Article pubs.acs.org/JAFC

Isotopic and Elemental Composition of Roasted Coffee as a Guide to Authenticity and Origin James F. Carter,* Hans S. A. Yates, and Ujang Tinggi Queensland Health Forensic and Scientific Services, P.O. Box 594, Archerfield, Queensland 4108, Australia S Supporting Information *

ABSTRACT: This study presents the stable isotopic and elemental compositions of single-origin, roasted coffees available to retail consumers. The δ13C, δ15N, and δ18O compositions were in agreement with those previously reported for green coffee beans. The δ15N composition was seen to be related to organic cultivation, reflected in both δ2H and δ18O compositions. The δ13C composition of extracted caffeine differed little from that of the bulk coffee. Stepwise discriminant analysis with jackknife tests, using isotopic and elemental data, provided up to 77% correct classification of regions of production. Samples from Africa and India were readily classified. The wide range in both isotopic and elemental compositions of samples from other regions, specifically Central/South America, resulted in poor discrimination between or within these regions. Simpler X−Y and geo-spatial plots of the isotopic data provided effective visual means to distinguish between coffees from different regions. KEYWORDS: authentication, caffeine, coffee, country of origin, elemental profile, isoscape, isotope ratio

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INTRODUCTION Coffee is brewed from the roasted beans of the plants Coffea arabica and/or Coffea canephora, commonly known as robusta. Grown mostly in regions between the latitudes of Cancer (23°26′ north of the equator) and Capricorn (23°26′ south of the equator), it is widely consumed across most continents and cultures. In 2012/2013 the International Coffee Organization (ICO) valued the global trade in coffee at U.S. $17.4 billion, providing the primary commodity income for some 50 developing nations in Africa, Asia, and Central/South America1 and having a direct financial impact on 20−30 million families.2 A greater sophistication in the palette of the consumer and a preference for coffees from countries renowned for the high quality and distinctive taste of their produce3 have led to increased consumption and an increase in the global coffee price of 325% between 2001 and 2015.1 At the top end of this market, coffees from Jamaica or Panama can command prices between $128 and 260 per kg, whereas Brazilian coffee can be purchased for as little as $15 per kg.1 Such a price differential may well lead unscrupulous vendors to pass off cheaper coffee as a premium brand. As long ago as 1820 Fredrick Accum4 addressed “The fraud of counterfeiting ground coffee...” and even in that era recorded that “...it is evident that this practice has been carried on for a long time...”. In more recent times, researchers have applied modern analytical techniques to determine the authenticity of coffee, with a special emphasis on the country of origin. Researchers have considered various chemical components of coffee as proxies for both country of origin and cultivar,5−8 and others have applied spectroscopic techniques such as 1H and 13C NMR and infrared spectroscopy to the same end.9−14 Increasingly, research has focused on the stable isotope and elemental composition of coffee as a means to determine the country of origin.15−20 What is apparent from all of these studies is that consideration of multiple components, using Published XXXX by the American Chemical Society

sophisticated data analysis, was necessary to resolve the country of origin of coffee beans. In general, researchers have concluded that, although it was possible to determine the continent of origin (Africa, Asia, Oceania, Central/South America), it was more challenging to determine intracontinental origin. Although most previous studies have examined green coffee beans, the vast majority of coffee sold at retail outlets is roasted and/or ground, and this study set out to examine roasted coffees that were available to consumers. A previous study has noted that whether coffee was green or roasted had little effect on the elemental composition.19 The aim of this paper was two-fold: to present a suite of stable isotope ratio and elemental concentration data for roasted coffees against which suspected substitute samples might be compared and to determine which parameters best discriminated between regions of production. As with many studies of food authenticity it was difficult to obtain truly authentic samples, and in obtaining the samples for this study our strategy was to seek out reputable vendors.

