Advancing Microplastics Characterization with FT-IR Microscopy

The growing concern over microplastic pollution has spurred the development of advanced analytical techniques to detect, identify, and quantify these tiny contaminants.

Defined as plastic particles ranging from 1 micron to 5 millimeters, microplastics are now pervasive in air, soil, and water due to the degradation of nearly 400 million tons of plastic produced annually. To address this environmental challenge, researchers increasingly rely on Fourier Transform Infrared (FT-IR) Microscopy and Imaging, a powerful tool for microplastic characterization.

You can also read: Microplastics in Food – How Much?

Why It’s Important to Characterize Microplastics

Characterizing microplastics goes beyond simply identifying their presence—it’s critical for understanding their environmental and health impacts. Microplastics transport toxic chemicals, and accumulate within food chains. Ultimately, they enter human organs through both ingestion and inhalation, raising serious health concerns.  Different types of plastics degrade at different rates, release various chemicals, and pose unique risks.

By determining their composition, size, shape, and concentration, researchers can:

• Assess ecological risks: Identifying polymer types helps determine the potential toxicity to marine life, wildlife, and humans.
• Track pollution sources: Characterization can link microplastics to specific industries, products, or waste management practices, aiding in pollution control efforts.
• Inform policy and regulation: Reliable data on microplastic contamination supports the development of evidence-based environmental policies and mitigation strategies.
• Evaluate remediation techniques: Understanding microplastic properties allows for the creation of more effective removal and treatment technologies.

How FT-IR Microscopy Works in Microplastic Detection

FT-IR Microscopy combines infrared spectroscopy with advanced imaging capabilities, enabling researchers to analyze microplastic particles with high precision. In this process, samples are exposed to infrared light, which interacts with the material at a molecular level. Consequently, each polymer absorbs specific wavelengths of IR light, creating a distinct spectral pattern that identifies its chemical structure.

Researchers in the study used the PerkinElmer Spotlight 400 FT-IR Imaging System to capture both visible images and infrared spectra. Operating at an 8 cm⁻¹ resolution with a 25-micron pixel size, the system efficiently scanned entire filters in under 40 minutes, significantly reducing analysis time compared to traditional manual methods.

Sample Preparation: A Critical Step

Accurate microplastic analysis starts with proper sample preparation. The study used a test sample tablet from the Norwegian Institute for Water Research, which was dissolved to separate microplastic particles. Researchers then performed vacuum filtration using 0.2-micron Anodisc filters and gold-coated filters, depending on the type of infrared measurement:

• Gold-coated filters supported reflectance measurements, extending the spectral range down to 700 cm⁻¹.
• Anodisc filters were used for transmission measurements, effective up to 1250 cm⁻¹.

This meticulous preparation ensured minimal contamination and enhanced the reliability of the results.

Identifying Microplastic Types with Precision

The FT-IR imaging system successfully identified 25 microplastic particles, classifying them into four major polymer types:

• Polyethylene (PE) – 5 particles
• Polystyrene (PS) – 4 particles
• Polyethylene Terephthalate (PET) – 8 particles
• Polyvinyl Chloride (PVC) – 8 particles

This precise identification enables scientists to understand not only the quantity of microplastics but also their potential risks based on polymer type. For instance, PVC can release harmful additives, while PE and PET degrade differently, impacting ecosystems in unique ways.

Leveraging Advanced Data Processing Techniques

One of the standout features of this approach is the use of Principal Components Analysis (PCA), a statistical method that enhances data interpretation. Through PCA, researchers rapidly classified microplastic types based on their IR spectra without manual sorting. This not only improved efficiency but also reduced human error in data analysis.

Moreover, the data seamlessly integrates with third-party microplastics analysis software, such as Purency and siMPle, offering additional flexibility for researchers handling large datasets.

Conclusion: The Future of Microplastics Research

The application of FT-IR imaging in microplastics research represents a significant leap forward in environmental monitoring. Its combination of high-resolution imaging, chemical specificity, and automated data processing allows scientists to detect and identify microplastics with unprecedented accuracy.

Also, as microplastic contamination continues to rise globally, advanced technologies like FT-IR imaging will be critical for tracking pollution, informing regulatory decisions, and ultimately protecting ecosystems and public health. Understanding microplastics through detailed characterization is the first step toward mitigating their impact on our planet.