Benefits of Agilent 8700 LDIR with Onboard ATR for Microplastics Characterization

Importance of the Topic

Environmental contamination by microplastics has become a global concern due to their persistence and potential impacts on ecosystems and human health. Reliable identification of polymer types in complex environmental and food matrices is essential to assess exposure levels and ecological risks. However, commonly used additives and sorbed substances such as zinc stearate can produce infrared spectral features similar to those of target polymers like polyethylene, leading to misidentifications and false positives. Addressing these spectral interferences is critical for accurate microplastic characterization.

Objectives and Study Overview

This study evaluates the performance of the Agilent 8700 Laser Direct Infrared (LDIR) chemical imaging system with an onboard micro-attenuated total reflection (µATR) accessory to:

• Verify the identity of known standards, exemplified by zinc stearate.
• Identify unknown particles extracted from infant formula samples.
• Differentiate between zinc stearate and low-density polyethylene microspheres using an automated particle analysis workflow and a customized spectral library.

Methodology

Standard zinc stearate powder and polyethylene microspheres were prepared and analyzed in parallel using an Agilent Cary 630 FTIR spectrometer with diamond-ATR and the Agilent 8700 LDIR system with onboard µATR. ATR spectra were matched against external, platform-independent libraries (Wiley KnowItAll Analytical Edition). Infant formula samples underwent extraction, vacuum filtration onto gold-coated membranes, and direct µATR interrogation by the LDIR system. Automated particle analysis in Agilent Clarity software employed spectral matching thresholds (HQI 0.65–0.99), size classification, and a scanning range of 975–1,800 cm⁻¹. A reference spectrum of zinc stearate was later added to the Microplastics Starter 2.0 library to evaluate its impact on identification accuracy.

Instrumentation Used

• Agilent 8700 LDIR Chemical Imaging System with onboard µATR module
• Agilent Cary 630 FTIR Spectrometer equipped with a diamond ATR accessory
• Agilent Clarity Software for automated particle analysis and library management
• Wiley KnowItAll Analytical Edition Software with ATR-IR spectral libraries

Main Results and Discussion

Known Material Verification: Zinc stearate was correctly identified by both FTIR-ATR (HQI 94.7) and LDIR-µATR (HQI 85.9) despite the LDIR’s narrower spectral window, confirming the µATR accessory’s capability for accurate material verification.

Unknown Particle Identification: Particles isolated from infant formula were matched to zinc stearate (HQI 85.1) using LDIR-µATR and external libraries, demonstrating the system’s flexibility in resolving unknown interferences.

Polyethylene vs. Zinc Stearate Distinction: Initial automated analysis without a zinc stearate reference resulted in 87.6% of zinc stearate particles misclassified as polyethylene. After adding the zinc stearate spectrum to the Microplastics Starter 2.0 library, correct identification exceeded 99% for both materials. The differentiation relied on characteristic absorption features in the 1,500–1,660 cm⁻¹ region when applying first-derivative spectral matching.

Benefits and Practical Applications of the Method

• Onboard µATR capability eliminates sample transfer steps, reducing contamination risk and analysis time.
• Automated particle analysis provides rapid, high-throughput assessment of particle count, size distribution, and chemical identity.
• Customizable spectral libraries enable targeted removal of interferences and improved confidence in polymer identification.
• Modular instrumentation adapts to diverse sample types, including environmental water, soil, food, and biological matrices.

Future Trends and Applications

Advances in curated spectral libraries, machine learning algorithms, and real-time data processing are expected to further reduce false positives in microplastic analysis. Integration of additional ATR and reflection modes, coupled with automated sampling workflows and AI-driven classification, will broaden applicability to nano- and micro-scale contaminants. Development of field-deployable LDIR platforms may enable on-site monitoring of microplastic pollution.

Conclusion

The Agilent 8700 LDIR system with onboard µATR demonstrates robust performance in accurately identifying microplastics and discriminating against interfering substances such as zinc stearate. Its combination of high-speed imaging, automated particle analysis, and flexible spectral library management addresses key challenges in environmental and food-related microplastic studies, providing reliable data for research and regulatory applications.

References

1. Witzig C.S. et al. When Good Intentions Go Bad – False Positive Microplastic Detection Caused by Disposable Gloves. Environ. Sci. Technol. 2020;54(19):12164–12172. doi:10.1021/acs.est.0c03742
2. Schymanski D. et al. Analysis of Microplastics in Drinking Water and Other Clean Water Samples with Micro-Raman and Micro-Infrared Spectroscopy. Anal. Bioanal. Chem. 2021;413:5969–5994. doi:10.1007/s00216-021-03498-y
3. Samandra S. et al. Accurate Microplastic Characterization in Infant Formula. Agilent Technologies. Application Note 5994-5928EN, 2023.
4. Alwan W. et al. Characterization of Microplastics in Environmental Samples by Laser Direct Infrared Imaging and User-Generated Libraries. Agilent Technologies. Application Note 5994-4822EN, 2022.