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Pulse oximetry enables oxygen saturation estimation (<inline-formula><math display="inline"> <semantics> <mrow> <msub> <mi>S</mi> <mi>p</mi> </msub> <msub> <mi>O</mi> <mn>2</mn></msub></mrow></semantics></math></inline-formula>) non-invasively in real time with few components and modest processing power. With the advent of affordable development kits dedicated to the monitoring of biosignals, capabilities once reserved to hospitals and high-end research laboratories are becoming accessible for rapid prototyping. While one may think that medical-grade equipment differs greatly in quality, surprisingly, we found that the performance requirements are not widely different from available consumer-grade components, especially regarding the photodetection module in pulse oximetry. This study investigates how the use of candidate light sources and photodetectors for the development of a custom <inline-formula> <math display="inline"> <semantics> <mrow> <msub> <mi>S</mi> <mi>p</mi> </msub> <msub> <mi>O</mi> <mn>2</mn> </msub> </mrow> </semantics> </math> </inline-formula> monitoring system can lead to inaccuracies when using the standard computational model for oxygen saturation without calibration. Following the optical characterization of selected light sources, we compare the extracted parameters to the key features in their respective datasheet. We then quantify the wavelength shift caused by spectral pairing of light sources in association with photodetectors. Finally, using the widely used approximation, we report the resulting absolute error in <inline-formula> <math display="inline"> <semantics> <mrow> <msub> <mi>S</mi> <mi>p</mi> </msub> <msub> <mi>O</mi> <mn>2</mn> </msub> </mrow> </semantics> </math> </inline-formula> estimation and show that it can lead up to 8% of the critical 90–100% saturation window.