A novel digital image colorimetric analysis system with adaptive sensitivity and measuring range

Abstract In this study, we aimed at developing an innovative digital image colorimetry (DIC) system, designated CAS-t, which integrates advanced hardware and software components. The CAS-t platform includes a fully enclosed illumination chamber with uniform LED lighting diffused by a polyethylene material, a camera positioned 25 cm above a 96-well microplate with an additional lens to reduce lateral distortion, and an adjustable exposure time to optimize performance. An image-processing algorithm within a Python workflow detects circular wells in a microplate, applies median filtering, and extracts RGB intensities analyzed via principal component analysis (PCA). The experimental results obtained from phenol red (PR) and indigo carmine (IC) indicators demonstrated that exposure time significantly influences the sensitivity and measurable concentration ranges, thereby allowing tunable detection parameters. For IC, increasing exposure time from 1 to 5 ms increased the slope magnitude from −0.014 to −0.027 µM⁻¹, whereas PR showed the opposite trend. Compared with the reflected-light systems and smartphone cameras, CAS-t achieved approximately two-fold higher sensitivity (−27.0 versus −14.0 µM⁻¹) and markedly improved precision (RSD: 5.75% versus 25.39%). As a proof concept, Fe(II) content in a pharmaceutical sample was quantified by performing CAS-t system calibrated using standard solutions (0.1–5.0 mg L⁻¹) of Fe(II) versus PC1 as the most sensitive and linear color channel (R² = 0.999). Validation against the reference UV-Vis spectrometer showed strong statistical agreement at 95% confidence (p > 0.05), confirming the reliability of the 7 ms as optimal exposure time chosen based on sensitivity and linearity analyses. Consequently, this integrated hardware-software approach offers a standardized framework for DIC assays with broad adaptability across diverse chemical and biochemical applications.