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Effects of bedrock on the chemical and Sr
isotopic compositions of plants
Article in Environmental Earth Sciences · February 2015
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Effects of bedrock on the chemical and Sr
isotopic compositions of plants

Byeong-Yeol Song, Mukesh Kumar
Gautam, Jong-Sik Ryu, Dongho Lee &Kwang-Sik Lee
Environmental Earth Sciences
ISSN 1866-6280
Environ Earth Sci
DOI 10.1007/s12665-015-4087-2

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Author's personal copy
Environ Earth Sci
DOI 10.1007/s12665-015-4087-2

ORIGINAL ARTICLE

Effects of bedrock on the chemical and Sr isotopic compositionsof plants
Byeong-Yeol Song • Mukesh Kumar Gautam •Jong-Sik Ryu • Dongho Lee • Kwang-Sik Lee

Received: 22 June 2014 / Accepted: 21 January 2015
Ó Springer-Verlag Berlin Heidelberg 2015

Abstract This study was conducted to investigate therelationship between the chemical and isotopic compositionsof plants and the lithology of their growth location. This
relationship is one of the principles underlying the geographical discrimination used for agricultural, forestry, andfood products. Locations in South Korea with solely basaltic(Jeju), granitic (Gongju), and carbonate (Yeongwol) rocks
were selected as test sites, and bedrocks, soils, and plants ineach location were sampled. The multi-element compositions and Sr isotope ratios (87Sr/86Sr) were analyzed for all
samples, including various soil fractions (exchangeable,
carbonates, silicates) and plant parts (tree leaves, vegetableleaves, and fruits). Based on a preliminary statistical analysis(ANOVA), Ca, Mg, K, Al, Sr concentrations, and 87Sr/86Sr
ratios were selected as key variables with greater variability
with location. Using these variables, a multivariate statisticalanalysis was conducted, and three soil fractions and plant
Electronic supplementary material The online version of thisarticle (doi:10.1007/s12665-015-4087-2) contains supplementarymaterial, which is available to authorized users.
B.-Y. Song  K.-S. Lee
Graduate School of Analytical Science and Technology,
Chungnam National University, Daejeon 305-764,
Republic of Korea
B.-Y. Song  M. K. Gautam  J.-S. Ryu  D. Lee (&) 
K.-S. Lee (&)
Division of Earth and Environmental Sciences, Korea Basic
Science Institute, Chungbuk 363-883, Republic of Korea
e-mail: dhlee17@kbsi.re.kr
K.-S. Leee-mail: kslee@kbsi.re.kr
B.-Y. SongChemical Analysis Division, National Forensic Service,
Wonju 220-170, Republic of Korea

parts were successfully discriminated according to theirgeographical origin (i.e., bedrock type) with statistical significance. The results indicated that Sr isotope ratios of
bedrocks can be preserved in soils (except in silicate fractions) and plants throughout weathering and plant physiological processes, and are the most critical variable in the
tracing of bedrock characteristics.
Keywords Multi-element composition  Sr isotopes Geographical discrimination  Multivariate statisticalanalysis

Introduction
The geochemical relationship between bedrocks and soils/
plants is one of the principles underlying geographical discrimination studies. Plants take up vital components from soils
that were originally derived from the weathering of basement
rocks. During weathering, minerals in rocks are chemically andmechanically disintegrated, depending on their vulnerability,
and their constituents are released into soils. The chemicalcomposition of soils is primarily affected by the source rocks
and the types of weathering processes. Therefore, the material
cycles through the source rock–soil–plant system are the principle underlying the geographical discrimination used for
agricultural, forestry, and food products, based on multi-element composition (Almeida and Vasconcelos 2003; Suzuki
et al. 2008; Lo Feudo et al. 2010; Furia et al. 2011). For
example, Almeida and Vasconcelos (2003) investigated therelationship between wines and their provenance soils using
multi-element composition, and concluded that soil composition has potential for use as a fingerprint of the origin of wines.
Along with multi-elemental composition, stable isotopes
have been increasingly used in studies of geographical123Author's personal copy
Environ Earth Sci