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MATERIALS AND METHODS

Sample Collection and Processing. A total of 54 roasted coffees were analyzed during this study (summarized in Table 1), which were considered to be representative of most coffee-producing regions of the world and of the samples available within Australia at the time of this survey (May 2014). Although Vietnam is the second largest producer of coffee in the world,1 neither this study nor any previous study has obtained single-origin coffee from this country. Forty-three of the samples were purchased as roasted beans and 11 as ground coffee. The majority (38) claimed to be arabica and the remainder (16) blends of arabica and robusta. Two of the samples (samples 41 Received: March 25, 2015 Revised: May 19, 2015 Accepted: May 22, 2015

A

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Table 1. Summary of the Coffees Analyzed in This Survey ID

country

regiona

supplier

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46c 47 48 49 50 51c 52 53 54

Ethiopia Ethiopia Ethiopia Ethiopia Ethiopia Ethiopia Ethiopia Kenya Kenya Kenya Rwanda Uganda Australia Australia Australia Australia PNG Costa Rica Costa Rica El Salvador Guatemala Guatemala Guatemala Honduras Jamaica Mexico Mexico Nicaragua Indonesia Indonesia Indonesia Indonesia Indonesia India India Bolivia Brazil Brazil Brazil Brazil Brazil Colombia Colombia Colombia Colombia Colombia Colombia Ecuador Ecuador Peru Peru Peru Peru Peru

A A A A A A A A A A A A Au Au Au Au Au/PNG CA CA CA CA CA CA CA CA CA CA CA Ind Ind Ind Ind Ind In In SA SA SA SA SA SA SA SA SA SA SA SA SA SA SA SA SA SA SA

Blackstar The Coffee Roaster Di Bella Elixer The Coffee Roaster The Coffee Roaster Coles Expressi Blue Sky The Coffee Roaster The Coffee Roaster The Coffee Roaster Cairns Highland Cairns Highland Cairns Highland The Coffee Roaster The Coffee Roaster The Coffee Roaster Sexie Cleanskin Blackstar The Coffee Roaster Sexie Di Bella The Coffee Roaster The Coffee Roaster Di Bella The Coffee Roaster Expressi Blackstar Cleanskin The Coffee Roaster Coles The Coffee Roaster Sexie Blue Sky Cleanskin Di Bella The Coffee Roaster The Coffee Roaster Coles Expressi Blackstar The Coffee Roaster Sexie Coles Woolworths Select Cleanskin Blue Sky Woolworths Macro Blackstar Di Bella The Coffee Roaster Woolworths Macro

description Yiracheffe Yiracheffe single origin Smado org Yiracheffe Yiracheffe Ethiopian Kenya single origin AA lena

highland pearls black mountain Queensland blue Nats organic Kimel estate Colibri tarrazu Tarrazy La Calandria Anekos Los Volcanoes Huehuetenango single origin blue mountain single origin La Bastilla Sumatra Aceh Aceh Kuda mas Sumatran Monsoon Malabar single origin Pedro redona single origin Toffee Cerrado Sao Marcos Brazilian Colombia Excelso Supremo Bachue Colombia Colombian (decaff) espresso Yambura single origin ground coffee Peruvian (decaf) single origin Peru

beansb

style

arabica arabica arabica arabica arabica arabica arabica arabica arabica arabica arabica blend arabica arabica arabica arabica blend arabica arabica arabica blend blend blend arabica arabica arabica arabica arabica blend blend blend arabica blend arabica arabica arabica blend blend blend blend blend arabica arabica arabica arabica arabica arabica blend blend arabica arabica arabica arabica arabica

beans beans beans beans beans beans ground ground beans beans beans beans beans beans beans beans beans beans beans beans bean beans beans beans beans beans beans beans ground beans beans beans ground beans beans beans beans beans beans beans ground ground bean beans beans ground ground beans beans ground ground beans beans ground

a

Samples were classified by region (A, Africa; AU, Australia; PNG, Papua New Guinea; CA, Central America; Ind, Indonesia; In, India; SA, South America). bSamples were classified according to bean cultivar (Coffea arabica) or blend, a mixture of Cof fea arabica and Caffea robusta. cCoffee sold as decaffeinated. and 51) were decaffeinated. Samples were stored frozen at −20 °C and opened immediately prior to analysis.

