The use of advanced chemometric techniques and trace element levels for controlling the authenticity of organic coffee

    Authors

    R. M. Barbosa; B. L. Batista; R. M. Varrique; V. A. Coelho; A. D. Campiglia;F. Barbosa

    Comments

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    Abbreviated Journal Title

    Food Res. Int.

    Keywords

    Chemometric; ICP-MS; Trace elements; Food samples; Quality control; PLASMA-MASS SPECTROMETRY; RARE-EARTH-ELEMENTS; NEAR-INFRARED; SPECTROSCOPY; SUPPORT VECTOR MACHINES; DATA MINING TECHNIQUES; ICP-AES; GEOGRAPHIC ORIGIN; ROASTED COFFEE; GREEN COFFEE; SAMPLES; Food Science & Technology

    Abstract

    This study examines the application of advanced data mining techniques associated with trace element concentrations for authenticity evaluation of organic coffee samples. Fifty-four coffee samples (20 organic and 34 non-organic) were evaluated according to their macro and microelement contents to establish reference ranges and identify differences in patterns of chemical elements in both crop procedures. Inductively coupled plasma mass spectrometry (ICP-MS) was used for the determination of thirty-eight chemical elements (Al, As, B, Ba, Be, Bi, Cd, Ce, Co, Cr, Cs, Cu, Dy, Eu, Er, Gd, Ge, Ho, La, Lu, Mn, Mo, Ni, Pb, Rb, Se, Sm, Sr, Yb, W, U, Ta, Tb, Th, Ti, Ti, Zr and Zn). In general, higher levels of chemical elements were found in conventional coffee compared with organic samples. The classification of samples was performed by using three well known data mining algorithms, named Multilayer Perceptron (MLP), Support Vector Machine (SVM) and Naive Bayes (NB). When all analyzed variables were considered, a 96.3% accuracy level was obtained with the three algorithms. The level of accuracy remained at 96.3% for SVM and MLP and rose to 98.2 for NB when only eight of the investigated minerals - i.e. Zn, Se, Ba, U, Dy, Ti, Th, Mo - were used for chemometric analysis. This level of accuracy provides a strong foundation to propose the combination of trace element spectral fingerprints with data mining techniques as a valuable tool to evaluate the authenticity of organic coffee samples. (C) 2013 Elsevier Ltd. All rights reserved.

    Journal Title

    Food Research International

    Volume

    61

    Publication Date

    1-1-2014

    Document Type

    Article

    Language

    English

    First Page

    246

    Last Page

    251

    WOS Identifier

    WOS:000338389500029

    ISSN

    0963-9969

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