Fluorescence spectroscopy as tool for the geographical discrimination of coffees produced in different regions of Minas Gerais State in Brazil

Abstract The designation of origin of high-value agricultural and food products has become increasingly relevant for the producers, since it allows the consumers to relate the singular characteristics of their preferred product to its respective provenance. Thus, coffee producers are in pursuit of ways to certify their products according to their authenticity pertaining provenance. Fluorescence spectroscopy was applied in order to develop a geographical discrimination model of coffees produced in Minas Gerais State, Brazil. PARAFAC, NPLS-DA and UPLS-DA were used in order to discriminate samples produced in four major production areas in Minas Gerais, namely Cerrado Mineiro (CM), Matas de Minas (MM), Norte de Minas (NM) e Sul de Minas (SM). The UPLS-DA presented the best results, with f-scores for CM and SM higher than 0.8, for both training and test sets, which indicates good classification. MM model presented a good f-score for the training set (1.000), but a poor result was obtained for the test set (0.250), mainly due to false positive samples. NM models presented an intermediary result, with a f-score of 0.913 for training set and 0.625 for test set. The proposed method requires a simple sample pre-treatment, it is fast and can be used for the determination of the geographical origin of coffees produced in Minas Gerais State.

[1]  F. M. Borém,et al.  Spatial distribution of coffees from Minas Gerais state and their relation with quality , 2011 .

[2]  E. Roussakis,et al.  Quercetin exhibits a specific fluorescence in cellular milieu: a valuable tool for the study of its intracellular distribution. , 2007, Journal of agricultural and food chemistry.

[3]  A. Stalmach,et al.  Polyphenolic and hydroxycinnamate contents of whole coffee fruits from China, India, and Mexico. , 2013, Journal of agricultural and food chemistry.

[4]  Carlos M. Silva,et al.  Discriminant analysis for unveiling the origin of roasted coffee samples: A tool for quality control of coffee related products , 2017 .

[5]  Adriana Farah,et al.  Phenolic compounds in coffee , 2006 .

[6]  J. Pawliszyn,et al.  Headspace solid-phase microextraction-gas chromatographic-time-of-flight mass spectrometric methodology for geographical origin verification of coffee. , 2008, Analytica chimica acta.

[7]  R. Bro PARAFAC. Tutorial and applications , 1997 .

[8]  R. Bro Multiway calibration. Multilinear PLS , 1996 .

[9]  B. Chandravanshi,et al.  Profiling of phenolic compounds using UPLC–MS for determining the geographical origin of green coffee beans from Ethiopia , 2016 .

[10]  V. L. Filardi,et al.  Classification of food vegetable oils by fluorimetry and artificial neural networks , 2015 .

[11]  C. Máguas,et al.  Stable isotope analysis for green coffee bean: a possible method for geographic origin discrimination. , 2009 .

[12]  R. Bro,et al.  Handling of Rayleigh and Raman scatter for PARAFAC modeling of fluorescence data using interpolation , 2006 .

[13]  Vincent Baeten,et al.  Evaluation of the overall quality of olive oil using fluorescence spectroscopy. , 2015, Food chemistry.

[14]  A. C. Freitas,et al.  Coffee geographic origin — an aid to coffee differentiation , 1999 .

[15]  F. M. Borém,et al.  Potential markers of coffee genotypes grown in different Brazilian regions: A metabolomics approach , 2014 .

[16]  E. Bona,et al.  Geographical and genotypic segmentation of arabica coffee using self-organizing maps , 2014 .

[17]  J. Sádecká,et al.  Fluorescence spectroscopy and chemometrics in the food classification - : a review , 2018 .

[18]  Age K Smilde,et al.  A Critical Assessment of Feature Selection Methods for Biomarker Discovery in Clinical Proteomics* , 2012, Molecular & Cellular Proteomics.

[19]  G. Woodward The potential effect of excessive coffee consumption on nicotine metabolism: CYP2A6 inhibition by caffeic acid and quercetin , 2008 .

[20]  C. You,et al.  Geographic determination of coffee beans using multi-element analysis and isotope ratios of boron and strontium. , 2014, Food chemistry.

[21]  J. M. Jurado,et al.  Characterization of Mexican coffee according to mineral contents by means of multilayer perceptrons artificial neural networks , 2014 .

[22]  K. Speer,et al.  The lipid fraction of the coffee bean , 2006 .

[23]  J. Wilkinson,et al.  Geographical Indications and “Origin” Products in Brazil – The Interplay of Institutions and Networks , 2017 .

[24]  M. León-Camacho,et al.  Authentication of green coffee varieties according to their sterolic profile , 1998 .

[25]  Y. Hwang,et al.  Binding of caffeine with caffeic acid and chlorogenic acid using fluorescence quenching, UV/vis and FTIR spectroscopic techniques. , 2016, Luminescence : the journal of biological and chemical luminescence.

[26]  E. Bona,et al.  Geographical and genotypic classification of arabica coffee using Fourier transform infrared spectroscopy and radial-basis function networks , 2014 .