Samples of beans were initially ground in a domestic coffee mill to a particle size typical of filter coffee. Previous work within the authors’ B

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Table 2. Results from a Preliminary Study of the Effects of Extracting Ground Coffee Beans with Water or with Hexane Δ 13C δ CVPDB × 10 13

3

aqueous extract

solvent extract

sample

country

whole beans

beans

extract

beans

extract

1 10 27 33 35 50

Ethiopia Kenya Mexico Indonesia India Peru

−26.86 −25.96 −29.30 −27.59 −28.65 −28.80

−0.02 0.17 −0.11 −0.14 −0.23 −0.51

0.60 0.72 1.15 0.16 −0.07 0.75

−0.84 −1.33 −1.07 −0.69 −0.43 −0.45

3.72 4.00 3.74 3.92 4.35 4.26

−0.14 −0.13

0.55 0.66

average median

−0.80 −0.77 Δ15N aqueous extract

δ15NAIR × 103

4.00 3.96

sample

country

whole beans

beans

extract

1 10 27 33 35 50

Ethiopia Kenya Mexico Indonesia India Peru

4.39 3.99 2.51 2.85 2.52 4.89

−0.17 −0.02 −0.22 −0.05 −0.13 −0.12

3.04 3.10 2.76 2.04 2.30 2.05

−0.12 −0.13

1.29 0.81

average median

laboratory has demonstrated that this process did not introduce significant metal contamination, and all subsequent stages of handling were performed using metal-free materials. Approximately 10−15 g of coarsely ground coffee was taken for a pretreatment study and for caffeine extraction. The remainder was ground with a zirconium oxide ball mill (Fritsch, Idar-Ocerstein, Germany) to produce a fine, visibly homogeneous powder. This sample was divided between two sterile, acid-washed containers for stable isotope and elemental analysis. Sample Pretreatment. As an initial study, six samples (samples 1, 10, 27, 33, 35, and 50), from diverse geographical origins, were extracted with water and with hexane to determine whether these pretreatments could increase the discrimination of isotopic analysis by removing some complexity from the analyte. Approximately 5 g of coarsely ground samples was extracted with approximately 150 mL of high-purity water or hexane using conventional Soxhlet apparatus for 5 h. Aqueous extracts were heated in wide beakers at 102 °C until visibly dry. Hexane extracts were dried by rotary evaporation. The dried extracts were analyzed by FT-infrared spectroscopy to determine the chemical composition, and both the extracted samples and the dried extracts were analyzed to determine the carbon and nitrogen isotopic composition. Extraction of the samples with water or hexane were considered to represent the extremes of what might be removed or recovered from the coffee beans, that is, by polar, aqueous, or nonpolar, organic, solvents. The aqueous extracts of the coffee were found to have infrared spectra consistent with mixtures of sugars and proteins, whereas the solvent extracts were found to have infrared spectra consistent with vegetable oil. The results of the isotopic analysis of the extracts and residues are summarized in Table 2. Extraction with water was found to have a negligible effect on either the δ13C or δ15N composition of the residual coffee, whereas the aqueous extracts were somewhat enriched in both isotopes. In contrast, extraction with organic solvent produced a significant depletion in the δ13C composition of the residual coffee with a concomitant large enrichment in the extract. The solvent extracts did not contain sufficient nitrogen to permit δ15N determination, and the extracted coffee was not significantly changed in nitrogen isotopic composition (data not shown).

When considering the patterns and relationships within these data, it was concluded that the pretreatments did not enhance the discriminatory power of isotopic analysis (based on δ13C and δ15N measurements), and all subsequent analyses were performed on untreated samples. Stable Isotope Analysis. All isotope ratio measurements were performed using a Thermo Scientific (Bremen, Germany) Delta VPlus isotope ratio mass spectrometer (IRMS) coupled to a ConFlo IV interface for working gas introduction and sample dilution. Carbon and nitrogen isotopic compositions were determined using a Thermo Scientific Flash 2000 HT elemental analyzer (EA) configured for flash combustion at 950 °C with a helium flow of 100 mL/min. The reactor comprised a single quartz tube containing chrome oxide, reduced copper, and silver/cobaltous oxide. A chemical trap containing magnesium perchlorate was inserted between the reactor and the IRMS. Each sample was analyzed in triplicate with median standard deviations of 0.09 and 0.13‰ for carbon and nitrogen measurements, respectively. Hydrogen and oxygen isotopic compositions were determined using a Thermo Scientific Flash 2000 HT EA configured for thermal conversion (TC/EA) operating at 1400 °C with a helium flow of 100 mL/min introduced via a bottom-feed adaptor. Samples were measured into silver capsules (4 × 3.2 mm, IVA Analysentechnik, Meerbursch, Germany) and equilibrated with purified Brisbane tap water (with a well-characterized isotopic composition) for 3 days. Samples were then dried over phosphorus pentoxide for 3 days, under vacuum. Encapsulated samples were introduced into the reactor via a Zero blank autosampler (Costech Analytical Technologies Inc., Valencia, CA, USA). Each sample was analyzed in triplicate with median standard deviations of 1.3 and 0.10‰ for hydrogen and oxygen measurements, respectively. Data were normalized to the international isotope scales (VPDBLSVEC or VSMOW-SLAP) by two-point calibration; a list of the reference materials and quality control (QC) materials used is given in the Supporting Information (supplement 1). The δ values of the nonexchangeable hydrogen content were estimated from the measured values of the sample and the equilibration water by mass balance: C

