FT-IR Contaminant Identification using OMNIC Specta Software

FT-IR Contaminant Identification using OMNIC Specta — Executive Summary

Importance of the topic

Rapid and reliable identification of unexpected contaminants in materials is a frequent and time-critical requirement in industrial quality control, failure investigation and regulatory compliance. Infrared (IR) spectroscopy provides fast, information-rich spectra suitable for both routine pass/fail screening and detailed contaminant identification. Automated, reproducible software tools that exploit known composition of a sample to highlight only the unexpected constituents reduce analysis time, minimize operator variability and support timely corrective actions that prevent production holds, shipment delays or recalls.

Objectives and study overview

This application note demonstrates the capabilities of Thermo Scientific OMNIC Specta software, specifically the Contaminant Search feature, to identify low-level unexpected components in samples whose main composition is already known. The study illustrates the workflow and advantages over traditional spectral subtraction through multiple practical examples: an adhesive/talc residue on an aluminum can, phthalate plasticizer detection in PVC, and trace erucamide contamination in EVA polymer. Emphasis is placed on automated searching against spectral libraries, ATR correction to match different collection modes, and visualization options that clarify component contributions.

Methodology

Key methodological elements used in the study:

• Spectral acquisition: ATR FT-IR spectra collected on Thermo Scientific Nicolet iS50 with Smart Orbit or Smart iTR diamond ATR. (Note: OMNIC workflows are compatible with Nicolet iS5 and iS10 systems.)
• Reference spectra: Verified pure-material spectra (e.g., PVC, phthalates, EVA) collected in the same laboratory and stored as the known-component inputs for searches.
• Library searching: Contaminant Search uses a two-input approach — a supplied pure-component spectrum (the expected main material) and one or more spectral libraries (potential contaminants). Libraries used in examples include Hummel Polymers & Plasticizers, Polymer Additives and Plasticizers, and Inorganics.
• ATR correction: Advanced ATR Correction within OMNIC Specta enables reliable comparison between spectra acquired with different ATR crystals or transmission measurements, avoiding replacement of established libraries.
• Algorithmic approach: Rather than user-dependent spectral subtraction, OMNIC Specta performs an automated, concerted multi-component search that computes a residual (unknown minus known) and searches that residual against library entries to find the best-matching contaminant components.

Used instrumentation

• Thermo Scientific Nicolet iS50 FT-IR spectrometer
• Thermo Scientific Smart Orbit diamond ATR
• Thermo Scientific Smart iTR diamond ATR
• Compatibility noted with Nicolet iS5 and iS10 systems
• Spectral libraries: Hummel Polymers & Plasticizers, Polymer Additives and Plasticizers, Inorganics and other commercial IR libraries

Main results and discussion

The Contaminant Search workflow delivered fast, specific identifications and intuitive visual confirmation in all presented cases:

• Aluminum can residue: A scraped sample produced a mixed spectrum. OMNIC Specta identified linseed oil as the dominant known component, then resolved the residual into talc and cyanoacrylate (super glue). The Terrain View and overlay/offset displays made it straightforward to see how the two contaminants contributed to the residual. The full identification was obtained in under two minutes from sample collection.
• PVC with plasticizers: Using a pure PVC spectrum as the known component and searching against polymer/plasticizer libraries, OMNIC Specta identified the specific phthalate additive rather than only the general class, addressing regulatory concerns where exact chemical identity matters.
• EVA production contamination: A production lot of EVA (target 18% vinyl acetate) narrowly failed QCheck. Contaminant Search, seeded with the 18% EVA spectrum, identified erucamide (a slip agent) at low concentration (~0.8%). The algorithm’s sensitivity and the visual agreement between composite and original spectra prevented false clearance based solely on the top search hit metric.

Key discussion points:

• Reference-quality spectra are critical: Best results are obtained when reference spectra are collected on the same instrument, accessory and laboratory using the same protocols as the unknowns.
• Automated residual-based searching outperforms manual spectral subtraction by eliminating user-to-user variability and spectral distortion artifacts from totally absorbing peaks.
• Visualization tools (overlay, offset, Terrain View) enhance user confidence by showing how identified components sum to the measured spectrum and by exposing any remaining unexplained features.

Benefits and practical applications

Practical advantages of OMNIC Specta Contaminant Search include:

• Faster root-cause identification in QC and failure investigations, reducing downtime and limiting regulatory exposure.
• Reproducible results suitable for SOPs and audit trails, removing subjective variability inherent to manual subtraction.
• Low-level contaminant detection sensitivity demonstrated (sub-percent levels in examples).
• Applicability across many scenarios: polymer additive detection, environmental contamination (dust, hair), adhesive residues, and regulatory screening (phthalates, flame retardants, bisphenols).
• Ability to leverage existing transmission libraries via ATR correction, protecting prior investments in spectral databases.

Future trends and potential uses

Areas where the described approach is likely to expand or integrate:

• Deeper integration with automated QC pipelines: direct handoff from screening (e.g., QCheck) to contaminant identification for closed-loop decision-making.
• Enlargement and curation of spectral libraries focused on regulated additives and low-level contaminants to improve specificity.
• Combination with chemometrics and machine learning methods to improve sensitivity, deconvolution of highly overlapping bands and probabilistic ranking of candidates.
• Hyphenation with imaging or micro-sampling for spatially resolved mixtures when required, while retaining bulk-sample workflows for homogeneous mixtures.
• Regulatory surveillance and supply-chain screening where rapid identification of banned additives is required at receiving docks and in inbound QC.

Conclusion

OMNIC Specta’s Contaminant Search provides a robust, operator-independent method to identify unexpected components in materials when the primary composition is known. By combining ATR correction, high-quality reference spectra, comprehensive libraries and informative visualizations, the software enables rapid, sensitive and defensible contaminant identification for QC, failure analysis and regulatory compliance. The method reduces reliance on manual spectral subtraction, accelerates investigations, and supports consistent SOP-driven workflows.

References

• Thermo Fisher Scientific. FT-IR Contaminant Identification using OMNIC Specta Software. Application Note AN51773, 2013.