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To produce specific desirable coffee blends, Coffea arabica and C. canephora are mixed each other, in some cases to suit consumer preference, but in others to reduce production costs. In this scenario, the aim of this work was to evaluate standard candidate reference materials (RMc) for analysis of different blends of roasted and ground coffee. For this purpose, we analyzed different percentages of C. arabica and C. canephora (100:0; 50:50; 25:75; and 0:100, respectively). These RMc samples were developed in a previous study with green coffee beans submitted to medium roasting. In this work, coffee species differentiation (C. arabica and C. canephora) was analyzed by real-time PCR, using specific primers previously developed, called ARA primers. The RMc material with 100% C. canephora did not present amplification, in contrast with the samples containing C. arabica, which all presented amplification. These results indicate the specificity of ARA primers for C. arabica and that the detection system assay can be used as a promising molecular tool to identify and quantify percentages of C. arabica in different coffee blends., (Copyright © 2018 Elsevier Ltd. All rights reserved.)

A deeper knowledge of the chemical composition of coffee silverskin (CS) is needed due to the growing interest in its use as a food additive or an ingredient of dietary supplements. Accordingly, the aim of this paper was to investigate the metabolic profile of aqueous extracts of two varieties of CS, Coffee arabica (CS-A), Coffee canephora var. robusta (CS-R) and of a blend of the two (CS-b) and to compare it to the profile of Coffee arabica green coffee (GC). Chlorogenic acids, caffeine, furokauranes, and atractyligenins, phytotoxins not previously detected in CS, were either identified or tentatively assigned. An unknown compound, presumably a carboxyatractyligenin glycoside was detected only in GC. Caffeine and chlorogenic acids were quantified while the content of furokauranes and atractyligens was estimated. GC and CS were also characterized in terms of total polyphenols and antioxidant capacity. Differences in the metabolites distribution, polyphenols and antioxidant capacity in GC and CS were detailed., (Copyright © 2017 Elsevier Ltd. All rights reserved.)

The occurrence of pipecolic acid betaine (homostachydrine) and its biosynthetic precursor N-methylpipecolic acid was detected for the first time in green coffee beans of Robusta and Arabica species. The analyses were conducted by HPLC-ESI tandem mass spectrometry and the metabolites identified by product ion spectra and comparison with authentic standards. N-methylpipecolic acid was found at similar levels in green coffee beans of Robusta and Arabica, whereas a noticeable difference of homostachydrine content was observed between the two green coffee bean species. Interestingly, homostachydrine content was found to be unaffected by coffee bean roasting treatment because of a noticeable heat stability, a feature that makes this compound a candidate marker to determine the content of Robusta and Arabica species in roasted coffee blends. To this end, a number of certified pure Arabica and Robusta green beans were analyzed for their homostachydrine content. Results showed that homostachydrine content was 1.5±0.5mg/kg in Arabica beans and 31.0±10.0mg/kg in Robusta beans. Finally, to further support the suitability of homostachydrine as quality marker of roasted blends of Arabica and Robusta coffee beans, commercial samples of roasted ground coffee blends were analyzed and the correspondence between the derived percentages of Arabica and Robusta beans with those declared on packages by manufacturers was verified., (Copyright © 2016 Elsevier Ltd. All rights reserved.)

NMR spectroscopy was used to verify the presence of Arabica and Robusta species in coffee. Lipophilic extracts of authentic roasted and green coffees showed the presence of established markers for Robusta (16-O-methylcafestol (16-OMC)) and for Arabica (kahweol). The integration of the 16-OMC signal (δ 3.165 ppm) was used to estimate the amount of Robusta in coffee blends with an approximate limit of detection of 1-3%. The method was successfully applied for the analysis of 77 commercial coffee samples (coffee pods, coffee capsules, and coffee beans). Furthermore, principal component analysis (PCA) was applied to the spectra of lipophilic and aqueous extracts of 20 monovarietal authentic samples. Clusters of the two species were observed. NMR spectroscopy can be used as a rapid prescreening tool to discriminate Arabica and Robusta coffee species before the confirmation applying the official method., (Copyright © 2015 Elsevier Ltd. All rights reserved.)