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None of the measured parameters showed significant differences between coffees purchased ground or as beans or between arabica and blends of arabica and robusta. Statistical Analysis. Data were analyzed and plotted using the R 3.0.1 software environment for statistical computing and graphics.25 Two-tailed t tests were used to compare coffees purchased ground versus beans and arabica versus blends of arabica and robusta. Least absolute deviation (LAD) regression was applied to the data to determine the equation of robust fit and the correlation, R2 (observed vs predicted). Principal component analysis (PCA) was performed on the elemental data to determine which parameters accounted for the largest differences between samples. This was performed using the L1norm to increase robustness to outliers compared to traditional PCA.26 Discriminant analysis (DA) was applied to both elemental and isotopic data to determine to what extent these parameters distinguished between the different regions or countries of origin. Stepwise DA, using the Mahalanobis distance, was used to find the optimum set of discriminant functions (DFs), and once these parameters were established, the classifications were calculated using jackknife tests. The results of conventional DA represent the recognition ability based on the samples used to generate the model, whereas the jackknife approach assesses the external validity and is considered a good approach when, as in this case, sample sizes are small. In these tests, one case is eliminated from the original data set and the DFs are computed using the remaining observations. This procedure is repeated with each individual observation in turn omitted, and the values are averaged to provide a jackknifed estimate of the DFs.

R nex = R meas − (R water × α × fex )/(1 − fex ) Rnex is the nonexchangable H isotope ratio, Rmeas is the measured H isotope ratio, and Rwater is the equilibration water H isotope ratio. The fraction of exchangeable H atoms ( fex) was taken as 0.25,22 and the fractionation factor (α) between H atoms in the equilibration water and samples was taken as 1. This estimate of nonexchangeable hydrogen was considered appropriate for a comparative study. Caffeine Extraction. Caffeine was isolated from an aqueous extract of coffee by supported liquid−liquid extraction using a method previously described to isolate active pharmaceutical ingredients.21 This method was reported to have no significant effect on isotopic composition. Coarsely ground coffee was extracted with high-purity water as described above. When cooled, 0.6 mL of the extracts was loaded onto ChemElute 1 mL unbuffered liquid extraction cartridges (Agilent Australia, Victoria, Australia) and allowed to permeate for 15 min. The cartridges were then eluted with 3.5 mL of high-purity chloroform, yielding 1.5 mL of extract directly into a 2 mL autosampler vial. The method was shown to recover >98% of the caffeine present in the sample. Caffeine Analysis. Extracted caffeine was characterized using a 7819A (Agilent Australia) gas chromatograph (GC) coupled to a Thermo Scientific ISQ MS. The injector was maintained at 280 °C with a constant helium flow of 1 mL/min and a split ratio of 10:1. Caffeine was separated using a DB-5 column (30 m × 0.25 mm × 0.25 μm) (Agilent Australia) with the following temperature program: 120 °C (2 min) raised at 15 °C/min to 280 °C (2 min). The carbon isotopic composition of extracted caffeine was determined using the analytical conditions above, with the GC column coupled to a Thermo Scientific GC-IsoLink interface with the combustion reactor operated at 1030 °C. Data were normalized to the VPDB-LSVEC scale using a certified sample of natural caffeine (IAEA-600; δ13CVPDB −27.77 ‰) and a sample of synthetic caffeine (δ13CVPDB −38.40 ‰) calibrated inhouse using the methodology described above. Elemental Analysis. The methods of analysis have been previously described.23,24 In summary, approximately 0.3 g samples were accurately weighed into PTFE vessels and digested with highpurity nitric acid (69% Seastar Chemicals, Canada) using a Mars Xpress microwave digestion system (CEM Corp., Matthews, NC, USA). Digested samples were analyzed using a Vista Pro ICP-OES (Varian Australia) and a 7700 ICP-MS (Agilent Australia) with autosampler and Integrated Sample Introduction System (ISIS). Appropriate reference materials were used for quality control (Supporting Information supplement 1). Data Pretreatment. All elemental concentration data that were below the limit of detection (LoD) for more than 50% of the samples were removed from the overall data. Any of the remaining data that were below the LoD were replaced with the numerical value of the LoD for a given element and are presented as such. Data are reported for 13 elements (Ca, K, P, Mg, Al, Ti, Mn, Fe, Ni, Cu, Zn, Sr, Ba), together with the δ2H, δ13C, δ15N, and δ18O compositions (Supporting Information supplement 2). To constrain the data further, the median and interquartile range (IRQ) were calculated for each measured parameter, and data were considered to be outliers if an individual value fell beyond

