Powerful New Identification Tools with OMNIC Specta Software

Significance of the topic

The identification of unknown materials and the deconvolution of multi-component infrared spectra are routine but often technically challenging tasks in analytical laboratories. Rapid, reproducible, and robust mixture analysis reduces operator dependence, accelerates workflows (e.g., QA/QC, forensic, pharmaceutical QC, polymer compliance), and improves confidence in decisions driven by spectral evidence. Software tools that automate multi-component identification therefore have immediate practical value across industries that rely on FT-IR and TGA-IR data.

Objectives and overview of the study

This technical note demonstrates the capability of Thermo Scientific OMNIC Specta software to perform automated multi-component identification from FT-IR and TGA-IR spectra. The goal is to show how an iterative, library-based search algorithm overcomes limitations of the traditional search-and-subtract approach and to illustrate performance on representative real-world samples: pharmaceutical mixtures, polymer/additive detection in plastics, and gas-phase products from TGA-IR experiments.

Methodology

The study compares standard single-hit spectral searching followed by manual subtraction with OMNIC Specta’s multi-component searching algorithm. Key methodological steps include:

• Collect ATR-FTIR spectra (diamond ATR) from mixtures and plastics using Thermo Scientific Nicolet FT-IR systems.
• Apply baseline and ATR corrections (Advanced ATR Correction Algorithm).
• Perform library searches against curated reference libraries (forensics, polymers/additives).
• Use OMNIC Specta’s iterative combinatorial search: best-matches are combined with the entire database across multiple iterations and weighted to build composite spectra that best represent the measured spectrum.
• For TGA-IR, extract spectra at temperature points from OMNIC Series files and run multi-component searches on gas-phase spectra.

Used instrumentation

• Thermo Scientific Nicolet iS10 FT-IR spectrometer (primary instrument used).
• Diamond ATR accessory for solid/liquid sampling.
• Thermo Scientific Nicolet iS5 and iS50 mentioned as compatible for most experiments; TGA-IR experiments require iS10 or iS50 (iS5 not supported for TGA accessory).
• Sample-compartment TGA accessory coupled to a TGA furnace for evolved gas analysis (TGA-IR).
• Thermo Scientific OMNIC Series software (data acquisition) and OMNIC Specta software (multi-component analysis).
• Reference spectral libraries: Georgia State Forensics Library, Hummel Polymers Library, Polymers and Additives Library.

Main results and discussion

Pharmaceutical mixtures:
The multi-component algorithm correctly identified all three components of a pharmaceutical mixture (acetaminophen, acetyl salicylic acid, and caffeine) where a single-hit search only made the primary component obvious and required expert interpretation for additional ingredients. OMNIC Specta returned consistent component lists and weighting factors across multiple reported matches, illustrating algorithmic stability and reproducibility. The weighting factors are qualitative estimates because library spectra are normalized, so quantitative concentration values cannot be directly inferred without calibration.

Plastics and flame-retardant detection:
An ABS/polycarbonate (ABS/PC) sample from a monitor chassis exhibited minor peaks not explained by the bulk polymer in a standard search. The multi-component search (two-component mode) produced a composite match pairing the base polymer with tetrabromobisphenol (TBBP), a regulated flame retardant, giving a visually excellent composite spectrum that accounted for both dominant polymer bands and the smaller additive-associated features. Repeated top matches showed consistent identification of the bulk polymer and TBBP, confirming robustness against slight formulation differences.

TGA-IR (deformulation of evolved gases):
TGA-IR spectral extracts from adhesives and an agricultural polymer released multiple overlapping gases (e.g., acetic acid, CO2, water, and other organics). OMNIC Specta’s multi-component search built composites that matched the experimental gas-phase spectra tightly, where conventional sequential search-and-subtract is more laborious and operator-dependent. Because gas-phase spectra are close to additive, the algorithm excels at deconvolution in TGA-IR use cases.

Limitations and practical considerations:

• The software’s reported weighting factors are not absolute concentrations because reference spectra are normalized and do not reflect differing absorptivities.
• Small peak shifts and line-broadening due to interactions (e.g., hydrogen bonding) can produce derivative-like residuals that complicate subtraction-based methods; OMNIC Specta’s combinatorial approach mitigates but does not eliminate interpretive issues from spectral distortions.
• Processing speed depends on library size; reasonable library sizes yield fast results, but very large databases increase compute time.

Benefits and practical applications

• Automation and reproducibility: Eliminates manual selection of subtraction scaling (k-factor), reducing operator-to-operator variability and the need for extensive training.
• Efficiency: Multi-component searches deliver results faster than iterative manual deconvolution for routine library sizes, increasing laboratory throughput.
• Applicability across domains: Useful in pharmaceutical formulation analysis, polymer/additive detection and compliance (e.g., RoHS/WEEE concerns), forensic identification, and TGA-IR evolved gas analysis.
• Improved detectability of minor components: The algorithm can reveal low-concentration additives or contaminants that are otherwise obscured by dominant matrix spectra.

Future trends and opportunities

• Quantification enhancements: Combining library-based multi-component identification with calibration models or concentration-sensitive reference spectra could enable semi-quantitative or quantitative outputs.
• Expanded and curated libraries: Larger, high-quality, application-specific libraries (including gas-phase references) will further improve identification accuracy and speed.
• Machine learning integration: Supervised or unsupervised learning approaches could augment combinatorial searches to better handle peak shifts, matrix effects, and non-linear mixing behaviors.
• Cross-technique workflows: Integration with Raman libraries and hyphenated techniques (GC-IR, TGA-MS/IR) will broaden applicability, especially for complex mixtures and volatile profiling.
• Real-time and in-line monitoring: Optimized search algorithms could be adapted for process analytical technology (PAT) and continuous monitoring where rapid, automated deconvolution is required.

Conclusion

OMNIC Specta’s multi-component search algorithm represents a significant advance for routine FT-IR and TGA-IR analysis by automating combinatorial spectral matching, improving reproducibility, and reducing dependence on operator expertise. Demonstrations on pharmaceutical mixtures, polymer/additive detection, and evolved-gas spectra show that the approach reliably identifies multiple components and produces composite spectra that closely match experimental data. While quantitative interpretation requires additional calibration, the method substantially simplifies qualitative mixture identification and speeds laboratory workflows.

References

1. Thermo Scientific Application Note AN50581: Advanced ATR Correction Algorithm.
2. Waste Electrical and Electronic Equipment Directive and Restriction on Hazardous Substances Directive (WEEE/RoHS) — regulatory guidance on restricted toxic substances in waste materials.