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Portable Multispectral Fluorometer with Embedded Machine Learning for Chlorophyll-a Estimation in Acetone-Extracted

Armando Daniel Blanco-Jáquez¹, María Teresa Alarcón-Herrera¹, Luz I Valenzuela-García¹

¹Departamento de Ingeniería Sustentable, SECIHTI-Centro de Investigación en Materiales Avanzados, Calle CIMAV 110, Ejido Arroyo Seco, Durango 34147, Mexico.

Sensors (Basel, Switzerland)

|July 15, 2026

Summary

A new portable fluorometer accurately estimates chlorophyll-a (Chl-a), a key indicator of phytoplankton biomass and water quality. This device offers a promising, accessible alternative to traditional lab-based methods for Chl-a analysis.

Keywords:

ESP32-S3 TinyML acetone extraction chlorophyll-a embedded machine learning multispectral fluorescence portable fluorometer water quality monitoring

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Area of Science:

Environmental Science
Analytical Chemistry
Instrumentation

Background:

Chlorophyll-a (Chl-a) is a crucial indicator for phytoplankton biomass and water quality assessment.
Traditional Chl-a determination methods are often lab-dependent, requiring specialized equipment and limiting field applicability.
There is a need for portable, accessible tools for accurate Chl-a estimation.

Purpose of the Study:

To develop and evaluate a portable multispectral fluorometer for estimating chlorophyll-a (Chl-a) concentrations.
To assess the device's performance using acetone extracts from water samples.
To enable on-device Chl-a estimation through embedded machine learning models.

Main Methods:

Development of a portable multispectral fluorometer integrating specific excitation sources, multispectral sensors (AS7341, AS7343), a microcontroller (ESP32-S3), and an Android application.
Utilized a dataset of 76 samples for regression model development, selection, and internal testing.
Implemented a Random Forest model exported to C++ for on-device inference and validated with 15 independent samples.

Main Results:

The portable fluorometer achieved high accuracy in validation, with R² = 0.979, RMSE = 35.41 µg/L, MAE = 28.91 µg/L, and MAPE = 6.14%.
Repeatability tests showed a maximum coefficient of variation (CV) of 3.29%, indicating consistent measurements.
Embedded inference demonstrated strong agreement with Python predictions (R² = 0.997).

Conclusions:

The developed portable multispectral fluorometer is a viable and accurate alternative for chlorophyll-a estimation in prepared acetone extracts.
The system's portability and on-device inference capabilities facilitate rapid, field-based water quality monitoring.
This technology holds promise for improving accessibility to phytoplankton biomass and water quality analysis.