**1. Introduction**

Optical radiation is electromagnetic energy that dissipates through space. Light, the visible part of optical radiation, upon reaching the surface of an object is either reflected, transmitted (if the object is transparent or translucent), or absorbed. Reflected light initiates vision when it is detected by the human visual system. Light absorbed by the object turns into heat and is considered wasted for illumination purposes. The absorbed light (energy) may cause a chemical change in the molecules due to photochemical reactions, and if the object is light-sensitive, such as a painting, it may cause irreversible damage (e.g., color fading) [1,2]. The dilemma between visibility and damage is a crucial aspect of lighting design for museums and galleries.

Characterizing the properties of the absorbed light can enable estimating and preventing further damage to sensitive works of art. Past studies suggest that there are four primary parameters that influence the optical damage to artwork: light intensity, exposure duration, spectral power distribution (SPD) of the light source, and spectral sensitivity of light sources [2,3]. An increase in light intensity and exposure duration increases damage to artwork, although the relationship is likely not linear. The spectral power distribution and spectral sensitivity of the pigments interact in a more complex manner.

Early models of damage were based on the Einstein–Planck law, which states that energy in lower wavelengths (i.e., ultraviolet radiation) may cause more damage than energy in longer wavelengths [4]. However, several research studies showed that energy in long wavelengths, such as infrared radiation, and energy in the visible spectrum could

**Citation:** Durmus, D. Characterizing Color Quality, Damage to Artwork, and Light Intensity of Multi-Primary LEDs for Museums. *Heritage* **2021**, *4*, 188–197. https://doi.org/10.3390/ heritage4010011

Received: 30 December 2020 Accepted: 15 January 2021 Published: 17 January 2021

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**Copyright:** © 2021 by the author. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/).

also cause damage to artwork [5–8]. Another important factor is the selective influence of light source SPD on the magnitude of damage. Studies suggest that the spectral absorption of pigments may dictate the amount of damage to an artwork since only the light absorbed by a pigment causes photochemical action [9–12]. This understanding, coupled with the overall effect of lighting intensity, encouraged researchers to use spectral optimization algorithms to reduce damage caused by lighting [13–19]. Some of these optimization studies even considered the energy consumed by lighting to balance the end-users' different needs [14,16,18,19]. Despite the increase in computational power and knowledge of materials' response to light, there is still no universal damage model that can account for different types of pigments. Another unresolved issue is the holistic presentation of the trade-offs and complex relationships between the parameters, such as damage to artwork, the color appearance of the painting, illumination levels (both for damage and visibility), and energy consumption. overall effect of lighting intensity, encouraged researchers to use spectral optimization algorithms to reduce damage caused by lighting [13–19]. Some of these optimization studies even considered the energy consumed by lighting to balance the end-users' different needs [14,16,18,19]. Despite the increase in computational power and knowledge of materials' response to light, there is still no universal damage model that can account for different types of pigments. Another unresolved issue is the holistic presentation of the trade-offs and complex relationships between the parameters, such as damage to artwork, the color appearance of the painting, illumination levels (both for damage and visibility), and energy consumption. Although multi-primary LED (mpLED) systems can be optimized to generate tailormade solutions for light-sensitive artwork, quantifying the complex relationships between target parameters using a single-dimensional model is not possible. Fortunately,

in long wavelengths, such as infrared radiation, and energy in the visible spectrum could also cause damage to artwork [5–8]. Another important factor is the selective influence of light source SPD on the magnitude of damage. Studies suggest that the spectral absorption of pigments may dictate the amount of damage to an artwork since only the light absorbed by a pigment causes photochemical action [9–12]. This understanding, coupled with the

*Heritage* **2021**, *4* FOR PEER REVIEW 2

Although multi-primary LED (mpLED) systems can be optimized to generate tailormade solutions for light-sensitive artwork, quantifying the complex relationships between target parameters using a single-dimensional model is not possible. Fortunately, the complex relationships between different aspects can be presented using a 3-D graph, and discretizing each continuous dimension of the graph (voxelating) can result in a discrete, measurable volume. The voxelization method has been previously applied to color rendition variability in mpLEDs [20]. Here, the voxelization method is applied to display the trade-offs between damage, the color appearance of artwork, illumination levels, and energy efficiency. The voxelization method is based on the idea that a large distribution of data points can be grouped into discrete packages or cubes called voxels, as shown in Figure 1. Converting a large dataset to voxels results in increased interpretability of the data, reduces visual cluster, and enables creating predictive models. The data points within each voxel are considered the "same" for classification purposes, and the "sameness" (uniqueness) of the data points within a voxel can be defined by identifying the borders of the voxel in each dimension. The voxelization in the context of museum lighting should contain the primary goals of illuminating artwork, such as preventing damage caused by lighting, optimizing the appearance of artwork (brightness and color), and improving the efficiency of the light sources. the complex relationships between different aspects can be presented using a 3-D graph, and discretizing each continuous dimension of the graph (voxelating) can result in a discrete, measurable volume. The voxelization method has been previously applied to color rendition variability in mpLEDs [20]. Here, the voxelization method is applied to display the trade-offs between damage, the color appearance of artwork, illumination levels, and energy efficiency. The voxelization method is based on the idea that a large distribution of data points can be grouped into discrete packages or cubes called voxels, as shown in Figure 1. Converting a large dataset to voxels results in increased interpretability of the data, reduces visual cluster, and enables creating predictive models. The data points within each voxel are considered the "same" for classification purposes, and the "sameness" (uniqueness) of the data points within a voxel can be defined by identifying the borders of the voxel in each dimension. The voxelization in the context of museum lighting should contain the primary goals of illuminating artwork, such as preventing damage caused by lighting, optimizing the appearance of artwork (brightness and color), and improving the efficiency of the light sources.

**Figure 1.** Converting continuous measures to discrete data enables the counting of unique voxels (red cubes), summing them to quantify the volume for a multi-primary LED lighting system. In this example, the size of each unit voxel is 1 for **Figure 1.** Converting continuous measures to discrete data enables the counting of unique voxels (red cubes), summing them to quantify the volume for a multi-primary LED lighting system. In this example, the size of each unit voxel is 1 for each dimension, and the magnitude of the voxelated volume is 3.

each dimension, and the magnitude of the voxelated volume is 3.