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RESULTS AND DISCUSSION Samples were grouped according to their region of origin: Africa (A), Australia (Au), Central America (CA), Indonesia (Ind), India (In), Papua New Guinea (PNG), and South America (SA), as shown in Table 1. When discriminant analysis was performed, the data for PNG were grouped with the close geographical neighbor Australia, so as not to include a group with a single member. Because of wide geographical differences, Indonesia and India were treated as separate entities rather than as a single “Asia” grouping. Correlation. A Pearson correlation matrix was calculated for all measured parameters in which few significant relationships were observed. Strong correlations were observed between δ2H and δ18O compositions and between δ18O composition and the δ13C compositions of both the coffee and the extracted caffeine (see below). The only strong correlation observed within the elemental concentration data was between the concentrations of strontium and barium, both group II metals. Principal Component and Discriminant Analysis. PCA was applied to the elemental concentration data to determine which parameters accounted for the largest differences between samples. The loading factors for PC1 were dominated by potassium concentration, the loading factors for PC2 by the concentrations of calcium and magnesium, and the loading factors of PC3 by phosphorus. PCA did not provide clear discrimination between the regions of origin. Stepwise DA reduced the elemental concentration data to 5 variables (Ca, Ti, Fe, Ni, Zn) from the original 13, the major contributions to the DFs being the concentrations of calcium and titanium. Stepwise DA reduced the isotopic data to three variables, (δ2H, δ13C, δ18O) from the original five, δ13C and δ18O being the major contributors to the DFs. This reduced data set can be explained by the strong covariance between δ2H and δ18O and between δ13Ccoffee and δ13Ccaffeine. Combining the elemental and isotopic data, stepwise DA identified eight variables, consistent with the results above (δ2H, δ13C, δ18O,

median − (1.5 × IQR) or median + (1.5 × IQR) Outliers were substituted with the median value for the measured parameter and are presented in the Supporting Information (supplement 2, highlighted in green). In total approximately 6% of the elemental concentration data were substituted by this process, although notably, 22% of the aluminum concentration data and 33% of the nickel concentration data were considered to be outliers. These elevated metal concentrations did not appear to be associated with particular countries of origin, suppliers, or style of coffee (beans vs ground) and may have resulted from contact with metal surfaces during storage. None of the isotopic data were classified as outliers. D

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Figure 1. Plots of (a) carbon versus nitrogen isotopic composition and (b) hydrogen versus oxygen isotopic composition of roasted coffee beans: Africa (diamonds, red), Central America (squares, blue), South America (triangles, green); other countries are identified by letters (see Table 1). Dashed line shows the LAD regression (Y = 5.93X − 229; R2 = 0.83).

