Revisiting camera-based calibration of oxygen optodes: Error sources, empirical alternatives, and practical guidelines for end-users

Oxygen optodes are widely used with camera-based systems to generate two-dimensional oxygen maps. However, calibration strategies often rely on Stern-Volmer (SV) equations developed for spectroscopy, without accounting for the spectral biases and signal mixing introduced by imaging detectors. When calibrating oxygen-sensitive optodes, two main objectives arise: (i) to characterize the sensor material itself by reporting SV parameters, and (ii) to determine unknown oxygen levels with high accuracy and low uncertainty. While the first objective is accomplished well in spectrofluorometric systems, direct transfer of SV approaches to camera-based setups introduces systematic errors. Here, we critically evaluate SV-based calibration in camera-acquired data, particularly for RGB imaging, using a combination of experimental and simulated datasets. We show that inter-channel crosstalk and even minor deviations in the zero-reference signal propagate into substantial errors in SV parameter estimation and oxygen readouts. To overcome these limitations, we propose a two-part calibration strategy: 1) direct empirical fitting of the ratiometric signal using a three-parameter logarithmic model, and 2) a spectral function-based framework that accounts for sensor-specific optical characteristics, enabling camera-independent calibration. In summary, this study establishes a set of best practices for oxygen optode calibration with imaging systems. We clarify under which conditions SV models can be meaningfully applied (i.e., with spectrofluorometric data or fully corrected camera-based spectra), and when empirical alternatives are preferable for direct oxygen quantification. We further provide recommendations on calibration range, number of calibration points, and analytical signal choice, consolidated into a visual decision guide to support both expert users and practitioners. • Stern-Volmer (SV) equations are reassed for camera-based oxygen measurements • SV parameters are biased by spectral properties of the imaging system • SV-based calibrations increase uncertainty compared to empirical fitting models • Camera-independent framework corrects crosstalk and improves calibration comparability • Visual guide supports users in choosing robust oxygen optode calibration strategies