Key Markers for Coffee Origin and Roasting Degree

More than 800 volatile compounds have been identified in roasted coffee so far (Illy, 2005). These can be divided into different classes, namely furans, pyrazines, ketones, pyrroles, phenols, hydrocarbons, acids and anhydrides, aldehydes, esters, alcohols, sulfur compounds, and others (Flament and Bessière-Thomas, 2002; Illy, 2005). Nonetheless, the desirable coffee aroma is produced by a delicate balance in the composition of volatiles, and it is believed that only about 5% of these compounds are actually odorous and capable of impacting coffee flavor (Yeretzian et al., 2003). Among these compounds, pyrazines stand out, followed by furans, aldehydes, ketones, phenols, and sulfur compounds, amongst others (Czerny et al., 1999; Maeztu et al., 2001; Sanz et al., 2002; Akiyama et al., 2005).

The utilization of DA technique for identifying key molecules to distinguish coffee samples on features such as roasting degree or geographic provenance allows a substantial reduction of the aforementioned list of volatiles to only 9-18 compounds, depending on the desired type of discrimination. Table 4 presents ten of the molecules that statistical modeling has shown to be influent to discriminate coffee samples. From these, 2-methylbutanal is perhaps the most versatile marker found, as it was considered important in all geographic discrimination tests discussed in this chapter (i.e., “Four Geographic Regions”, “Brazil vs.

Others”, “Brazil vs. America”, “Roasting degree”). This molecule belongs to a group comprising chemical compounds with remarkable high odor activity values (OAV) (De Maria et al., 1999). 2-methylbutanal imparts buttery aromas to coffee samples, which are of positive contribution to their quality, a feature also found in compounds like 3-methylbutanal,

2,3-butanedione, and 2,3-pentanedione. Besides this aldehyde, 2-ethylpyrazine and 2,5-dimethylpyrazine are two worth noting cases of molecules that models consider relevant for both geographic origin and roasting degree assessments. From a sensorial perspective, the two compounds are linked to peanuts/roasted and hazelnut/roasted notes, respectively, which are also of positive contribution to coffee quality.

Table 4. Selected key chemical markers for differentiation of coffee samples regarding origin and roasting degree

Compound Structure Sensorial note Marker scope

1-hydroxy-2-butanone Toasted “Four Geographic Regions”

“Brazil vs. Others”

2-methylbutanal Buttery

“Four Geographic Regions”

“Brazil vs. Others”

“Brazil vs. America”

“Roasting degree”

dihydro-2-methyl-3(2H)-furanone Coffee “Roasting degree”

2,3-butanedione Buttery “Brazil vs. America”

2,3-dimethylpyrazine Hazelnut/

Roasted “Roasting degree”

pyrazine Roasted

“Four Geographic Regions”

“Brazil vs. Others”

“Brazil vs. America”

2-ethylpyrazine Peanuts/

Roasted

“Four Geographic Regions”

“Roasting degree”

2,5-dimethylpyrazine Hazelnut/

Roasted

“Four Geographic Regions”

“Brazil vs. America”

“Roasting degree”

2-furfuryl-5-methylsulfide Sulfuraceous “Four Geographic Regions

furfural Almond/

Bitter “Four Geographic Regions”

As a final remark, it should be emphasized that not all relevant compounds from a sensorial perspective (high OAV) were found statistically relevant to distinguish coffee

samples with different origins. Hence, one may anticipate that sensorial analysis may be inefficient to sort samples although being of more expedite implementation, because the DA approach requires time consuming analytical results. On the other hand, DA revealed a good accuracy and hints to a deep chemical understanding of what clearly differentiates coffee samples regarding their intrinsic origin and roasting features.

C

The quality control of coffee samples is a challenging task, for which multivariate statistics can be a powerful aid. It includes techniques such as Principal Component Analysis (PCA), Partial Least Squares (PLS), and Discriminant Analysis (DA). They can be used to model geographic origin and roasting degree of coffee samples based on their volatiles profiles.

The screening potential of DA was demonstrated through three models proposed to differentiate (i) samples from Brazil from others of the same continent, (ii) samples from Brazil from others of the world, and (iii) samples belonging to one of four geographic regions: Central America, South America, Africa and Asia. For this, a maximum number of 18 compounds was required to clearly distinguish the provenance of the various coffee samples.

With respect to the differentiation of coffee samples submitted to Light, Medium, Dark or French roastings, a DA model comprising two equations and 10 volatile molecules was enough to explain 99.4% of the variance exhibited by the experimental data. In the future, the presented DA tool can be extended with advantage to the eight roasting degrees of the AGTRON Roasting Classification, depending on the availability of a database.

While some chemical markers were specific for only one type of differentiation study addressed, 2-methylbutanal, 2-ethylpyrazine and 2,5-dimethylpyrazine were relevant for both geographic origin and roasting degree assessments. These compounds possess high odor activity values (OAV) and thus a great positive impact on coffee organoleptics.

In view of the successful application of the DA approach to databases of this size and variability, this essay provides compelling arguments for the development of DA-based tools with the purpose of controlling the quality of coffee in terms of their geographic and/or roasting features.

A

The authors thank the Brazilian National Research Council (CNPq) and the Coordination for the Improvement of Higher Level Personnel (CAPES) for financial support. This work was developed within the scope of the project CICECO-Aveiro Institute of Materials, POCI-01-0145-FEDER-007679 (FCT Ref. UID/CTM/50011/2013), financed by national funds through the FCT/MEC and when appropriate co-financed by FEDER under the PT2020 Partnership Agreement.

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Editor: John L. Massey © 2016 Nova Science Publishers, Inc.

Chapter 4