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An Innovative Multiparametric Sensor Design for Detecting Microplastics and Heavy Metals
Summary
Researchers designed a multiparametric optical sensor system using 22 LED light sources and 18 narrow-bandwidth photodiodes to simultaneously detect microplastics and heavy metals in environmental samples, with concentration evaluation performed by a trainable multilayer perceptron algorithm. The system offers a lower-cost alternative to conventional detection methods, eliminating the need for expert interpretation while maintaining analytical capability.
Microplastics and heavy metals are materials that harm the environment and living organisms. Rapid detection enables their control or the identification of their sources. Conventional detection methods are expensive and require expert interpretation. The proposed sensor system detects these materials and evaluates their concentrations using a trainable multilayer perceptron algorithm. The system consists of twenty-two different light spectrum LEDs and eighteen narrow bandwidth photodiodes. The absorbance of incoming light and the shifted bandwidths in the spectrum can be evaluated by assessing multiparametric optical events. The study examines water samples containing eight different heavy metals, namely arsenic (As), cadmium (Cd), chromium (Cr), copper (Cu), mercury (Hg), nickel (Ni), lead (Pb), and zinc (Zn), along with three types of microplastics: melamine particles with a diameter of 8 µm, polystyrene particles with a diameter of 8 µm, and polystyrene particles with a diameter of 10 µm. Classification was performed in a concentration-dependent and concentration-independent manner. The system performance was improved by selecting features using the Ranker method of the InfoGain algorithm. Measurements were performed without applying any indicator chemicals. The system demonstrated high success in concentration-independent evaluation and acceptable concentration accuracy for heavy metals.
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