NEWS ARTICLE
学术动态：Multi-Wavelength Machine Learning for High-Precision Colorimetric Sensing
论文标题：Multi-Wavelength Machine Learning for High-Precision Colorimetric Sensing
发布日期：2026-05-24
作者：Majid Aalizadeh, Chinmay Raut, Ali Tabartehfarahani, Xudong Fan
DOI：10.3390/s26113327
论文摘要：Conventional colorimetric sensing methods typically rely on signal intensity at a single wavelength, often selected heuristically based on peak visual modulation. This approach overlooks the structured information embedded in full-spectrum transmission profiles, particularly in intensity-based systems where linear models may be highly effective. In this study, we experimentally demonstrate that applying a forward feature selection strategy to normalized transmission spectra, combined with linear regression and ten-fold cross-validation, yields significant improvements in predictive accuracy. Using food dye dilutions as a model system, the mean squared error was reduced from over 22,000 with a single wavelength to 3.87 using twelve selected features, corresponding to a more than 5700-fold enhancement. These results validate that full-spectrum modeling enables precise concentration prediction without requiring changes to the sensing hardware. The approach provides a proof-of-concept framework that may be extended to colorimetric assays used in medical diagnostics, environmental monitoring, and industrial analysis following broader validation with real analytes and heterogeneous sample matrices.
元数据：Crossref 收录的 MDPI Sensors 论文。 DOI: 10.3390/s26113327. Vol. 26, Issue 11. Authors: Majid Aalizadeh, Chinmay Raut, Ali Tabartehfarahani, Xudong Fan.
开放许可：https://creativecommons.org/licenses/by/4.0/
原文链接：https://doi.org/10.3390/s26113327
PDF 链接：https://www.mdpi.com/1424-8220/26/11/3327/pdf