Ethiopia (sample 4) and the most depleted samples originated from Australia (samples 13−15) or El Salvador (sample 20). The variation in δ13C composition of roasted coffees measured in this study (from −30.0 to −25.2‰) was in agreement with those determined in earlier studies of green coffee beans: from −29.9 to −24.6 to‰17 and from −28.1 to −23.8‰.18 The variation in δ15N composition spanned a slightly wider range (from −0.3 to +6.4‰) than reported in these earlier studies: from +1.5 to +3.317 and from +1.2 to +5.6.18 These findings suggest that, akin to elemental composition, there was little change in the isotopic composition of green and roasted coffee with respect to δ13C and δ15N. The large variation in the data presented in Figure 1 suggested that many factors influence the isotopic composition such as water and nutrient availability, light intensity, and temperature.27 Despite the large number of environmental factors that may control these data, some trends were apparent. In general, the samples most enriched in both 13C and 15N came from Africa and the most depleted samples came from Central/South America. The one exception to this trend was sample 7, which was purchased as ground coffee from a large supermarket chain and claimed to be a product of Ethiopia. Coffees from Indonesia formed a cluster with little variation in carbon and nitrogen composition. Coffees from Australia, Papua New Guinea, and India had similar δ13C compositions, typical of C3 plants (approximately −28.7‰), but had δ15N compositions that spanned the entire range observed in this study.

Ca, P, Ti, Ni, Zn) with the major contributions to the DFs being the isotopic data. Stepwise DA was also applied to distinguish between countries within Central/Douth America. Both the elemental concentrations (Ca, K, Ti, Fe, Ni, Cu, Zn, Ba) and isotopic compositions (δ2H, δ13C, δ15N) identified as major contributors to the DFs were different from those that contributed to global variations. When elemental concentration and isotopic data were combined, stepwise DA removed only the concentrations of strontium and barium from the data, confirming that climatic and geological variations are more subtle on a regional scale than globally. The same approach was applied to distinguish between countries within the African continent. Stepwise DA identified variables within the isotopic data (δ2H/δ18O) and within the elemental concentration data (Ca, K, Mg, P, Al, Mn, Fe), which were quite different from those selected for global data or for the Central/South America region. Again, this finding suggests that regional variations are driven by different factors from global variations and from variations within other regions. Carbon and Nitrogen Isotopes. Figure 1a shows an X−Y scatter plot of δ15N versus δ13C compositions of the coffee samples. Overall, the δ13C data spanned a wide range (from −30.0 to −25.2‰); the most enriched samples originated from Ethiopia or Kenya (samples 4 and 10) and the most depleted sample (sample 36) from Bolivia. The δ15N compositions spanned a similar range (from −0.3 to +6.4‰); the most enriched samples originated in Australia (sample 16) or E

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Figure 2. Isoscape of the oxygen isotopic composition of coffee samples.

Figure 3. Carbon isotopic composition of extracted caffeine versus carbon isotopic composition of roasted beans: Africa (diamonds, red), Central America (squares, blue), South America (triangles, green); other countries are identified by letters (see Table 1). Dashed line shows the LAD regression (Y = 0.81X − 5.3; R2 = 0.81).

In common with earlier studies of green coffee beans18 it was apparent that a combination of carbon and nitrogen isotopic composition allowed some degree of discrimination between Africa, Asia, and Central/South America regions but not intracontinent. Three Australian coffees had δ15N compositions close to that of atmospheric nitrogen (0‰), whereas the fourth (sample 16) was the most enriched sample in this study (+6.4‰), claiming to be organic, as did sample 4. Nitrogen stable isotopic composition has been used to distinguish organic and conventional cultivation methods for a range of fruits28−31 on the basis that inorganic fertilizers have δ15N compositions close to atmospheric nitrogen (from −6 to +6‰), whereas manure and other organic fertilizers can be greatly enriched (from +1 to +37‰). From the limited data available in this study it appeared that δ15N composition may be extended to the authentication of organic coffee beans. This principle would also support the fact that African countries, in which natural fertilizers predominate, are typically enriched in 15N with respect to countries that favor the use of inorganic fertilizers. Hydrogen and Oxygen Isotopes. Figure 1b shows an X− Y scatter plot of the δ2H versus δ18O compositions of the coffee samples with a relationship defined by

