The current study presents an application of near infrared spectroscopy for identification and quantification of the fraudulent addition of barley in roasted and ground coffee samples. Nine different types of coffee including pure Arabica, Robusta and mixtures of them at different roasting degrees were blended with four types of barley. The blending degrees were between 2 and 20 weight percent of barley. D-optimal design was applied to select 100 and 30 experiments to be used as calibration and test set, respectively. Partial least squares regression (PLS) was employed to build the models aimed at predicting the amounts of barley in coffee samples. In order to obtain simplified models, taking into account only informative regions of the spectral profiles, a genetic algorithm (GA) was applied. A completely independent external set was also used to test the model performances. The models showed excellent predictive ability with root mean square errors (RMSE) for the test and external set equal to 1.4% w/w and 0.8% w/w, respectively.

Detection of addition of barley to coffee using near infrared spectroscopy and chemometric techniques

LEARDI, RICCARDO;OLIVERI, PAOLO;CASOLINO, MARIA CHIARA;LANTERI, SILVIA
2012-01-01

Abstract

The current study presents an application of near infrared spectroscopy for identification and quantification of the fraudulent addition of barley in roasted and ground coffee samples. Nine different types of coffee including pure Arabica, Robusta and mixtures of them at different roasting degrees were blended with four types of barley. The blending degrees were between 2 and 20 weight percent of barley. D-optimal design was applied to select 100 and 30 experiments to be used as calibration and test set, respectively. Partial least squares regression (PLS) was employed to build the models aimed at predicting the amounts of barley in coffee samples. In order to obtain simplified models, taking into account only informative regions of the spectral profiles, a genetic algorithm (GA) was applied. A completely independent external set was also used to test the model performances. The models showed excellent predictive ability with root mean square errors (RMSE) for the test and external set equal to 1.4% w/w and 0.8% w/w, respectively.
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11567/378462
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