Codrin Donciu

Smartphone-based colorimetric biosensor readout is strongly influenced by illumination, yet lighting conditions are often characterized mainly by illuminance rather than by their spectral composition. This study investigates the spectral, photometric, colorimetric, and energetic effects of programmable LED illumination on smartphone colorimetric readout under controlled acquisition conditions. Twenty lighting scenarios were generated using three independently controlled color-capable LED luminaires and characterized by spectral irradiance, illuminance, chromaticity, color rendering, and electrical power. A Samsung Galaxy S20 FE was used to acquire JPEG and RAW images of a standardized 24-patch ColorChecker target used as a controlled colorimetric reference for evaluating the optical readout methodology, and colorimetric deviation was quantified using CIEDE2000. Mean ΔE₀₀ ranged from 11.73 to 42.33 across the investigated scenarios. Two conditions differing by less than 1% in illuminance produced a 37% difference in mean colorimetric error, demonstrating that illuminance alone does not determine readout accuracy. An illuminance-only regression model yielded R² = 0.819, whereas inclusion of a relative spectral descriptor increased R² to 0.919. High-output conditions also revealed substantial JPEG clipping despite negligible RAW saturation. These results show that reliable smartphone colorimetric readout requires joint consideration of illumination intensity, spectral composition, electrical demand and image-saturation constraints.

This paper proposes a frequency-estimation method based on an iterative midpoint search applied to the discrete Fourier transform (MS-DFT). The method is designed to reduce the sensitivity of classical interpolation-based estimators to fractional-bin offsets and noise variations. The proposed approach exploits the unimodal structure of the spectral magnitude and performs iterative interval contraction using midpoint evaluation and magnitude ordering. This mechanism enables consistent estimation behavior across different spectral alignments without relying on interpolation formulas. Extensive simulations show that the method achieves a nearly constant ratio between the root mean square error (RMSE) and the Cramér–Rao lower bound (CRLB) over a wide range of signal-to-noise ratios (−7.5 dB to 65 dB). This behavior indicates stable relative efficiency with respect to the theoretical limit, rather than optimization at isolated operating points. In addition, the method reaches near-optimal accuracy within a small number of iterations (typically 8–9), resulting in low and predictable computational complexity. These properties make the MS-DFT estimator suitable for real-time and resource-constrained applications such as embedded sensing and biosignal processing.