The measurement and classification of light is essential across many scientific disciplines. Devices used to measure light range from the highly precise scanning spectroradiometers to the more practical compact multichannel filter-array type imaging sensors and the ubiquitous RGB pixel. While there have been numerous successful efforts to reconstruct spectrum from RGB, RGB-to-spectrum reconstruction has historically been limited to natural scenes and other edge cases under strict constraints. However, information theory and recent advances in deep learning have shed new light on the vast amount of redundancy contained within data collected in the natural world, including light. In this paper, we will investigate how analytic methods can help map high dimensional spectra data to a low-dimensional feature space with minimal inductive bias. Through a better understanding of the intrinsic dimension of the data, we can use the features expressed in this representation to exploit regularities and make tasks like data compression, measurement and classification more efficient. The aim of this analysis is to help inform how and when low-dimensional representation of spectra is useful in practice for designing compact sensors as well as for lossy data compression and robust classification.