We used a semi-analytical expression to estimate the concentration of optically active constituents, allowing us to have an interpretable formulation consistent with the laws of physics. We focused on a probabilistic approach, inverting the model with its respective uncertainty. Considering future applications to big data, we explored a neural-network-based method, retrieving computationally efficient estimates with an accuracy comparable to existing state-of-the-art algorithms.