The hydrogen isotopic composition spanned a range from −113 to −8‰, and the oxygen isotopic composition spanned a range from +20.6 to +36.7‰. The most enriched sample (sample 11) originated from Rwanda and the most depleted samples (samples 46 and 50) from Colombia and Peru. The range of δ18O compositions (from +20.6 to +32.4‰) was somewhat smaller than previously reported for green coffee beans (from +18.7 to +34.8‰).17 Although the δ18O composition of plant material is attributed mainly to precipitation at different latitudes and altitudes,32 the data in this study showed no correlation with latitude. This may reflect little variation, as a majority of coffee is grown between latitudes 23°26′ north and south of the equator. The relationship between δ2H and δ18O was characterized by a slope that was typical neither of meteoric water (typical slope 8)33 or of evaporation-enriched water in fruits (typical slope 4).34 It is likely that the observed relationship contained contributions from both of these processes and/or water loss through roasting. During roasting, green coffee beans are progressively heated to 230 °C, losing approximately 13% of their weight,35 the majority of which is likely to be water. The pattern of data presented in Figure 1b for δ2H/δ18O broadly reflected those observed from δ13C/δ15N, samples from Africa being the most isotopically enriched and samples from Central/South America being the most depleted. For δ2H/δ18O

δ 2 H = δ18O × 5.9 − 229 F

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data there was a clear delineation of African countries, including sample 7, which had ambiguous δ13C/δ15N characteristics. Samples from low-coffee-producing nations (Australia, India, and Indonesia) formed discrete groupings within the data, with the exception of Australian sample 16, which was an organic product. On the basis of Figure 1b it is likely that δ2H/δ18O composition was also characteristic of organic production methods, organic and African samples being the most enriched. Figure 2 presents a simple geo-spatial map (isoscape) showing the distribution of δ18O of the coffee samples from this study. The isotopic data were ranked into 20 groups, assigned a color according to the palette shown in Figure 3, and plotted on a global map using the longitude and latitude of each sample (Supporting Information supplement 2). Very similar patterns were observed for plots of δ2H and δ13C as might be expected given the strong correlations between these variables. Although the data shown in Figure 2 were relatively sparse, there was a marked similarity to isoscapes predicting plant δ18O composition derived from global precipitation.36 By plotting data in this way it becomes possible to answer the question, “is the isotopic composition consistent with the claimed geographical origin?”, removing the need for comparative samples. Caffeine. Figure 3 shows an X−Y scatter plot of the δ13C composition of the extracted caffeine versus that of the whole coffee with a relationship defined by

Table 3. Results of Discriminant Analysis Applied to Regions and Countries of Origin Showing the Percentage Correct Classification Obtained by Jackknifed and Conventional (Non-jackknifed) Methods percentage correct classification elemental

isotopic

combined

A AU CA Ind In SA

region/country

83 60 43 40 50 68

92 80 27 80 50 65

100 60 64 80 100 71

total, jackknifed total, conventional

63 74

65 73

77 88

0 20 50 0 50 0 100 0 0 0 0 40

0 40 0 50 0 0 33 0 0 0 0 50

0 0 0 50 0 0 0 100 0 0 0 25

33 97

21 57

11 100

Kenya Ethiopia Rwanda Uganda

0 0 100 100

33 57 0 0

0 0 100 100

total, jackknifed total, conventional

17 100

42 83

17 100

Bolivia Brazil Colombia Costa Rica Ecuador El Salvador Guatemala Honduras Jamaica Mexico Nicaragua Peru

δ13Ccaffeine = δ13Ccoffee × 0.8 − 5.3

The isotopic compositions of the extracted caffeine spanned a slightly smaller range (from −29.6 to −25.7‰) than previously reported (from −29.9 to −25.1‰),20 and there was a strong correlation between δ13Ccaffeine and δ13Ccoffee (R2 = 0.81). The small difference between δ13Ccaffeine and δ13Ccoffee (median difference = −0.22‰; range from −0.80 to +0.77‰) was in agreement with a study of the biosynthesis of purine alkaloids (specifically caffeine), which predicted a small depletion in the carbon isotopic composition of caffeine with respect to cellulose.37 The general trends observed in Figure 1 were also apparent in Figure 3, African samples being the most isotopically enriched, Central/South American samples being the most depleted, and low-coffee-producing regions (Australia, India, and Indonesia) forming discrete groupings within the data. The strong correlation between δ13Ccaffeine and δ13Ccoffee may provide a limited means to determine the authenticity of caffeinated beverages by reference to Figure 3. However, such an approach may be limited by the technical complexity of GCIRMS measurements for elements other than carbon. Country of Origin. Table 3 summarizes the results of DA applied to the analytical data obtained in this study. A reduced data set of elemental concentrations (Ca, Ti, Fe, Ni, Zn) provided a reasonable basis to discriminate between regions of production (63% overall correct). A reduced data set of isotopic composition (δ2H, δ13C, δ18O) provided somewhat better discrimination for some regions but misclassified samples from Central/South America and India, leading to similar overall success (65% correct). Combining elemental and isotopic data improved the discrimination between Central and South America and the overall correct classification (77% correct). Although the degree of correct classification was lower than reported by previous studies,16−19 we find that the current results represent a realistic estimate of what is possible with a limited number of samples.

