Mixture resolution according to the percentage of robusta variety in order to detect adulteration in roasted coffee by near infrared spectroscopy

  1. Pizarro, C. 1
  2. Esteban-Díez, I. 1
  3. González-Sáiz, J.M. 1
  1. 1 Universidad de La Rioja
    info

    Universidad de La Rioja

    Logroño, España

    ROR https://ror.org/0553yr311

Revista:
Analytica Chimica Acta

ISSN: 0003-2670

Año de publicación: 2007

Volumen: 585

Número: 2

Páginas: 266-276

Tipo: Artículo

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DOI: 10.1016/J.ACA.2006.12.057 PMID: 17386674 SCOPUS: 2-s2.0-33846637455 WoS: WOS:000244328100010 GOOGLE SCHOLAR

Otras publicaciones en: Analytica Chimica Acta

Resumen

Near infrared spectroscopy (NIRS), combined with multivariate calibration methods, has been used to quantify the robusta variety content of roasted coffee samples, as a means for controlling and avoiding coffee adulteration, which is a very important issue taking into account the great variability of the final sale price depending on coffee varietal origin. In pursuit of this aim, PLS regression and a wavelet-based pre-processing method that we have recently developed called OWAVEC were applied, in order to simultaneously operate two crucial pre-processing steps in multivariate calibration: signal correction and data compression. Several pre-processing methods (mean centering, first derivative and two orthogonal signal correction methods, OSC and DOSC) were additionally applied in order to find calibration models with as best a predictive ability as possible and to evaluate the performance of the OWAVEC method, comparing the respective quality of the different regression models constructed. The calibration model developed after pre-processing derivative spectra by OWAVEC provided high quality results (0.79% RMSEP), the percentage of robusta variety being predicted with a reliability notably better than that associated with the models constructed from raw spectra and also from data corrected by other orthogonal signal correction methods, and showing a higher model simplicity. © 2007 Elsevier B.V. All rights reserved.