origin for plants and plant products because stable isotopes(such as carbon, oxygen, hydrogen, nitrogen, and sulfur)
can be used to trace, record, and identify ecologicalparameters (Kelly et al. 2005; West et al. 2006; Rodrigues
et al. 2011; Bong et al. 2012a; Rock 2012). For example,
Rodrigues et al. (2011) used carbon, nitrogen, sulfur, andoxygen isotopes to identify the effects of varying climaticconditions on green coffee beans from the Hawaiian
Islands. They suggested that stable isotope ratios can serve
as a useful fingerprint to discriminate between different
Hawaiian regions. Furthermore, radiogenic isotope ratios
such as 87Sr/86Sr have been widely used in geographicaldiscrimination studies. Radiogenic isotopes have an
advantage over light stable isotopes in that they do notexhibit measurable fractionation during soil formation andplant physiological processes, so their ratios reflect only thegeochemical characteristics of the rocks and soils fromwhich they were derived (Degryse et al. 2010; Lee et al.
2011; Techer et al. 2011; Bong et al. 2012b). For example,
Lee et al. (2011) examined the geographical origin of
ginsengs grown in South Korea and China using 87Sr/86Sr
ratios, and concluded that their 87Sr/86Sr ratios reflect thelithological characteristics of the cultivation areas.
Previous studies have mainly focused on the practicalapplication of geochemical variables to the tracing of geographical origin. However, few detailed analytical studies
have been conducted on the geochemical relationshipbetween bedrock–soil–plant systems, and the effects ofvarious bedrock types and soil fractions. Soils are a complex mixture of components that can have different elemental and isotopic compositions (Opfergelt et al. 2014), so
plants may uptake materials from selective soil fractions
with elemental and isotopic compositions that differ fromthe bulk soils. The current study was motivated by the
deficiency in knowledge about the geochemical relationships between bedrocks/soils and plants. We selected threestudy sites with different rock types (granite, basalt, carbonate) and collected samples of bedrocks, soils, and six
plant species. We conducted multi-element and isotopic
analyses of the samples including three soil fractions(exchangeable, carbonates, silicates) and plant parts (leaves
and fruits). The objectives of this study were to elucidate thegeochemical relationships between bedrocks, soils, andplant parts, and to identify the most suitable elemental andisotopic tracers for geographical discrimination studies.

Materials and methods
SamplesThree locations—Gongju, Yeongwol, and Jeju in South
Korea—were selected for sample collection based on their123

distinct lithology, including Mesozoic granites (Gongju),
Paleozoic carbonates (Yeongwol), and Cenozoic basalts(Jeju; Fig. 1). In Gongju and Yeongwol, fresh rock samples
were collected at the outcrops near the soil and plant
sampling locations. Samples of basaltic rocks in Jeju were
not collected in this study; geochemical data from previous
studies were used instead (Chung and Land 1997; Shinet al. 1998; Choi et al. 2002; Yang et al. 2004; Tatsumi
et al. 2005; Choi et al. 2006; Koh et al. 2008; Brenna et al.
2010, 2012). At each site, upper soils (0 * 5 cm) werecollected from *10 locations distributed over a 1-km2 areawith a total sample quantity *0.5 kg dry soils. The six
plant species, which were growing at all sampling sites,
consisted of three tree species and three vegetable species:
Acacia (Acacia), Zelkova (Zelkova serrata), Cherry (Prunus serrulate var.), Red pepper (Capsicum annuum L.),
Corn/maize (Zea mays L.), and Spring onion (Allium fistulosum L.). For tree species, leaves (N = 27) were collected from the upper (N = 9), middle (N = 9), and lower
parts (N = 9). For vegetable species, leaves (N = 18
samples) and fruits (N = 13) were collected (Table 1).
Diverse sizes of vegetable leaves were collected to avoid
possible size-dependent variability in the elemental andisotopic compositions. All plant samples were collected intriplicate.
Sample preparation and analyses
PlantsTo remove particles/dust from the surfaces of plant samples, samples were immersed in de-ionized water andrepeatedly sonicated. After drying at 60 °C, the samples
were ground using a medical mixer (HM-2500P, Medical
Hyundai Inc., Korea) and kept in a vacuum oven.
Approximately 0.7 g of powdered samples was digested ina Teflon vessel using 10 mL of 70 % HNO3. After digestion, the solution was dried and residues were flushed
several times by a 3:1 mixture of HNO3 and HClO4. This
process was followed by re-dissolution in 3 % HNO3 forthe complete dissolution of samples.
Rocks and soils
Rock and soil samples were crushed by a shatter box
equipped with tungsten carbide grinding jars. The crushed
samples were wet sieved to smaller than 2 mm in diameter and for soil samples, particles larger than 2 mm indiameter were discarded. Crushed samples with diameters
smaller than 2 mm were completely ground to powder.
Approximately 0.1 g of each powdered soil and rocksample was completely digested in a 5:3 mixture of HF
and HNO3. Each sample was then dried, refluxed several

