Chocolate Sample Classification by Principal Component Analysis of Preprocessed Terahertz Transmission Spectra

The efficiency of chocolate sample classification by type and manufacturer is demonstrated using a spectralprint method and THz transmission spectra. Their baselines are determined using the adaptive iteratively reweighted penalized least squares (airPLS) method to suppress noise and the Fabry–Perot...

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Bibliographic Details
Published in:Journal of applied spectroscopy 2022-05, Vol.89 (2), p.251-255
Main Authors: Khodasevich, M. A., Lyakhnovich, A. V., Eriklioglu, H.
Format: Article
Language:English
Subjects:
Analysis
Analytical Chemistry
Atomic/Molecular Structure and Spectra
Chocolate
Classification
Cluster analysis
Physics
Physics and Astronomy
Principal components analysis
Regression analysis
Spectra
Support vector machines
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Summary:The efficiency of chocolate sample classification by type and manufacturer is demonstrated using a spectralprint method and THz transmission spectra. Their baselines are determined using the adaptive iteratively reweighted penalized least squares (airPLS) method to suppress noise and the Fabry–Perot effect. The classification was carried out by constructing a low-dimensional space of the principal components of the baselines and applying cluster analysis methods in this space. The precision and recall values of the classification of chocolate samples by the k-means, classification and regression tree, and hierarchical cluster analysis methods are 0.85 and 0.83, 0.91 and 0.90, and 0.94 and 0.93, respectively. The support vector machine is successfully applied to two cases where pairwise classification is most problematic.
ISSN:0021-9037
1573-8647
DOI:10.1007/s10812-022-01351-3