Replicating the Blue Wool Response Using a Smartphone Spectroradiometer

Abstract

A spectroradiometer was developed using the rear camera of the Samsung S7 smartphone for replicating the response of blue wool, a light comparative fading test from the textile industry that was adopted by the art conservation community in the 1960s. This technique was regarded as a cost effective, readily available comparative standard for understanding lightfastness of museum objects, but not an end all solution. Many other solutions have been found since the suggestion of the blue wool standard. Including the Canadian Light Damage Calculator and Lightcheck® ,which are comparator guides for lighting museum objects. The Berlin model for comparing tested spectral data is taken with expensive equipment, to a database to determine an objects sensitivity. Microfadeometry that directly tests the object with a 0.4-mm diameter focused Xenon source that deteriorates the artwork. None however have been able to completely replace the vetted, cost effective, easy to use blue wool standard for determining the sensitivity of museum and gallery objects, but a solution is needed. The solution is a designed and tested smartphone spectroradiometer attachment that measures the illumination and reflectance spectrum of museum and gallery objects to deduce an absorption spectrum that can be correlated to an expected blue wool response under the same conditions. The attachment for the phone is made from off the shelf and 3D printed parts. It has measured the deterioration of blue wool under a high intensity source and can predict the expected time for a blue wool specimen to visibly fade under the illumination of museum LED lighting. This thesis covers the design, modeling and testing experiment for the smartphone spectroradiometer that currently has a resolution of ± 7 nm, a spectral range from 393 to 650 nm with five orders of magnitude and an absolute radiometric error of 27.5% with the possibility of room for improvement. This includes increasing the accuracy of the modeled spectrum of the sun used for calibration, applying more advanced noise removal techniques, applying filters in post processing for better resolution and of course using a smartphone that takes raw images and can have its optical image stabilizer turned off during manual mode.