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.

Let's remind EMSLIBS 2019 conference in Brno a bit. As a part of the program, we have prepared challenging benchmark dataset, which was used for the classification contest. An extensive report could be found in our recent paper, but for highlights, you are just at the right place.

Unsurprisingly, the spectroscopic community was not missed out by the recent boom of modern machine learning approaches to data processing. Thus, we felt it was an ideal time to push the limits, compare performance, and also share knowledge across the community.

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 ...

In our previous post, we described the common characteristics of spectroscopic data (sparsity, redundancy, and high dimensionality). In this post, we describe a dataset that you can use to experiment with spectroscopic data and to get familiar with the challenges posed by its properties. In the following, we state the motivation behind the creation of the dataset and its properties.

Welcome to our new blog!

In this modern era of data science and machine learning, there are plenty of excellent resources for image processing, natural language processing and other exciting stuff. However, we have found out complete lack of content related to spectroscopic data. So, we have decided to create a platform to gather useful information about the processing of such data and notice you about new ideas in spectroscopy. Also, be prepared for some educational content (python/R code) and occasionally an interview with leading spectroscopists.