Open AccessUtilization of UV-visible and FTIR spectroscopy coupled with chemometrics for differentiation of Indonesian tea: an exploratory study
Author(s) -
Agustina Ari Murti Budi Hastuti
Publication year - 2022
Publication title -
indonesian journal of pharmacy
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.153
H-Index - 6
eISSN - 2338-9486
pISSN - 2338-9427
DOI - 10.22146/ijp.3795
Subject(s) - fourier transform infrared spectroscopy , chemometrics , principal component analysis , black tea , spectroscopy , ultraviolet visible spectroscopy , pattern recognition (psychology) , analytical chemistry (journal) , chemistry , mathematics , artificial intelligence , computer science , chromatography , physics , food science , optics , organic chemistry , quantum mechanics
Ultraviolet (UV)-visible and Fourier-transformed infrared (FTIR) spectroscopy are two of the most popular and readily available laboratory instruments. Fingerprinting analysis of the UV-visible and FTIR spectra has been applied for food classification and authentication studies. In this study, the UV-visible and FTIR spectra of brewed tea, and their data fusion data sets, were used to build models for the classification of tea based on tea types and origins. The study included black and green tea samples from several provinces in Sumatra and Java Islands (Indonesia). Chemometric models of principal component analysis (PCA), k-nearest neighbor (kNN), and logistic regression were developed for classification purposes. All PCA models were able to well-separate the tea groups. kNN and logistic regression models based on UV-visible spectra successfully classified green and black tea with >0.8 classification accuracy. The kNN model of FTIR spectra had good accuracy (0.903) for classifying tea based on its origin. ReliefF algorithm was employed to select the best features among the data fusion data sets of UV-visible and FTIR spectra. The data fusion data sets of UV-visible and FTIR spectra demonstrated good separation of tea types and origins with a high area under the ROC curve (>0.8) and moderate accuracy (0.548). Therefore, UV-visible and FTIR spectroscopy may provide complementary information for tea classification based on tea types and origins.