total, jackknifed total, conventional

DA was also applied independently to data from Central/ South America and from Africa (Table 3). These tests failed to correctly classify coffee from any of the countries, with the possible exceptions of Guatemala/Honduras and Rwanda/ Uganda. As only a single sample from each country was analyzed, it is not possible to conclude if this finding reflects that these countries were characterized by a unique elemental composition. DA results can be explained with reference to Figures 1 and 2; countries from Africa were clearly delineated by enriched isotopic compositions, whereas data for Central/South America and low-coffee-producing countries (Australia, India, and Indonesia) were encompassed in a broad band of values. Isotopic composition appeared to be driven by factors such as precipitation, more than national or continental boundaries. Consequently, in South America there was a distinction between the 18O compositions of coffees from the east and west coasts, which was more pronounced than any difference between Central/South America; that is, intraregion differences were similar to, or greater than, inter-region differences. For this reason, assigning countries to broad, man-made groupings such as “Asia” as a basis for DA may not be appropriate. G

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Although DA was successfully applied to assess numerically the region of origin, it was apparent (Table 3) that the level of discrimination provided by DA was greatly overestimated when compared to the results obtained using a jackknifed approach. We recommend caution in the haphazard use of DA; specifically, the number of observations must be significantly greater than the number of independent variables (>5:1), and the number of observations in the smallest group must be greater than the number of independent variables. In the absence of such large sample sets, simpler X−Y and geo-spatial plots (Figures 1−2) provide a simple and robust approach to narrow the range of possible origins. The isotopic composition of a coffee can be compared against the data presented in Figure 1 to determine if the combinations of both δ13C/δ15N and δ2H/δ18O are consistent with a single geographical origin in Africa, Australia, India, Indonesia, or Papua New Guinea. The elemental composition of a sample can then act as a further point of comparison, and an analyst may have reasonable confidence in reporting such a result. The use of kernel density estimation (kde) has been applied to stable isotope data to calculate a quantitative weight of evidence that a sample belongs to one group rather than another.38 This approach may be extended to food forensic applications but is beyond the scope of the current work. The wide spread and overlap of isotopic data for Central and South American coffees may preclude such comparisons but, as noted above, the presentation of isotopic data in the form of a geospatial map or isoscape (Figure 2) provides a means by which to consider whether the isotopic composition of a sample is consistent with the proposed region of origin. Increasing the value of such comparisons will require large data sets and/or models linking our data to existing databases such as global precipitation.36

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ASSOCIATED CONTENT

S Supporting Information *

Measured and assigned values of quality control materials used for isotopic or elemental analysis and the δ values assigned to reference materials for the normalization and quality control of IRMS data (Supplement 1); sample details and analytical data for all of the coffees analyzed in this study (Supplement 2). The Supporting Information is available free of charge on the ACS Publications website at DOI: 10.1021/acs.jafc.5b01526.

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Article

AUTHOR INFORMATION

Corresponding Author

*(J.F.C.) Phone: +61 (07) 3274 9228. Fax: +61 (07) 3274 9196. E-mail: [email protected]. Notes

The authors declare no competing financial interest.

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ACKNOWLEDGMENTS We are grateful to the editorial staff for their patience and to the reviewers for identifying a number of flaws in the original drafts of the manuscript. We acknowledge the support of Queensland Health Forensic and Scientific Services in completing this work. We are also grateful to and apologize to the specialty coffee roasters of Brisbane who enthusiastically sold us their finest single-origin coffee without realizing that it would never be drunk. We extend special thanks to Prof. Brian Fry of Griffith University for many mutually valuable discussions on the appropriate use of discriminant analysis. H

DOI: 10.1021/acs.jafc.5b01526 J. Agric. Food Chem. XXXX, XXX, XXX−XXX

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DOI: 10.1021/acs.jafc.5b01526 J. Agric. Food Chem. XXXX, XXX, XXX−XXX