Microplastics: Detection methods-An update

Chalannavar, Raju K and Kamble, Avinash A and Malabadi, Ravindra B and MS, Divakar and Swathi, Swathi and Karamchand, Kishore S and Kolkar, Kiran P and Moramazi, Somayyeh and Munhoz, Antonia Neidilê Ribeiro and Coronado, Karen Viviana Castaño (2025) Microplastics: Detection methods-An update. World Journal of Advanced Research and Reviews, 26 (2). pp. 2809-2824. ISSN 2581-9615

Microplastics are synthetic polymers with major dimension of ≤5 mm. The particles occur in a large variety of shapes, sizes, colors, and compositions. Microplastics enter the food chain, they may be biomagnified and bioaccumulated by larger organisms and ultimately reach humans. Apart from organisms, other food materials such as salt, honey, beer, tea bags, and drinking water have also been reported to have microplastic contamination. Organs reported to be contaminated by microplastics and nanoplastics include the gastrointestinal tract, respiratory system, skin, liver, kidneys, and even the brain. Effect of microplastic contamination on these organs can range from inflammatory responses to tissue damage and potential disruption of organ function and carcinogenesis. Microplastics have entered drinking water via various pathways, raising concerns about their potential health impacts. A number of fluorescent dyes, including Nile red, Rhodamine B, Safranin T, and fluorescein iso phosphate, can label plastic polymers and hence are used in the detection of microplastics. Among these, Nile red has been used widely as a rapid method for detecting microplastics. The three main methods for detecting and quantifying microplastic concentrations in water are FTIR Spectroscopy, py-GC/MS, and Raman Spectroscopy. FTIR and Raman Spectroscopy can determine the number of microplastic particles by plastic type and size range, whereas py-GC/MS can quantify concentrations of specific types of microplastics in mg/l. To overcome the challenges of time and labor-intensive, microplastic detection techniques, researchers are increasingly adopting machine learning and automation. These technologies can process large datasets with greater speed and accuracy, training algorithms to detect microplastic more efficiently.