Background: The aim of this study is to investigate the feasibility of a 'holistic' approach, using near infrared (NIR) spectroscopy and electronic devices (electronic nose and electronic tongue), as instrumental tools for the classification of different coffee varieties. Analyses were performed on green coffee, on ground roasted coffee and on coffee beverage. Principal component analysis was applied on spectral and sensory data to uncover correlations between samples and variables. After variable selection, linear discriminant analysis was used to classify the samples on the basis of the three coffee classes: Robusta, natural Arabica and washed Arabica., Results: Linear discriminant analysis demonstrates the practicability of this approach: the external test set validation performed with NIR data showed 100% of correctly classified samples. Moreover, a satisfying percentage of correct classification in cross-validation was obtained for the electronic devices: the average values of correctly classified samples were 81.83% and 78.76% for electronic nose and electronic tongue, respectively., Conclusion: NIR spectroscopy was shown to be a very reliable and useful tool to classify coffee samples in a fast, clean and inexpensive way compared to classical analysis, while the electronic devices could assume the role of investigating techniques to depict the aroma and taste of coffee samples., (© 2014 Society of Chemical Industry.)

This study reports direct quantification of arabica in roasted and ground coffee blends of Coffea arabica and Coffea canephora var. robusta. (1)H-NMR analysis of water extracts of coffee blends were combined with multivariate statistical analysis to obtain an OPLS model with high predictive capability. This approach allowed to evaluate the composition of coffee blends of unknown arabica and robusta content, on the basis of multiple chemical components. Differences in geographical origin of the analyzed samples did not affected the compositional determination of coffee blends. This approach represents a valid tool in authentication procedures of arabica and robusta blends of roasted and ground coffee., (Copyright © 2012 Elsevier B.V. All rights reserved.)

(13)C NMR-based metabolomics was demonstrated as a useful tool for distinguishing the species and origins of green coffee bean samples of arabica and robusta from six different geographic regions. By the application of information on (13)C signal assignment, significantly different levels of 14 metabolites of green coffee beans were identified in the classifications, including sucrose, caffeine, chlorogenic acids, choline, amino acids, organic acids, and trigonelline, as captured by multivariate analytical models. These studies demonstrate that the species and geographical origin can be quickly discriminated by evaluating the major metabolites of green coffee beans quantitatively using (13)C NMR-based metabolite profiling.

Green coffee bean isotopes have been used to trace the effects of different climatic and geological characteristics associated with the Hawaii islands. Isotope ratio mass spectrometry (IRMS) and inductively coupled plasma mass spectrometry ((MC)-ICP-SFMS and ICP-QMS) were applied to determine the isotopic composition of carbon (δ13C), nitrogen (δ15N), sulfur (δ34S), and oxygen (δ18O), the isotope abundance of strontium (87Sr/86Sr), and the concentrations of 30 different elements in 47 green coffees. The coffees were produced in five Hawaii regions: Hawaii, Kauai, Maui, Molokai, and Oahu. Results indicate that coffee plant seed isotopes reflect interactions between the coffee plant and the local environment. Accordingly, the obtained analytical fingerprinting could be used to discriminate between the different Hawaii regions studied.

Among the 100 different known Coffea species, Coffea arabica L. (Arabica) and Coffea canephora Pierre (Robusta) are the only two of commercial interest. They differ in a range of agronomic, genetic, and chemical properties. Due to the significant price difference between Arabica and Robusta, there is an economic incentive to illicitly replace Arabica with Robusta. Therefore, it is crucial to have accurate methods to determine the Robusta-to-Arabica-ratio in blends. This paper presents the proof of principle of a new and fast approach to determine the Robusta fraction in a blend based on Raman spectroscopy. The oils of two references (a pure Robusta and pure Arabica coffee) and six blends thereof consisting of different Robusta and Arabica fractions were extracted using a Soxhlet system. The solutes were analyzed by means of Raman spectroscopy without further workup. Using the intensity ratio between two Raman peaks, one characteristic for kahweol and one characteristic for fatty acids, allowed determinination of the Robusta content in a given mixture. The intensity ratio is linearly dependent on the Robusta content of the compound. Above a Robusta content of 75 wt %, kahweol was not detectable. The Raman data are in agreement with results obtained from the very time-consuming multistep DIN 10777 procedures based on HPLC.

In this feasibility study, Fourier transform infrared (FTIR) spectroscopy and chemometric analysis were adopted to discriminate coffees from different geographical origins and of different roasting degrees. Roasted coffee grounds were extracted using two methods: (1) solvent alone (dichloromethane, ethyl acetate, hexane, acetone, ethanol, or acetic acid) and (2) coextraction using a mixture of equal volume of the solvent and water. Experiment results showed that the coextraction method resulted in cleaner extract and provided a greater amount of spectral information, which was important for sample discrimination. Principal component analysis of infrared spectra of ethyl acetate extracts for dark and medium roast coffees showed separated clusters according to their geographical origins and roast degrees. Classification models based on soft independent modeling of class analogy analysis were used to classify different coffee samples. Coffees from four different countries, which were roasted to dark, were 100% correctly classified when ethyl acetate was used as a solvent. The FTIR-chemometric technique developed here may serve as a rapid tool for discriminating geographical origin of roasted coffees. Future studies involving green coffee beans and the use of larger sample size are needed to further validate the robustness of this technique.

