Positive and Nondestructive Identification of Acrylic-Based Coatings

Importance of the Topic

Acrylic-based coatings are ubiquitous across industrial, decorative, printing ink, powder, and wall covering markets due to their high performance and low VOC emissions. Ensuring that the correct coating formulation is applied and that its chemistry remains stable under service conditions is critical for product longevity and regulatory compliance. A portable, nondestructive identification method allows engineers and quality teams to verify coating types in situ without damaging the substrate.

Objectives and Overview of the Study

This application note aimed to develop a rapid, nondestructive workflow for positive identification of 14 proprietary acrylic coating formulations with similar binder chemistry. The work evaluates both traditional spectral library search and multivariate classification via Partial Least Squares Discriminant Analysis (PLS-DA) using the Agilent 4300 Handheld FTIR.

Methodology and Instrumentation Used

Sample Preparation and Data Collection:

• Fourteen industrial acrylic coatings (labeled A–N) were spray-applied to metal Q-panels.
• Ten FTIR spectra were acquired per panel at random positions to capture coating inhomogeneity.
• Spectral range: 5200–650 cm–1; resolution: 8 cm–1; 128 co-added scans; acquisition time <40 s per spectrum.

Data Analysis:

• Initial identification via similarity search against an extended diffuse reflectance library.
• PLS-DA classification models built using eight spectra per coating for calibration and two as unknowns.
• Spectral preprocessing: mean centering, multiplicative scatter correction, nine-point Savitzky–Golay first derivative.

Instrumentation Used

The Agilent 4300 Handheld FTIR spectrometer with interchangeable sampling interfaces:

• Diffuse reflectance external interface for nondestructive, deeper penetration spectra.
• Attenuated Total Reflectance (ATR) interface for comparison.

Main Results and Discussion

Library search correctly matched each test spectrum with high hit quality (>0.998 primary matches), but closely similar formulations occasionally yielded ambiguous secondary hits. PLS-DA provided a statistically robust classification by capturing subtle spectral variance:

• Five sequential PLS-DA models achieved R2 values between 0.984 and 0.999 and separated the 14 coatings into distinct groups.
• Model integration via the Agilent MicroLab PC Component Reporting logic enabled automatic selection of the correct calibration and final identification.
• The combined method correctly classified all unknown spectra, offering greater confidence than library search alone.

Benefits and Practical Applications

• Truly nondestructive, field-deployable analysis without sampling or substrate damage.
• Rapid results (<40 s per spectrum) with minimal on-site preparation.
• High confidence in distinguishing closely related coating formulations.
• Utility in quality control, maintenance inspections, and forensic investigations.

Future Trends and Potential Applications

• Expansion of multivariate classification to other polymer and composite materials.
• Integration with cloud-based spectral libraries and AI-driven algorithms for real-time decision support.
• Development of universal portable workflows for on-site process monitoring and regulatory compliance.
• Use in environmental exposure studies to track coating degradation and weathering.

Conclusion

The Agilent 4300 Handheld FTIR combined with PLS-DA and Component Reporting delivers a robust, nondestructive method for accurate identification of similar acrylic coatings in the field. This approach enhances quality assurance and supports rapid, reliable decision-making for coating selection and verification.