The objective of this work was to compare the effectiveness of three chemical families, namely, chlorogenic acids, fatty acids, and elements, for the discrimination of Arabica varieties (traditional versus modern introgressed lines) and potential terroirs within a given coffee-growing area. The experimental design included three Colombian locations in full combination with five (one traditional and four introgressed) Arabica varieties and two field replications. Chlorogenic acids, fatty acids, and elements were analyzed in coffee bean samples by HPLC, GC, and ICP-AES, respectively. Principal component analysis and discriminant analysis were carried out to compare the three methods. Although elements provided an excellent classification of the three locations studied, this chemical class was useless for Arabica variety discrimination. Chlorogenic acids gave satisfactory results, but fatty acids clearly offered the best results for the determination of both varieties and environments, with very high percentages of correct classification (79 and 90%, respectively).

In a previous study, the effectiveness of chlorogenic acids, fatty acids (FA), and elements was compared for the discrimination of Arabica varieties and growing terroirs. Since FA provided the best results, the aim of the present work was to validate their discrimination ability using an extended experimental design, including twice the number of location x variety combinations and 2 years of study. It also aimed at understanding how the environment influences FA composition through correlation analysis using different climatic parameters. Percentages of correct classification of known samples remained very high, independent of the classification criterion. However, cross-validation tests across years indicated that prediction of unknown locations was less efficient than that of unknown genotypes. Environmental temperature during the development of coffee beans had a dramatic influence on their FA composition. Analysis of climate patterns over years enabled us to understand the efficient location discrimination within a single year but only moderate efficiency across years.

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Previous study on food plants has shown that near infrared (NIR) spectral methods seem effective for authenticating coffee varieties. We confirm that result, but also show that inter-variety differences are not stable from one harvest to the next. We put forward the hypothesis that the spectral signature is affected by environmental factors. The purpose of this study was to find a way of reducing this environmental variance to increase the method's reliability and to enable practical application in breeding. Spectral collections were obtained from ground green coffee samples from multilocation trials. Two harvests of bean samples from 11 homozygous introgressed lines, and the cv 'Caturra' as the control, supplied from three different sites, were compared. For each site, squared Euclidean distances among the 12 varieties were estimated from the NIR spectra. Matrix correlation coefficients were assessed by the Mantel test. We obtained very good stability (high correlations) for intervariety differences across the sites when using the two harvests data. If only the most heritable zones of the spectrum were used, there was a marked improvement in the efficiency of the method. This improvement was achieved by treating the spectrum as succession of phenotypic variables, each resulting from an environmental and genetic effect. Heritabilities were calculated with confidence intervals. A near infrared spectroscopy signature, acquired over a set of harvests, can therefore effectively characterize a coffee variety. We indicated how this typical signature can be used in breeding to assist in selection.

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