Powerful New Identification Tools with OMNIC Specta Software

Importance of the topic

Identification of unknown materials and decomposition products is a routine but critical task in analytical chemistry, quality control and forensic investigations. Rapid, reproducible identification of multi-component samples (complex formulations, contaminants, evolved gases) improves laboratory throughput, reduces operator bias and supports regulatory compliance. Advanced spectral-search strategies that reliably deconvolute overlapping infrared signatures are therefore essential in laboratories that use FT-IR, ATR and TGA-IR techniques.

Objectives and study overview

This technical note presents and evaluates the OMNIC Specta multi-component spectral-search algorithm as implemented by Thermo Scientific. The primary aims are to demonstrate the algorithm’s ability to:

• Recognize multiple components in single FT-IR spectra without iterative manual subtraction,
• Provide consistent, operator-independent identifications, and
• Handle challenging use cases including pharmaceutical mixtures, polymer/additive detection, and gases evolved during TGA-IR experiments.

Practical examples and comparisons to conventional search-and-subtract workflows are used to highlight advantages and limitations.

Used instrumentation

• Thermo Scientific Nicolet iS10 FT-IR spectrometer (experiments also compatible with iS5 and iS50 for non-TGA work)
• Diamond ATR accessory for direct sampling
• Thermo Scientific TGA furnace coupled to a sample-compartment TGA accessory for TGA-IR experiments
• Software: OMNIC Series (data collection), OMNIC Specta (multi-component search and processing)
• Reference spectral libraries: Georgia State Forensics Library; Hummel Polymers Library; Polymers and Additives Library

Methodology

The study contrasts the traditional iterative search-and-subtract workflow with the OMNIC Specta multi-component search. Conventional deconvolution involves: a single-spectrum search, manual transfer of a best-match into the workspace, and iterative subtraction using a user-chosen scaling factor k (Original – k*Match), followed by repeat searches on the residual. This approach is operator-dependent and sensitive to peak shifts, lineshape changes and choice of k.

OMNIC Specta uses a combinatorial, multi-iteration search algorithm: several top-match spectra are combined and re-evaluated against the full library in subsequent passes. Weighting factors are returned for component spectra to indicate their relative contribution to the composite. Spectra were pre-treated using ATR correction and baseline adjustments (Advanced ATR Correction Algorithm) before searching. TGA-IR spectra (gas-phase) were exported from OMNIC Series into OMNIC Specta without additional processing for multi-component identification.

Key results and discussion

• Pharmaceutical mixture: A mixture of acetaminophen, acetylsalicylic acid and caffeine analyzed on diamond-ATR produced a spectrum where a single conventional search identified the dominant component but did not clearly reveal the others. The OMNIC Specta multi-component search correctly identified all three ingredients consistently across iterative runs, with stable weighting factors indicating reproducible sampling of combinations rather than fixation on a single initial hit.
• Plastics and additives: A micro-scrape from a monitor chassis (ABS/PC bulk polymer) produced strong matches to the base polymer in a single-component search while small additional peaks (1000–1400 cm-1) were unexplained. A two-component OMNIC Specta search attributed the residual features to the flame retardant tetrabromobisphenol (TBBP), producing a composite spectrum that visually and quantitatively matched the raw data much more closely than the single-hit result. Subsequent top hits consistently paired similar polymer formulations with TBBP, demonstrating algorithmic stability.
• TGA-IR (deformulation): Analysis of evolved gases from an adhesive and an agricultural polymer showed simultaneous emission of multiple species. OMNIC Specta reconstructed composite spectra containing three or more gases (e.g., acetic acid, CO2, water) that matched extracted spectra from OMNIC Series very tightly. Because gas-phase spectra are nearly strictly additive, the multi-component algorithm excelled and removed the need for user-guided iterative subtraction.

Discussion points:

• The algorithm reduces operator bias by eliminating the manual k-factor choice and by sampling many component combinations rather than relying solely on the top initial hit.
• Weighting factors are indicative of relative spectral contributions but should not be treated as absolute concentrations because library spectra are normalized and the algorithm assumes similar absorptivity among components.
• Small peak shifts and lineshape changes (e.g., hydrogen-bonding induced shifts) remain a source of residual mismatch in subtraction methods; OMNIC Specta’s combinatorial approach mitigates but does not fully obviate effects of spectral perturbation.
• Processing speed scales with library size; reasonable library sizes yield substantially faster and more consistent results than manual deconvolution.

Benefits and practical applications

• Consistent, operator-independent identification of multi-component mixtures increases confidence in routine QC, forensic, pharmaceutical and polymer analyses.
• Efficient detection of low-level additives (e.g., regulated flame retardants) aids compliance with WEEE/RoHS and similar regulations.
• Rapid deconvolution of TGA-IR data simplifies deformulation studies and provides qualitative identification of evolved gases without extensive user expertise.
• Short learning curve and automation enable broader use across laboratory staff with variable experience levels.

Future trends and applications

• Integration of quantitative corrections: coupling multi-component search weightings with component-specific absorptivity or reference concentration data could extend results from qualitative identification toward semi-quantitative analysis.
• Improved handling of spectral perturbations: algorithms that model peak shifts and lineshape changes (e.g., via adaptive fitting or chemometric correction) would further reduce residual artifacts for mixtures with strong intermolecular interactions.
• Expanded spectral libraries and machine-learning ranking: larger, curated libraries together with ML-driven pre-filtering may improve speed and reduce false positives for complex matrices.
• Broader coupling with hyphenated techniques (e.g., GC-IR, LC-IR) and cloud-based searchable libraries could enhance forensic and regulatory workflows.

Conclusion

OMNIC Specta’s multi-component search algorithm represents a practical advance for FT-IR and TGA-IR mixture analysis. By combinatorially sampling library spectra and returning stable weightings, the tool minimizes operator-dependent steps, produces reproducible identifications in complex samples, and handles simultaneous gas-phase emissions effectively. While quantitative interpretation of weightings is limited by normalization assumptions, the approach significantly improves qualitative deconvolution speed and reliability across pharmaceutical, polymer and deformulation use cases.

References

1. Thermo Scientific Application Note AN50581, Advanced ATR Correction Algorithm.
2. Waste Electrical and Electronic Equipment Directive; Restriction of Hazardous Substances Directive (WEEE/RoHS) — regulatory limits on hazardous additives in electronic waste.