A chromatographic procedure (HPLC-DAD) using a relatively rapid gradient has been combined with a chemometric curve deconvolution method, multivariate curve resolution-alternating least squares (MCR-ALS), in order to quantify caffeine and chlorogenic acid in green coffee beans. Despite that the HPLC analysis (at these specific operating conditions) presents some coeluting peaks, MCR-ALS allowed their resolution and, consequently, the creation of a calibration curve to be used for the quantification of the analytes of interest; this procedure led to a high accuracy in the quantification of caffeine and chlorogenic acid present in the samples. In a second part of this study, the possibility of classifying the green coffee beans on the basis of their cultivar (Arabica or Robusta), by partial least squares discriminant analysis (PLS-DA) and soft independent modeling of class analogies (SIMCA), has been explored. SIMCA resulted in 100% of sensitivity and specificity for the Arabica class, while for the Robusta, it reached 66.7% of sensitivity and 100% of specificity, or 100% of sensitivity and 100% of specificity, depending on the extraction procedure followed prior to the chromatographic analysis; PLS-DA achieved 100% of correct classification independently of the procedure used for the extraction.

Simultaneous quantification of caffeine and chlorogenic acid in coffee green beans and varietal classification of the samples by HPLC-DAD coupled with chemometrics

Biancolillo A.;
2018-01-01

Abstract

A chromatographic procedure (HPLC-DAD) using a relatively rapid gradient has been combined with a chemometric curve deconvolution method, multivariate curve resolution-alternating least squares (MCR-ALS), in order to quantify caffeine and chlorogenic acid in green coffee beans. Despite that the HPLC analysis (at these specific operating conditions) presents some coeluting peaks, MCR-ALS allowed their resolution and, consequently, the creation of a calibration curve to be used for the quantification of the analytes of interest; this procedure led to a high accuracy in the quantification of caffeine and chlorogenic acid present in the samples. In a second part of this study, the possibility of classifying the green coffee beans on the basis of their cultivar (Arabica or Robusta), by partial least squares discriminant analysis (PLS-DA) and soft independent modeling of class analogies (SIMCA), has been explored. SIMCA resulted in 100% of sensitivity and specificity for the Arabica class, while for the Robusta, it reached 66.7% of sensitivity and 100% of specificity, or 100% of sensitivity and 100% of specificity, depending on the extraction procedure followed prior to the chromatographic analysis; PLS-DA achieved 100% of correct classification independently of the procedure used for the extraction.
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11697/139310
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