0
Article Tier 2 Human Health Effects Sign in to save

Neuro-Fuzzy Concentration Classifier for Microplastics in Saline Solution

2025

AI summary Read the abstract

This paper describes a neuro-fuzzy concentration classifier designed to detect tiny concentrations of micro- and nanoplastics in saline solution, an important step toward detecting plastics in biological tissues. The approach demonstrated the ability to distinguish microplastic concentrations in a complex liquid medium.

Micro and nano plastic have been detected in almost every tissue in the human body. The main goal of medical researchers today is to determine if the presence of microplastics in tissue is toxic for humans. Therefore, the most immediate challenge is to find a method of measurement that determines the presence of micro and nano plastic in living tissue. This paper presents research efforts to find a method for detecting tiny concentrations of microplastics in a saline solution. Authors have previously developed a bioimpedance spectrum measurement technology for studying biological processes in cells. The measurement method is non-invasive, without the use of any hazardous materials or processes. In this study, plastic beads of 6 micrometer diameters were implemented in different concentrations. The results of the proposed classifier method were compared with previous results obtained by using the Cole-Cole model, indicating improvement in accuracy and correlation detection.

More Papers Like This

Article Tier 2

Development of neural network based numerical platform for nanoplastics discrimination in water

AI summary Read the abstract

This study developed a neural network-based electrochemical sensor platform for detecting and discriminating nanoplastics in water. Silver nanoparticles bound to a glassy carbon electrode served as the sensing element, with machine learning used to interpret the electrochemical signals.

Article Tier 2

Polymer bead size revealed via neural network analysis of single-entity electrochemical data

AI summary Read the abstract

A neural network was trained to extract microplastic particle size from electrochemical current-spike data recorded when individual polymer beads collide with a microelectrode — a method that avoids the need for optical microscopy. Accurate near-real-time sizing of microplastics in solution is an important analytical advance for water quality monitoring, where detecting and characterizing small plastic particles quickly and affordably remains a major technical challenge.

Article Tier 2

A Portable and Intelligent Dual-Modal Sensing System for Microplastics Detection

AI summary Read the abstract

Researchers built a portable dual-sensor system combining fiber optic fluorescence detection and impedance spectroscopy on a custom microfluidic chip, then trained a convolutional neural network on the combined signals to identify microplastics of varying sizes and concentrations with 100% accuracy alongside other dissolved substances.

Article Tier 2

Integrating MetalAquaDect SERS platform: Machine-learning assisted real-time monitoring of sub-2mg/L microplastics and nanoplastics in complex matrices

AI summary Read the abstract

Researchers used a machine learning-assisted SERS platform (AquaDect) to qualitatively and quantitatively detect microplastics and nanoplastics of multiple types and sizes in aqueous solutions at concentrations below 2 mg/L, demonstrating the approach across polystyrene, polyethylene, polypropylene, and PMMA.

Article Tier 2

Replacing human judgment in fluorescence-based microplastic detection with machine learning: toward harmonization with spectroscopic methods

AI summary Read the abstract

Researchers developed a machine learning application called NRmachine to replace subjective human judgment in fluorescence-based microplastic detection. The study found that the optimal ML model achieved detection probability of 50% for 40-micrometer particles and 90% for 100-micrometer particles, with accuracy comparable to established FTIR spectroscopy methods.

Research digests by email

When a large batch of papers lands in the Atlas, we read through it and send a short write-up of what stood out.

Email me about

Share this paper