction Using Continuous Wavelet Transform (CWT)”. Applied Spectroscopy. Submitted. en
dc.format.mimetype application/pdf
dc.language.iso en en
dc.relation.ispartof Scandinavian Symposium on Chemometrics en
dc.rights openAccess en
dc.subject.other 215 Chemical engineering en
dc.title How Does Computational Pre-processing Affect Spectral Analysis? An Investigation on Simulated Spectra en
dc.type Poster fi
dc.description.version Peer reviewed en
dc.contributor.department Department of Bioproducts and Biosystems
dc.subject.keyword 215 Chemical engineering
dc.identifier.urn URN:NBN:fi:aalto-201802091535
dc.type.version publishedVersion

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