This paper presents an approach that may be applied as an accurate and rapid tool for classifying coffee beans on the basis of the specific kahweol content. Using Fourier-transform Raman spectroscopy with 1064 nm excitation it is possible to monitor the characteristic Raman bands of kahweol in green coffee beans without chemical and physical processing of the beans. The procedure was optimized on the basis of 83 and 125 measurements of whole and ground beans, respectively, using coffee samples of two different species, Coffea arabica L. and Coffea canephora L. (var. Robusta), and different origins (Asia, Africa, and South America). The relative contribution of the kahweol in individual beans can be determined quantitatively by means of a component analysis of the spectra, yielding a spectral kahweol index (σka) that is proportional to the relative content of kahweol in a coffee bean. The reproducibility of the spectroscopic measurement and analysis was found to be 3.5%. Individual beans of the same type and origin reveal a scattering of the σka values. Nevertheless, an unambiguous distinction between Arabica and Robusta samples is possible on the basis of single-bean measurements as the σka values are greater than and less than 10 for Arabica and Robusta coffees, respectively. Measurements of whole and ground beans afforded very similar results, despite the heterogeneous distribution of kahweol within a bean. Unlike conventional analytical techniques, the single-bean sensitivity of the present approach may also allow for a rapid detection of unwanted admixtures of low-value Robusta coffee to high-quality and more expensive Arabica coffee.

Coffee samples were analyzed by GC/MS in order to determine the most important peaks for the discrimination of the varieties Arabica and Robusta. The resulting peak tables from chromatographic analysis were aligned and pretreated before being submitted to multivariate analysis. A rapid and easy-to-perform peak alignment procedure, which does not require advanced programming skills to use, was compared with the tedious manual alignment procedure. The influence of three types of data pretreatment, normalization, logarithmic and square root transformations and their combinations, on the variables selected as most important by the regression coefficients of partial least squares-discriminant analysis (PLS-DA), are shown. Test samples different from those used in the calibration and comparison with the substances already known as being responsible for Arabica and Robusta coffees discrimination were used to determine the best pretreatments for both datasets. The data pretreatment consisting of square root transformation followed by normalization (RN) was chosen as being the most appropriate. The results obtained showed that the much quicker automated aligned method could be used as a substitute for the manually aligned method, allowing all the peaks in the chromatogram to be used for multivariate analysis., (Copyright © 2010 Elsevier B.V. All rights reserved.)

Green coffee beans of the two main commercial coffee varieties, Coffea arabica (Arabica) and Coffea canephora (Robusta), from the major growing regions of America, Africa, Asia, and Oceania were studied. The contents of chlorogenic acids, cinnamoyl amides, cinnamoyl glycosides, free phenolic acids, and methylxanthines of green coffee beans were analyzed by liquid chromatography coupled with UV spectrophotometry to determine their botanical and geographical origins. The analysis of caffeic acid, 3-feruloylquinic acid, 5-feruloylquinic acid, 4-feruloylquinic acid, 3,4-dicaffeoylquinic acid, 3-caffeoyl-5-feruloylquinic acid, 3-caffeoyl-4-feruloylquinic acid, 3-p-coumaroyl-4-caffeoylquinic acid, 3-caffeoyl-4-dimethoxycinnamoylquinic acid, 3-caffeoyl-5-dimethoxycinnamoylquinic acid, p-coumaroyl-N-tryptophan, feruloyl-N-tryptophan, caffeoyl-N-tryptophan, and caffeine enabled the unequivocal botanical characterization of green coffee beans. Moreover, some free phenolic acids and cinnamate conjugates of green coffee beans showed great potential as means for the geographical characterization of coffee. Thus, p-coumaroyl-N-tyrosine, caffeoyl-N-phenylalanine, caffeoyl-N-tyrosine, 3-dimethoxycinnamoyl-5-feruloylquinic acid, and dimethoxycinnamic acid were found to be characteristic markers for Ugandan Robusta green coffee beans. Multivariate data analysis of the phenolic and methylxanthine profiles provided preliminary results that allowed showing their potential for the determination of the geographical origin of green coffees. Linear discriminant analysis (LDA) and partial least-squares discriminant analysis (PLS-DA) provided classification models that correctly identified all authentic Robusta green coffee beans from Cameroon and Vietnam and 94% of those from Indonesia. Moreover, PLS-DA afforded independent models for Robusta samples from these three countries with sensitivities and specificities of classifications close to 100% and for Arabica samples from America and Africa with sensitivities of 86 and 70% and specificities to the other class of 90 and 97%, respectively.

