Welcome back!

Previously we looked at some basic vizualization of spectroscopic data. In this post, we begin preprocessing the data for further analysis. Namely, we present a simple, yet effective baseline correction algorithm. Now, you might be wondering what is baseline correction. For an answer, we have to look at the various constituents of spectra.

Welcome back in our Hands-on section for processing of spectroscopic data in Python. At this point, we suppose you have already gone through the last post, considering data formats and importing. You should have imported the benchmark dataset and loaded the following variables:

• trainData
• trainClass
• wavelengths
• testData

Today, we will use all mentioned variables except testData. Firstly, just simple plotting and visualization, followed by the demonstration of the PCA algorithm.

Welcome back.

In our previous post, we introduced a spectroscopic dataset aimed at benchmarking classification models. In this post, we will load in that dataset. Thus, we will go through the script provided in the repository with the dataset. Then, we will go a step further by improving that script by making it faster. This will mark the beginning of a series of posts dealing with the processing of spectroscopic data in python more ...