## 1 Introduction and Objectives
### Background
Thus, the goal of this concept is to determine how closely the color perception produced by a light source matches that of daylight, considering natural light as the perfect reference for color rendering. Moreover, color rendering is not the sole variable to take into account in lighting design. The chromatic preference can vary depending on the studied scenario, and thus color rendering can be influenced by boundary conditions, such as the color of the walls and ceiling, the geometry of the inner space, or the task developed by the observer [18]. In line with this assertion, [PERSON] et al. [19] evaluated color fidelity in the case study of a picture gallery, selecting multichannel LED luminaires for this specific application. [PERSON] et al. [20] carried out an interesting study which modified not only the flux and CCT of light sources, but also the wall color of a simulated picture gallery. The results showed that a combination of the three parameters affect the color perception of the observers. Moreover, [PERSON] et al. [21] also studied the effect of CCT in the color perception of fine art paintings, determining that low CCT values (near 3500 K) are suitable for hue discrimination. In a different setting, [PERSON] et al. [22] examined color preference for a retail shop, determining the appropriate SPD for LED luminaires. More recently, [PERSON] et al. [2] analyzed color preference across various scenarios, including retail displays, supermarkets, and restaurants, with each case study yielding distinct results. Notably, research by [PERSON] et al. [23] evaluated color preference with 25 participants, concluding that most of the color rendering metrics do not align with color preference criteria, despite the fact that these preferences can be predicted using calculation models based on the metrics defined by TM-30-20, namely the Gamut Index (Rg) and the Fidelity Index (Rf). The picture that emerges from the described scenario led us to conclude that the most effective way to determine the color perception--given both objective and subjective parameters--is through surveys to determine the hue preference of the observer for a specific venue, as well as objective trials, such as the Farnsworth-Munsell (FM-100) or the Natural Color System tests, as evidenced by the following studies: [PERSON] et al. [24] conducted comprehensive research, analyzing 40 surveys to assess whiteness perception considering various LED lamps, concluding that blue wavelength light is needed to induce whiteness. Moreover, [PERSON] et al. [25] assessed the color quality provided by LED lamps, examining a wide range of light sources with different CCT across more than 80 surveys. Similarly, [PERSON] et al. [26] analyzed the precision of different color metrics using 20 surveys with different LED lamps, according to different CCTs. A recent example is the study by [PERSON] et al. [27], which studied a set of color rendering metrics using 10 surveys, concluding that the CAM02-UCS color space is suitable for determining hue deviation. It must be noted that the performing of color tests depends on many parameters, such as the color of the environment and the age of the participants. When developing tests to determine a light source's ability to differentiate colors, it is crucial that the tests provide an objective quantification of the results [28]. [PERSON] et al. [29] defined a new procedure for measuring the color discrimination, Rd, which assigns a score based on the number of transpositions made in the FM-100 trial. An Rd close to zero indicates an excellent color discrimination, while a score greater than 16 indicates poor hue discrimination. As can be deduced from the above, color rendering concepts are continually evolving due to the lack of solid criteria for determining a light source's chromatic performance [30]. Moreover, most metrics focus on quantifying color deviation, neglecting the need to distinguish colors with similar hues, brightness, and chromaticity.