ArticleSensors (Basel, Switzerland)2026
Portable Multispectral Fluorometer with Embedded Machine Learning for Chlorophyll-a Estimation in Acetone-Extracted Samples.
Article in Sensors (Basel, Switzerland), 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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8 authors.
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Abstract
Chlorophyll-a (Chl-a) is commonly used as an indicator of phytoplankton biomass and water quality, but its determination usually depends on laboratory-based methods and specialized equipment. In this work, a portable multispectral fluorometer was developed and evaluated for Chl-a estimation in 90% acetone extracts prepared from water samples. The system integrates a nominal 420-425 nm fluorescence excitation source, 90° detection geometry, two multispectral sensors (AS7341 and AS7343), an ESP32-S3 microcontroller, and an Android application for device control and result visualization. A dataset of 76 samples was used for model development, including regression-model benchmarking, model selection, and internal testing. A Random Forest model selected for embedded implementation was exported to C++ for on-device inference. Validation using 15 independent samples yielded
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