Quantification of Coffea arabica and Coffea canephora var. robusta concentration in blends by means of synchronous fluorescence and UV-Vis spectroscopies
Anna Dankowska , Anna Domagała , Wojciech Kowalewski
AbstractThe potential of fluorescence, UV-Vis spectroscopies as well as the low- and mid-level data fusion of both spectroscopies for the quantification of concentrations of roasted Coffea Arabica and Coffea canephora var. robusta in coffee blends was investigated. Principal component analysis was used to reduce data multi-dimensionality. To calculate the level of undeclared addition, multiple linear regression (PCA-MLR) models were used with lowest root mean square error of calibration (RMSEC) of 3.6% and root mean square error of cross-validation (RMSECV) of 7.9%. LDA analysis was applied to fluorescence intensities and UV spectra of Coffea arabica, canephora samples, and their mixtures in order to examine classification ability. The best performance of PCA-LDA analysis was observed for data fusion of UV and fluorescence intensity measurements at wavelength interval of 60 nm. LDA showed that data fusion can achieve over 96% of correct classifications (sensitivity) in the test set and 100% of correct classifications in the training set, with low-level data fusion. The corresponding results for individual spectroscopies ranged from 90% (UV-Vis spectroscopy) to 77% (synchronous fluorescence) in the test set, and from 93% to 97% in the training set. The results demonstrate that fluorescence, UV, and visible spectroscopies complement each other, giving a complementary effect for the quantification of roasted Coffea Arabica and Coffea canephora var. robusta concentration in blends.
|Journal series||Talanta, ISSN 0039-9140, (A 40 pkt)|
|Publication size in sheets||0.5|
|Keywords in English||food fraud, coffee authenticity, fluorescence spectroscopy, UV-Vis spectroscopy, data fusion, multivariate data analysis|
|Score||= 40.0, 11-03-2020, ArticleFromJournal|
|Publication indicators||= 17; : 2016 = 1.27; : 2017 = 4.244 (2) - 2017=3.937 (5)|
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