Chemometric Analysis of a Ternary Mixture of Caffeine, Quinic Acid, and Nicotinic Acid by Terahertz Spectroscopy
Issued Date
2022-10-11
Resource Type
eISSN
24701343
Scopus ID
2-s2.0-85136969277
Journal Title
ACS Omega
Volume
7
Issue
40
Start Page
35783
End Page
35791
Rights Holder(s)
SCOPUS
Bibliographic Citation
ACS Omega Vol.7 No.40 (2022) , 35783-35791
Suggested Citation
Loahavilai P., Datta S., Prasertsuk K., Jintamethasawat R., Rattanawan P., Chia J.Y., Kingkan C., Thanapirom C., Limpanuparb T. Chemometric Analysis of a Ternary Mixture of Caffeine, Quinic Acid, and Nicotinic Acid by Terahertz Spectroscopy. ACS Omega Vol.7 No.40 (2022) , 35783-35791. 35791. doi:10.1021/acsomega.2c03808 Retrieved from: https://repository.li.mahidol.ac.th/handle/123456789/84045
Title
Chemometric Analysis of a Ternary Mixture of Caffeine, Quinic Acid, and Nicotinic Acid by Terahertz Spectroscopy
Other Contributor(s)
Abstract
Caffeine, quinic acid, and nicotinic acid are among the significant chemical determinants of coffee quality. This study develops a chemometric model to quantify these compounds in ternary mixtures analyzed by terahertz time-domain spectroscopy (THz-TDS). A data set of 480 THz spectra was obtained from 80 samples. Combinations of data preprocessing methods, including normalization (Z-score, min-max scaling, Mie baseline removal) and dimensionality reduction (principal component analysis (PCA), factor analysis (FA), independent component analysis (ICA), locally linear embedding (LLE), non-negative matrix factorization (NMF), isomap), and prediction models (partial least-squares regression (PLSR), support vector regression (SVR), multilayer perceptron (MLP), convolutional neural network (CNN), gradient boosting) were analyzed for their prediction performance (totaling to 4,711,685 combinations). Results show that the highest quantification performance was achieved at a root-mean-square error of prediction (RMSEP) of 0.0254 (dimensionless mass ratio), using min-max scaling and factor analysis for data preprocessing and multilayer perceptron for prediction. Effects of preprocessing, comparison of prediction models, and linearity of data are discussed.