Positive and Nondestructive Identification of Acrylic-Based Coatings

Significance of the Topic

Acrylic-based coatings are widely employed across industrial, decorative, ink, powder, and wall‐covering markets due to their high performance and low VOC emissions. Ensuring the correct coating formulation on a substrate is critical for durability and performance. Portable, nondestructive methods that can rapidly verify coating identity on‐site support quality assurance and longevity assessments without damaging the coated surface.

Objectives and Study Overview

This work evaluates the capability of the Agilent 4300 Handheld FTIR to distinguish 14 industrial acrylic coatings of similar chemistry. Initially, a standard spectral library search was tested but showed limited discrimination among closely related formulations. A more robust classification approach using partial least squares discriminant analysis (PLS-DA) was then implemented, coupled with Agilent MicroLab PC’s Component Reporting, to achieve unequivocal identification of each coating in handheld, field‐deployable fashion.

Methodology

1. Sample Preparation: Fourteen proprietary acrylic coatings (labeled A–N) were spray coated onto 4×9 inch Q‐panels; ten spectra were collected at random locations per panel to capture surface inhomogeneity.
   
2. Spectral Acquisition: For each coating, eight spectra populated the calibration set, and two served as unknowns for method evaluation.
   
3. Data Processing: Spectra were preprocessed by mean centering, multiplicative scatter correction, and nine‐point Savitzky-Golay first derivative prior to analysis.
   
4. Library Search: A similarity‐match algorithm in MicroLab PC provided initial identification but showed closely spaced quality values among secondary hits.
   
5. PLS-DA Modeling: Five sequential PLS-DA models (R2 = 0.984–0.999; 3–6 latent factors) were built to progressively separate all coatings into subgroups, then individual formulations.
   

Used Instrumentation

• Agilent 4300 Handheld FTIR spectrometer with diffuse reflectance and ATR sampling interfaces.
  
• Diffuse reflectance interface favored for deeper IR penetration, improved band intensity, and truly nondestructive measurements.
  
• Spectral range: 5,200–650 cm–1; resolution: 8 cm–1; 128 co-added interferograms; acquisition time < 40 s per spectrum.
  

Key Results and Discussion

Diffuse reflectance spectra delivered richer information than ATR, revealing binder and additive bands with higher reproducibility and no sample damage. Library matching correctly identified all coatings but occasionally yielded ambiguous secondary hits (quality values up to 0.997). PLS-DA classification provided statistically robust separation of similar formulations. By combining five PLS-DA calibration models within MicroLab PC Component Reporting logic (using thresholds and Mahalanobis distance), each unknown spectrum was automatically routed to the proper model, achieving 100% correct identification of all 14 coatings in handheld operation.

Benefits and Practical Applications

• Truly nondestructive, in situ coating identification without sample removal.
  
• Rapid (under 40 s) and portable analysis across diverse coated articles.
  
• High confidence in differentiating closely related formulations using multivariate chemometrics.
  
• Field-deployable solution for quality control, maintenance, and failure analysis.
  

Future Trends and Opportunities

• Expansion to other polymeric and multi‐layer coatings using advanced chemometric libraries.
  
• Integration of real-time IoT connectivity and cloud‐based spectral databases for global QA networks.
  
• Development of enhanced algorithms (machine learning, deep learning) for even finer discrimination in complex matrices.
  
• Extension of handheld FTIR applications into environmental monitoring, historical artifact conservation, and plastic sorting.
  

Conclusion

The Agilent 4300 Handheld FTIR, combined with PLS-DA and MicroLab PC Component Reporting, delivers a robust, nondestructive approach to differentiate and identify acrylic coatings of very similar composition. Diffuse reflectance sampling enhances spectral detail, while integrated chemometric logic automates model selection for reliable field use.