Over the course of the last few months, the Library of Stains team has been analyzing data collected from the hundreds of images we gathered during three trips to the Library of Congress, the University of Pennsylvania, and the Universities of Wisconsin and Iowa.
Multispectral data analysis can take many shapes and forms. As part of the Library of Stains project, the team has applied a methodology specific to stains. This post takes you through the steps of that process as it relates to characterizing stains and what is, and is not, possible to know.
The data that come to us straight from the camera images first need to be changed into .tif files. This is done through Capture One, a Phase One software, and both sets of images can be saved in the same file. New software is being released soon and will do this step automatically.
Next, all .tif files need to be flattened. Flattening images is done in two steps. First you need to clean the flats – a series of shots of a white piece of paper taken on the same day as the images. Second, you flatten the set of images for a given side (or folio) to create an image that is evenly balanced agains the white light spectrum. Using Image J again, the software does the work and automatically processes the series of “flats” in line with the manuscript images. The flattening process is a pretty easy learning curve, but somewhat time-consuming. The good news is that this step will also be done automatically with the new software.
Once the files have been flattened, an intermediary step recreates a color image using Image J. This takes the flattened .tif files from a specific side (folio) and with the light information from the full stack of multispectral images, recreates a color image. The Library of Stains team wanted to be able to have .jpg images that can be annotated and the specific areas on a given folio identified for the elements that were analyzed.
Eventually the image will end up in Digital Mappa – a data curation software environment being used for our data visualization. In the meantime, to make our data analysis workflow as efficient as possible, we have put all the RGB color images into a powerpoint presentation.
Now, with the Powerpoint up on one computer, and Image J on another, we’re ready to begin analysis. We will be using Image J once again to plot the z-axis for each spot that will serve to create a spectral curve. For the the stain project, this meant we would be plotting a z-axis for the substrate (parchment or paper), the inks, the red and blue pigments used for rubrication and decoration, and of course, as many stains as our hearts desired.
To move from image files to spectral curves on an excel spreadsheet requires a few steps:
- Using the 10 non-filtered wavelengths we imaged for each side, we opened them in Image J and configured them into one stack.
- Scrolling through them highlights how different components on a given folio react to different light wavelengths.
- Plotting the z-axis on a particular part of the image is easy with Image J. Outline the portion to be plotted with a small rectangle and choose “Plot z-axis.” The results immediately appear as a curve on the screen. The curve can also be viewed as a list of number values.
- These values are plotted in the appropriate columns on an excel spreadsheet and labelled whichever element. As shown below, we have columns for substrate, inks, pigments and stains.
- In order to see the true reflectance, the z-axis of specific component needs to be adjusted against the values for the white color checker. Indeed, the white color checker values are the first to be plotted and inserted into the appropriate columns for each of the material components.
- Following the methodology devised by colleagues at the Preservation and Testing Division of the Library of Congress, on the spreadsheet the formula is automatically calculated by dividing the intensity of the ink, substrate, pigment or stain by the intensity of the white swatch on the color checker. This then is what is plotted on the x and y axis of the spectral curve – the x axis showing light wavelengths, and the y axis, reflectance levels.
Then comes the fun part. We are able to begin to decipher the curves.
Preliminary results from the University of Iowa and Wisconsin show variations of ink curves, as well as possible curves on a number of folios that may indicate wax residues. Further analysis is underway and final results, alongside the data itself, will be ready for open access by August 31, 2018.
With much thanks to Leah Pope Parker, PhD candidate in English at the University of Wisconsin, for her intellectual contributions to this project, as well as countless hours analyzing and visualizing data.