A New Portable Colorimetric Sensing Device Prototype Using Machine-Learning-Assisted Dual-Regime Calibration Model for Cloud-Integrated Monitoring of Environmental Water Contaminants

Minh-Vuong Phan; Le-Kim-Thuy Nguyen; Quynh-Nhu Pham; Thanh-Tan Nguyen; Manh-Huy Do; Duc-Quy Nguyen; Minh-Tien Pham; Cao-Hien Nguyen; Thuy Nguyen; Thi-Kim-Dung Hoang; Thuy Nguyen

doi:10.1021/acs.analchem.6c01885

June 8, 2026Analytical Chemistry

A New Portable Colorimetric Sensing Device Prototype Using Machine-Learning-Assisted Dual-Regime Calibration Model for Cloud-Integrated Monitoring of Environmental Water Contaminants

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Key Points

This research aims to develop a portable colorimetric sensing device that integrates machine-learning for monitoring water contaminants.
Prototype development of a colorimetric sensing device.
Implementation of a dual-regime calibration model.
Validation with UV-vis measurements in real surface waters.
Achieved >0.998 correlation with laboratory-grade measurements.
Demonstrated AOAC-consistent recoveries and good repeatability.
Successful application for high-frequency monitoring of inorganic nitrogen in field conditions.

Implication

Demonstrates effective monitoring of water contaminants using a new portable device, highlighting its practicality in environmental assessments.

Abstract

≥ 0.998), AOAC-consistent recoveries, good repeatability, and strong agreement with UV-vis measurements in real surface waters. Thus, the framework bridges low-cost portable sensing and laboratory-grade quantitative analysis for high-frequency field monitoring of inorganic nitrogen.

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Cite This Study

Phan et al. (2026) studied this question.

synapsesocial.com/papers/6a28ff336f82f25be989c384 https://doi.org/10.1021/acs.analchem.6c01885

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A New Portable Colorimetric Sensing Device Prototype Using Machine-Learning-Assisted Dual-Regime Calibration Model for Cloud-Integrated Monitoring of Environmental Water Contaminants

Key Points

Implication

Abstract

Discussion

Cite This Study

A New Portable Colorimetric Sensing Device Prototype Using Machine-Learning-Assisted Dual-Regime Calibration Model for Cloud-Integrated Monitoring of Environmental Water Contaminants

Key Points

Implication

Abstract

Discussion

Cite This Study

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