Characterization of Animal and Vegetable Oil by Attenuated Total Reflectance – Fourier Transform Infrared (ATR-FTIR) Spectroscopy with Supervised Pattern Recognition and Filter Algorithm

Abstract

The potential of filter algorithms in improving spectral model performance was evaluated. A total of 329 animal and vegetable oil samples were used to collect attenuated total reflectance – Fourier transform infrared (ATR-FTIR) spectra. Fisher discriminant analysis (FDA), support vector machine (SVM), decision tree (DT), K-nearest neighbor analysis (KNN), and multilayer perceptron neural network (MLP) were considered to build models. Three filter algorithms, finite length unit impulse response filter (FIR), infinite length impulse response filter (IIR) and wavelet transform (WT), were evaluated to enhance the performance of the models. The Morlet basis function was the most suitable wavelet transform, accurately classifying 90.3% of the training set and 94.4% of the test set. The MLP algorithms were demonstrated to be superior to the others. The best performance was obtained using low-pass or band-stop filters that provided 100% accuracy with the MLP model. The reported method is demonstrated to be affordable and easy-to-use in forensic analysis.

Keywords:

• Animal and vegetable oil
• attenuated total reflectance – Fourier transform infrared (ATR-FTIR)
• chemometrics
• low-pass/band-stop filter
• multilayer perceptron neural network (MLP)