Abstract：
:This article aims to develop and validate a multivariate model for quantifying Robusta-Arabica coffee blends by combining near infrared spectroscopy (NIRS) and total reflection X-ray fluorescence (TXRF). For this aim, 80 coffee blends (0.0-33.0%) were formulated. NIR spectra were obtained in the wavenumber range 11100-4950 cm-1 and 14 elements were determined by TXRF. Partial least squares models were built using data fusion at low and medium levels. In addition, selection of predictive variables based on their importance indices (SVPII) improved results. The best model reduced the number of variables from 1114 to 75 and root mean square error of prediction from 4.1% to 1.7%. SVPII selected NIR regions correlated with coffee components, and the following elements were chosen: Ti, Mn, Fe, Cu, Zn, Br, Rb, Sr. The model interpretation took advantage of the data fusion between atomic and molecular spectra in order to characterize the differences between these coffee varieties.
journal_name
Food Chemjournal_title
Food chemistryauthors
Assis C,Gama EM,Nascentes CC,de Oliveira LS,Anzanello MJ,Sena MMdoi
10.1016/j.foodchem.2020.126953subject
Has Abstractpub_date
2020-04-30 00:00:00pages
126953eissn
0308-8146issn
1873-7072pii
S0308-8146(20)30815-3journal_volume
325pub_type
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