 Hi, you are here to learn about Spectral Orange. This is a collaborative project aimed at developing an algorithm for Orange that allows data analysis of hyperspectral datasets. We assume that you already have Orange installed. If not, head over to Orange website and get the latest version. Then, install Spectroscopy Toolbox. Simply go to Options, add on its menu and look for Spectroscopy Package. Now, let's build a workflow for an easy task of normalizing, baseline correcting and then visualizing a series of spectra. First, add the datasets widget from the data panel. Open datasets and look for the Collagen Spectroscopy dataset. Select it and click Send Data. Next, connect datasets to preprocessed spectra and open the widget. We see a few spectra randomly selected from the whole dataset. On the top right panel, we see the original and in the bottom right, the final preprocessed data. At this point, they are the same as we haven't added any preprocessors. You can do this from the top left drop-down menu. Let's add normalized spectra and use the default vector normalization method. The right panel changes and the top half shows the input of normalization step while the bottom displays the normalized data. Next, we could do a simple baseline correction by selecting the appropriate method from the drop-down menu. If you select the title bar of the preprocessing step, the view on the right follows the selection and shows you the effect of the actual preprocessor. Once you are satisfied with preprocessing, click commit to send the data to the output. We will add the spectra widget to visualize our spectra. We have two views, individual and average. Let's first select the average visualization from the menu. We see the mean spectrum of the whole dataset in thick black line and a shaded area that represents plus minus one standard deviation. Try other functions from the menu. Don't worry, you can't break anything. Functions have keyboard shortcuts, so it can be very fast to set up visualization that you like. Let's see, what if we unclick averages? Nice, now we see individual spectra. One thing is important to note here, this is not the whole dataset because oftentimes it would be difficult to see the differences when many spectra are displayed. Therefore, we only show 100 randomly selected spectra. You can resample the selection from the menu or by pressing R. Let's just experiment a little. You can give a title to your graph, write titles on the axis and even show a grid to guide the eye. Do you like the visualization? Why not save it and import it into a presentation or a manuscript? There you go. Congratulations, this is your first orange graph. We will stop here and let you explore the functionalities of these three orange widgets. In the next video, we will show you the possibilities of spectroscopy toolbox and how those widgets connect with orange environment. Remember, orange is all about connecting the dots, so go make some cool workflows and see what you can do.