Forensic Analysis of Textile Synthetic Fibers Using a FT-IR Spectroscopy Approach.

Area of Science:

• Forensic Science
• Analytical Chemistry
• Materials Science

Background:

• Synthetic fibers are crucial trace evidence in criminal investigations.
• Accurate fiber analysis links suspects, victims, and crime scenes.
• Traditional methods require robust classification techniques.

Purpose of the Study:

• To classify 138 synthetic textile fibers using FT-IR spectroscopy.
• To evaluate multivariate statistical methods and machine learning for fiber classification.
• To establish a reliable method for forensic fiber analysis.

Main Methods:

• Fourier Transform Infrared Spectroscopy (FT-IR) for spectral acquisition.
• Preprocessing techniques: Savitzky-Golay first derivative and Standard Normal Variate (SNV).
• Multivariate analysis: Principal Component Analysis (PCA) for pattern observation and clustering.
• Machine learning classification: Soft Independent Modeling by Class Analogy (SIMCA).

Main Results:

• PCA revealed unique patterns and sample clusters for different fiber types.
• SIMCA model achieved high classification accuracy for synthetic fibers.
• 97.1% of test samples were correctly classified at a 5% significance level.

Conclusions:

• FT-IR combined with PCA and SIMCA offers a powerful approach for synthetic fiber classification.
• This method enhances the evidential value of textile fibers in forensic science.
• The study validates a robust analytical framework for trace evidence analysis.