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

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...

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Published inAnalytical letters Vol. 57; no. 2; pp. 307 - 316
Main Authors Qiu, Weilun, Yi, Rongnan
Format Journal Article
LanguageEnglish
Published Abingdon Taylor & Francis 22.01.2024
Taylor & Francis Ltd
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ISSN0003-2719
1532-236X
DOI10.1080/00032719.2023.2207023

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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.
AbstractList 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.
Author Yi, Rongnan
Qiu, Weilun
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Snippet 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...
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SubjectTerms Algorithms
Animal and vegetable oil
attenuated total reflectance - Fourier transform infrared (ATR-FTIR)
Bandstop filters
Basis functions
chemometrics
Decision analysis
Decision trees
Discriminant analysis
Fourier transforms
Impulse response
Infrared analysis
Infrared spectra
Infrared spectroscopy
Low pass filters
low-pass/band-stop filter
multilayer perceptron neural network (MLP)
Multilayer perceptrons
Neural networks
Pattern recognition
Performance evaluation
Reflectance
Spectrum analysis
Support vector machines
Vegetable oils
Wavelet transforms
Title Characterization of Animal and Vegetable Oil by Attenuated Total Reflectance - Fourier Transform Infrared (ATR-FTIR) Spectroscopy with Supervised Pattern Recognition and Filter Algorithm
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