Combining mid infrared spectroscopy and paper spray mass spectrometry in a data fusion model to predict the composition of coffee blends

•A PLS model predict and characterize the composition of coffee blends.•Data fusion of FTIR and paper spray MS for predicting Robusta-Arabica blends.•Variable selection improved the models and facilitated spectral interpretation.•The best model allowed matrix characterization by correlating FTIR and...

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Bibliographic Details
Published in:Food chemistry 2019-05, Vol.281, p.71-77
Main Authors: Assis, Camila, Pereira, Hebert Vinicius, Amador, Victoria Silva, Augusti, Rodinei, de Oliveira, Leandro Soares, Sena, Marcelo Martins
Format: Article
Language:English
Subjects:
Coffea - chemistry
Coffee - chemistry
Coffee authentication
Food Analysis
Food Handling
Mid infrared spectroscopy
Multi-block regression
Multivariate calibration
Paper spray mass spectrometry
Reproducibility of Results
Spectrophotometry, Infrared
Spectroscopy, Fourier Transform Infrared
Variable selection
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Summary:•A PLS model predict and characterize the composition of coffee blends.•Data fusion of FTIR and paper spray MS for predicting Robusta-Arabica blends.•Variable selection improved the models and facilitated spectral interpretation.•The best model allowed matrix characterization by correlating FTIR and MS spectra.•Main discriminant markers were trigonelline, chlorogenic acids, sugars, quinic acid. This paper describes a robust multivariate model for quantifying and characterizing blends of Robusta and Arabica coffees. At different degrees of roasting, 120 ground coffee blends (0.0–33.0%) were formulated. Spectra were obtained by two different techniques, attenuated total reflectance Fourier transform infrared (ATR-FTIR) spectroscopy and paper spray mass spectrometry (PS-MS). Partial least squares (PLS) models were built individually with the two types of spectra. Nevertheless, better predictions were obtained by low and medium-level data fusion, taking advantage from the synergy between these two data sets. Data fusion models were improved by variable selection, using genetic algorithms (GA) and ordered predictors selection (OPS). The smallest prediction errors were provided by OPS low-level data fusion model. The number of variables used for regression was reduced from 2145 (full spectra) to 230. Model interpretation was performed by assigning some of the selected variables to specific coffee components, such as trigonelline and chlorogenic acids.
ISSN:0308-8146
1873-7072
DOI:10.1016/j.foodchem.2018.12.044