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Fig. 1 Map of locations andlithologies of sampling sites

times by 6 M HCl to remove fluorides, and re-dissolvedin 3 % HNO3.
Sequential leaching of soils
A sequential dissolution method was used to extract
exchangeable, carbonate, and residual (silicate) fractions ofsoils (Rauret 1998). Approximately 1 g of each soil sample
was reacted with 10 mL of 1 M NH4Ac for 6 h to extractthe exchangeable fraction. After the reaction, the mixture
was centrifuged and the supernatant was filtered using a
0.45-lm polypropylene syringe filter. The filtered solution
was dried and re-dissolved in 3 % HNO3 to prepare foranalysis. The residue of the previous step was reacted with

10 mL of 4 M HAc for 6 h to extract the carbonate fraction
in soils, following the same procedure used to extract theexchangeable fractions. Finally, residue (mainly silicates)from the carbonate extraction step was dried at 60 °C andapproximately 0.1 g of the subsample was completely
digested in a 5:3 mixture of HF and HNO3. Samples weredried and refluxed several times in 6 M HCl to remove
fluorides, and then re-dissolved in 3 % HNO3.
Multi-element analysis
Concentrations of major, minor, trace, and rare earth elements were determined using either ICP-OES (Optima
4300DV; PerkinElmer, Waltham, MA, USA) or ICP-MS

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Table 1 Overview of thesampling locations and sample
types

GongjuCoordinates

Yeongwol

Jeju

N127°150 1600

N128°260 400

N126°330 2900

00E33°250 2400

E36°21 49

N number of samples analyzed

00

E37°7 22

Elevation (m)

118

192

71

Mean annual temp (°C)

12.3

10.7

16

Mean annual rainfall (mm)

1,354

1,200

1,872

Bedrock

GraniteLimestone

BasaltMesozoic

Cambrian–Ordovician

Cenozoic–Paleocene

Samples types

N

Rock

1

2

–

Soil

5

11

6

N

N

Acacia leaves

3

3

3

Zelkova leaves

3

3

3

Cherry leaves

3

3

3

Red pepper
Pepper leaves

3
3

3
3

3
3

Corn

–

2

2

Spring onion

3

3

3

(X7, Thermo Scientific, Bremen, Germany) at the Korea
Basic Science Institute (KBSI). Various standard referencematerials (SRMs 1570a and 1573a for plants, SRMs BCR2, G-3, and MAG-1 for soils and rocks) were used to verify
the uncertainty involved in each analysis.
Strontium isotope analysis
Depending on the Sr concentration, approximately
0.2–1 mL of the sample solution was evaporated underlaminar flow conditions and re-dissolved in 8 M HNO3 toconvert the matrix of the sample solution. Then, following
the procedure described by Swoboda et al. (2008), Sr was
separated from matrix elements using Eichrom Sr resin(Eichrom Industries, Darien, IL, USA). The eluted solution
was evaporated and re-dissolved in 3 % HNO3 to a final Srconcentration of *200 ng/g. Sr isotopic ratios were measured using a double-focusing multi-collector inductively
coupled plasma mass spectrometer (MC-ICP-MS) (Neptune, Thermo Scientific, Bremen, Germany) equipped withan CETAC autosampler (ASX-112FR). The analytical
reproducibility of the measured 87Sr/86Sr ratios, based on a
replicate analyses of NIST SRM 987, was0.710247 ± 0.000008 (2r, N = 24), with a negligible
blank of less than 0.1 ng Sr.
Statistical analysis
As a first step, the chemometrics of rocks (25 elements),
soils including different fractions (exchangeable-16

123

00

elements, carbonate-34 elements and silicate-47 elements),
and plants (48 elements) were compiled and processedusing a one-way ANOVA (Welch’s ANOVA, SPSS Inc.,
Chicago, IL, USA). Based on these ANOVA results(p \ 0.05), Ca, Mg, K, Al, Sr, and 87Sr/86Sr ratios wereselected for further analysis due to their significant differences by location. Prior to the ANOVA, normality (Shapiro–Wilk’s test) and homoscedasticity (Leven’s test) werechecked and normalized to log-values whenever p values
were less than 0.05 (Zar 2010). Furthermore, a linear discriminant analysis (LDA) was conducted to test their efficiency in discriminating samples, based on their elemental
and isotopic composition. Overall, the significance of thediscriminant model was tested using Wilks’ k. In all thecases, Wilks’ k was highly significant (p \ 0.0001),
showing the relationship that there was statistically significant separation of the sites based on 87Sr/86Sr isotope
ratios and all the elements selected. The Mahalanobis
distance measure (p \ 0.01) was used to support the resultsof the discriminant analysis. Tolerance values greater than
0.01 and VIF (Variance Inflation Factor) values less than
10 indicated the absence of multi-colinearity between
variables. To determine the efficiency of discriminant
analysis, cross validation procedure was used. Cross-validation through training and validation determines the percentage of observations correctly classified and determines
to which group a particular observation belongs. We used
default cross-validation mode in the XLStat for thiswherein, 75 % observations were used in training samples
and remaining 25 % observation in validation. All analyses

