How to pre-process your spectra for research (SNV, MSC, Derivatives, etc.)

In this webinar, graduate student Edwin Caballero offers an introduction on what are unwanted spectral variations and what methods can be used to reduce them.

Data preprocessing (DP) methods are mathematical algorithms used to reduce unwanted spectral variations in spectral data. They are highly versatile however they should be used sparingly, specially when using spectral data to create models.

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CHAPTERS
00:00 Intro
02:44 Artefacts
03:21 Baseline Artefact
05:20 Scattering Artefact
09:30 Noise Artefact
11:24 Data Preprocessing Methods
12:45 Reducing baseline (detrending, assymetric least squares, derivatives)
21:30 Reducing scattering (SNV, RNV, MSC, normalization)
30:00 Reducing noise (SG smoothing, moving average)
33:39 Strategies for DP
37:59 Programs where you can use DP methods

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Data preprocessing is a set of techniques that can be applied to data obtained with infrared and Raman spectroscopy to improve the quality and usefulness of the data. Data preprocessing can help to remove noise and other unwanted features from the data, and to correct for systematic errors or biases. It can also be used to align and combine data from multiple spectra, and to transform the data into a more useful form. For example, data preprocessing can be used to normalize the data, to apply baseline correction, or to perform peak picking. Overall, data preprocessing is an important step in the analysis of infrared and Raman spectroscopy data, as it can help to improve the accuracy and reliability of the results.

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