Raman Spectroscopy to Distinguish Microplastics Mixed with Environmental Impurities

Raman spectroscopy, a molecular spectroscopic technique, is a popular technique used in various industries, including in environmental analysis. Because of its nondestructive nature, Raman spectroscopy is an ideal technique for the classification and characterization of microplastics in the environment. In a new development, researchers from China Jiliang University in Hangzhou, China, demonstrated how Raman spectroscopy can create pseudo-color images that accurately represent the distribution and types of microplastics, according to a new study published in Analyst (1).

Microplastics are tiny plastic particles less than 5 mm in size (1). These particles have emerged as a significant environmental concern, permeating marine and terrestrial ecosystems (1,2). Often originating from plastic debris and consumer products, microplastics can have detrimental effects on wildlife and potentially human health (1). Traditional methods to detect microplastics have often come up short because of their inability to accurately and consistently characterize and detect small pollutants like microplastics.

Marine microplastic pollution contaminates large bodies of water entering the food chain. Concept Marine Pollution, Microplastics, Water Contamination, Food Chain Impact. Generated by AI. | Image Credit: © Anastasiia - stock.adobe.com

In this study, Dr. Huacai Chen in the College of Optical and Electronic Technology at China Jiliang University and his team explored using Raman spectroscopy to create a "fingerprint" characterization of microplastics (1). By selecting characteristic peaks and applying the classical least-squares (CLS) fitting method, the research team was able to create pseudo-color imaging maps that visually represent the distribution of different microplastics within a sample (1). This approach is unique as a tool for environmental analysis and accomplishes two objectives. First, it can identify types of mixed microplastics (1). Second, the approach can distinguish the microplastics from environmental impurities (1).

Read More: Integrating Raman Spectroscopy and Machine Learning to Classify Microplastics

This capability is significant for one main reason. Because of the complexity of real-world environmental samples, where organic substances often adhere to the surface of microplastics and interfere with detection methods, it is important that modern detection methods can accurately detect the difference of various substances and impurities (1). Even when spectral signals are weaker than normal, the researchers showed in this study that Raman spectroscopy, unlike traditional methods, can overcome this challenge (1).

Another key point of this study is that cloud-point extraction, which is a sample preparation technique that manipulates temperature and surfactant concentration to move aqueous solutes, was successfully applied followed by membrane filtration method to identify mixed-component microplastics (1,3).

Read More: Advanced IR Spectroscopy Techniques Revolutionize Micro- and Nanoplastics Research

However, the research also acknowledges some limitations. For example, the researchers wrote that identifying plastic particles with a particle size of less than 1 μm still has a low success rate (1). Additionally, the current methods for removing organic matter attached to microplastics are cumbersome, posing a challenge for efficient visual detection (1). As a result of these limitations, future research should focus on developing simpler methods to eliminate these organic interferences, thereby enhancing the efficiency of Raman mapping for microplastics detection.The potential applications of Raman spectroscopy in environmental science extend beyond microplastics, offering a valuable tool for the detection of various contaminants in complex environmental matrices.

References

(1) Liu, K.; Pang, X.; Chen, H.; Jiang, L. Visual Detection of Microplastics Using Raman Spectroscopic Imaging. Analyst 2024, 149, 161–168. DOI: 10.1039/D3AN01270K

(2) Wetzel, W. Integrating Raman Spectroscopy and Machine Learning to Classify Microplastics. Spectroscopy. Available at: https://www.spectroscopyonline.com/view/integrating-raman-spectroscopy-and-machine-learning-to-classify-microplastics (accessed 2024-07-17).

(3) Raynie, D. E. Surfactant-Mediated Extractions, Part 1: Cloud-Point Extraction. LCGC Europe 2016, 29 (1), 36–38.