Self-calibrating colorimetric sensor assisted deep learning strategy for efficient and precise Fe(II) detection

Abstract

Colorimetry is to determine the component content usually comparing or measuring the “RGB” values, which has the advantages of quick response and visual comparison. However, the widely reported “RGB” models are limited by requiring professional operation such as accurate sampling, insufficient information, cumbersome calculation, etc., which has significantly restricted the promotional application. Inspired by the PAA-PVA gel with excellent absorbing-swelling ability and superior response ability, indefinite volume addition has a significant difference in the swelling phenomenon of PAA-PVA hydrogel, the extent of this enlargement is directly related to the amount of testing sample absorbed, which can be applied to reflect testing sample volume added. In addition, benefited from a comprehensive and precise parameter network, the deep learning (DL) algorithm solves the defects such as ambient light effect and manual extract RGB values of the colorimetric sensing analysis strategy. Considering Fe(II) is an essential element for life science and environmental monitoring, we have developed a self-calibrating colorimetric sensor assisted deep learning strategy for the Fe(II) detection with indefinite volume. It can directly complete the analysis of Fe(II) concentration via captured images, without going through a complicated process similar to the RGB model. Specifically, we integrated the advantages of colorimetric sensing strategy, absorbing-swelling hydrogel, and DL algorithm to develop a phenanthroline (PHEN) indicator doped PAA-PVA colorimetric sensor (PHEN-PAA-PVA). Then, 2,500 colorimetric response images were collected as the training dataset and assessed 2 DL algorithms and 7 machine learning (ML) algorithms. The convolutional neural network (CNN) model exhibited the most efficiency to quantify 1 × 10−5–9 × 10−4 mol/L Fe(II), with a limit of detection (LOD) of 3.3 × 10−6 mol/L. It provides a promising and blooming in-situ detection approach for Fe(II) analysis, contributes to health management and environment monitoring.