Benefits of Agilent 8700 LDIR with Onboard ATR for Microplastics Characterization

Significance of the Topic

Microplastics are pervasive environmental contaminants that require accurate identification to assess ecological and health risks. Additives such as zinc stearate can produce infrared spectral features similar to common polymers like polyethylene, leading to false positives in microplastic analysis. Reliable methods to distinguish these interferences are essential for robust environmental monitoring and quality control.

Study Objectives and Overview

This study evaluates the performance of the Agilent 8700 Laser Direct Infrared (LDIR) chemical imaging system with onboard micro-Attenuated Total Reflection (µATR) and the Agilent Cary 630 FTIR with diamond ATR in:

• Verifying the identity of a known zinc stearate standard.
• Confirming the composition of unknown particles extracted from infant formula.
• Distinguishing between polyethylene microspheres and zinc stearate using an automated particle analysis workflow.

Methodology and Instrumentation

The workflow combined high-speed IR imaging, onboard ATR sampling, and external spectral libraries:

• Agilent 8700 LDIR Chemical Imaging System with µATR accessory for localized ATR spectra.
• Agilent Cary 630 FTIR spectrometer fitted with a diamond ATR module as an independent verification tool.
• Agilent Clarity software with the Microplastics Starter 2.0 library and customizable spectral entries.
• Wiley KnowItAll Analytical Edition software for platform-agnostic library matching and hit quality indexing.
• Sample preparation used gold-coated membrane filters, germanium ATR crystal cleaning protocols, and vacuum filtration of ethanol suspensions.

Major Findings and Discussion

• Verification of Zinc Stearate: Both Agilent 8700 LDIR-µATR and Cary 630 FTIR-ATR accurately identified zinc stearate with hit quality indices (HQI) of 85.9 and 94.7, respectively, despite the narrower spectral range of the 8700 LDIR.
• Unknown Particle Identification: ATR spectra collected from unidentified particles in infant formula matched zinc stearate (HQI 85.1), demonstrating the benefit of onboard µATR for targeted interrogation.
• Distinguishing PE from Zinc Stearate: Initial automated analysis misclassified 87.6% of zinc stearate particles as polyethylene. By adding a reference zinc stearate spectrum to the Microplastics Starter 2.0 library and reprocessing, correct identification exceeded 99% and eliminated false positives for polyethylene.
• Spectral Differentiation: While both materials share the 1,480–1,440 cm–1 C–H bending band, zinc stearate exhibits a distinct absorbance region at 1,500–1,660 cm–1. First-derivative spectral matching enhanced differentiation in automated workflows.

Benefits and Practical Applications

Integrating the Agilent 8700 LDIR system with µATR and Agilent Clarity software offers:

• Rapid, high-throughput imaging and automated identification of microplastics and interfering contaminants.
• Improved data accuracy by minimizing false positives from additives such as zinc stearate.
• Seamless export of µATR spectra to external libraries for flexible matching across platforms.
• Enhanced workflow efficiency through automatic reanalysis when libraries are updated.

Future Trends and Potential Utilization

Advances in chemical imaging and spectral libraries are expected to drive:

• Broader adoption of onboard ATR in imaging systems for in situ material verification.
• Expanded user-generated and curated spectral libraries covering emerging contaminants and complex mixtures.
• Integration with machine-learning approaches for real-time classification and quantification of environmental microplastics.
• Standardization of automated workflows to support regulatory and industrial quality assurance.

Conclusion

This work demonstrates that the Agilent 8700 LDIR with onboard µATR, combined with robust software libraries, can accurately distinguish microplastics from interfering additives such as zinc stearate. The approach enhances confidence in microplastic characterization, reduces false positives, and streamlines high-throughput analysis.

Reference

1. Witzig C.S.; Földi C.; Wörle K.; et al. When Good Intentions Go Bad – False Positive Microplastic Detection Caused by Disposable Gloves. Environ. Sci. Technol. 2020, 54(19), 12164–12172.
2. Schymanski D.; et al. Analysis of Microplastics in Drinking Water and Other Clean Water Samples with Micro-Raman and Micro-Infrared Spectroscopy: Minimum Requirements and Best Practice Guidelines. Anal. Bioanal. Chem. 2021, 413, 5969–5994.
3. Samandra S.; Alwan W.; Clarke B.; et al. Accurate Microplastic Characterization in Infant Formula. Agilent Technologies Application Note 5994-5928EN, 2023.