The chemotaxonomic relationships between Coffea (subgenus Coffea) species have been poorly studied to date and the compounds tested so far - chlorogenic acids, diterpenoids and purine alkaloids - did not enable the establishment of phylogenetic relationships analogous to those revealed by chloroplast and nuclear DNA studies. In the present study, the relationships between African Coffea species were assessed on the basis of their seed lipid composition. Fatty acids and sterols were determined in 59 genotypes belonging to 17 distinct Coffea species/origins. Principal Component Analysis of fatty acid and sterol data enabled easy identification of the few species for which one or several compounds could serve as a quantitative signature. Hierarchical Clustering classified the Coffea species in seven groups with both fatty acids and sterols. However, while groupings based on seed fatty acid composition showed remarkable ecological and geographical coherence, no phylogeographic explanation was found for the clusters retrieved from sterol data. When compared with previous phylogenetic studies, the groups deduced from seed fatty acid composition were remarkably congruent with the clades inferred from nuclear and plastid DNA sequences.

The present study is focused on the volatile fraction of roasted hazelnut and coffee samples, differing in botanical origins, morphological characteristics, and roasting treatments, selected as challenging matrices. Volatile components, sampled by headspace solid phase microextraction (HS-SPME), were analyzed by GC x GC-qMS, and separation results were adopted to classify, correlate, and/or compare samples and evaluate processing effects. The high-complexity sample profiles were interpreted through different methods: a group-type characterization, a direct fingerprint comparison, and a template matching to extract useful and consistent information, and advantages and limits of each specific approach were critically evaluated. The group-type analysis, focused on several known botanical and technological markers, enabled sample comparison and characterization based on their quali-quantitative distribution; it is highly reliable, because of the authentic standard confirmation, and extends the comparative procedure to trace and minor components. Fingerprint approaches (i.e., direct fingerprint comparison and template matching), on the other hand, extended sample comparisons and correlations to the whole volatiles offering an increased discrimination potential and improved sensitivity due to the wider analyte pattern considered. This study demonstrates the ability of comprehensive GC to further explore the complexity of roasted samples and emphasizes the advantages of, and the need for, a comprehensive and multidisciplinary approach to interpret the increased level of information provided by GC x GC separation in its full complexity.

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).

An EST database has been generated for coffee based on sequences from approximately 47,000 cDNA clones derived from five different stages/tissues, with a special focus on developing seeds. When computationally assembled, these sequences correspond to 13,175 unigenes, which were analyzed with respect to functional annotation, expression profile and evolution. Compared with Arabidopsis, the coffee unigenes encode a higher proportion of proteins related to protein modification/turnover and metabolism-an observation that may explain the high diversity of metabolites found in coffee and related species. Several gene families were found to be either expanded or unique to coffee when compared with Arabidopsis. A high proportion of these families encode proteins assigned to functions related to disease resistance. Such families may have expanded and evolved rapidly under the intense pathogen pressure experienced by a tropical, perennial species like coffee. Finally, the coffee gene repertoire was compared with that of Arabidopsis and Solanaceous species (e.g. tomato). Unlike Arabidopsis, tomato has a nearly perfect gene-for-gene match with coffee. These results are consistent with the facts that coffee and tomato have a similar genome size, chromosome karyotype (tomato, n=12; coffee n=11) and chromosome architecture. Moreover, both belong to the Asterid I clade of dicot plant families. Thus, the biology of coffee (family Rubiacaeae) and tomato (family Solanaceae) may be united into one common network of shared discoveries, resources and information.

The genetic determinism of seed desiccation sensitivity was studied using a cross between two coffee species exhibiting a large difference for this trait, Coffea pseudozanguebariae (tolerant) and C. liberica (sensitive). Throughout the whole study, seed desiccation tolerance was quantified both in terms of water content and water activity. Whatever the parameter used, the level of seed desiccation tolerance in F1 hybrids corresponded to that of the mid-parent, thus indicating an additive inheritance of seed desiccation tolerance at the F1 level. A broad variation was observed among hybrids backcrossed to C. liberica (BCs) for seed desiccation tolerance, independent of the parameter used to quantify it. This variation was continuous and BCs showed transgression in the direction of the most desiccation sensitive parent, indicating (i) that desiccation tolerance is a polygenic trait in coffee species, and (ii) that C. pseudozanguebariae does not present the most favourable alleles for all the genes involved in seed desiccation tolerance. No significant difference was observed between the two reciprocal backcrosses, F1xC. liberica and C. libericaxF1, for the level of desiccation tolerance of their seeds, showing the absence of a maternal effect on this trait. There was no significant effect of the number of seeds harvested from each BC on the level of desiccation tolerance of its seeds. Moreover, there was no significant correlation within BCs between seed size, seed viability, and water content before desiccation and desiccation tolerance.