Spectrophotometry vs Digital Image Colorimetry: Analytical Challenges, Standardization, and Emerging Intelligent Frameworks
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Keywords:
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Digital Image Colorimetry, Spectrophotometry, Smartphone-Based Sensing, Color Space Analysis, Green Analytical Chemistry, Analytical Reliability
Abstract
Colorimetric analysis provides a rapid and cost-effective approach for monitoring chemical reactions and determining analyte concentrations. This review examines the strengths and limitations of conventional spectrophotometric methods, known for high precision but high cost and low portability, against digital image colorimetry (DIC). DIC, utilizing smartphones and other portable imaging devices, offers advantages in portability, low operational cost, and alignment with Green Analytical Chemistry principles. However, its analytical performance is often influenced by illumination variability, differences between imaging devices, color-space selection, and calibration strategies. This paper reviews recent developments in DIC, including hardware platforms, color-space transformations, and advanced data processing approaches used to improve analytical performance. Furthermore, to evaluate analytical reliability, this review introduces a novel fuzzy logic-based qualitative reasoning framework. Using a Mamdani-type inference system, the model evaluates the complex interactions between four key variables: lighting stability, device consistency, color-space robustness, and calibration strength. The framework demonstrates that DIC can achieve a "Good" analytical reliability score when lighting is moderately controlled, color-space selection is robust, and calibration strategies are sufficiently strong. However, uncontrolled device variability remains a limiting factor preventing systems from reaching an "Excellent" reliability category, highlighting the ongoing need for methodological standardization in digital colorimetric technologies.
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Authors
Manafiaddin, F., Nazriati, & Nur, H. (2026). Spectrophotometry vs Digital Image Colorimetry: Analytical Challenges, Standardization, and Emerging Intelligent Frameworks. Science and Technology Indonesia, 11(3), 940–954. https://doi.org/10.26554/sti.2026.11.3.940-954
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