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were performed using the XLStat-ProÒ program (Addinsoft, New York).

strictly follow the pattern observed in bedrocks, Ca and K(and also Mg, Al, Sr) concentrations varied with location,
supporting the utility of these elements for the tracing ofgeographical origin.

Results and discussion
PlantsMulti-element concentrations
Bedrocks
Multi-element concentrations of all sample types arereported in the Appendix. For all locations, geochemical
data reported in other studies were also used to calculate
the average composition (Chung and Land 1997; Shin et al.
1998; Choi et al. 2002; Yang et al. 2004; Tatsumi et al.
2005; Choi et al. 2006; Koh et al. 2008; Brenna et al. 2010,2012). Among the measured elemental concentrations, we
focused on the five elements (Ca, K, Mg, Al, Sr) selectedby the ANOVA, because of their suitability for geographical discrimination. In Fig. 2, concentrations of Ca and K in
all sample types are shown as examples. In bedrock samples, Ca concentration varied with the abundance of Cabearing minerals such as calcium carbonates and Ca-richplagioclases, i.e., the highest was in Yeongwol (carbonates), followed by Jeju (basalt), and the lowest was in
Gongju (granite). As expected from the abundance of
K-feldspars and micas, K concentrations were higher inGongju than in other locations and lower in Yeongwol.
Therefore, both Ca and K concentrations clearly differentiate locations with different bedrocks.
Soils
In bulk soil samples, Ca concentration was highest in
Yeongwol, as with bedrock samples. However, the concentrations in Jeju and Gongju were similar (Fig. 2). The
same pattern was observed for carbonate and silicate
fractions. With regard to exchangeable fractions, Ca concentration was highest in Jeju with a large variability, andlowest in Gongju. Therefore, in soil and its fractions,
Yeongwol can be identified from other locations based on
its higher Ca concentration, except in exchangeable
fractions.
K concentration markedly varied by location in soil andits fractions, although the pattern of variability differedfrom that of bedrock (Fig. 2). In bulk soils and silicate
fractions, K concentrations were lower in Jeju and higher inGongju and Yeongwol. In exchangeable and carbonate
fractions, the variability in K concentration exhibited areversed pattern compared with that of bedrocks, that is, it
was highest in Yeongwol, intermediate in Jeju, and lowest
in Gongju. In summary, although the variability in theelemental concentrations of soil and its fractions may not

The variability in Ca concentrations of tree and vegetableleaves was similar to that observed in the bulk soils, carbonate, and silicate soil fractions, although with less difference in concentration by location (Fig. 2). Differencesin concentration were most remarkable in vegetable fruits,
which had the highest Ca concentrations in Gongju and thelowest in Jeju. K concentrations were higher in Gongju andlower in Jeju, with considerable variability. Differences in
K concentration by location were small or negligible in tree
and vegetable leaves.
The effectiveness of using elemental concentrations totrace plant origins may vary by elements and plant parts, as
discussed previously. In many cases, the elemental concentrations of plants varied sufficiently by location, but thisvariability did not strictly follow the pattern observed in
bedrocks or in specific soil fractions. The complexity inplant elemental concentration may be related to the plant
physiological processes and the compositional differencein various soil fractions. Some previous studies have suggested that plants may uptake essential elements fornutrition from the exchangeable fraction of soils. However,based on our results, it is difficult to relate plant elemental
composition to specific soil fractions. Nevertheless, thefindings suggest that large compositional differences in
bedrocks can be inherited to plant elemental composition invarious and unspecified ways, thereby enabling the identification of geographical origin.
Strontium isotope ratios
Bedrocks
The 87Sr/86Sr ratios were highest in Gongju and lowest in
Jeju (Fig. 3). This finding is consistent with the reported
87
Sr/86Sr ratios for similar rock types collected in Korea:
0.70402–0.70623 for Cenozoic sedimentary and volcanic
rocks (Kim et al. 1999a, 2003); 0.71067–0.75594 forMesozoic granites (Cheong and Chang 1997; Kwon et al.
2006); and 0.70916–0.715573 for Paleozoic carbonate
rocks (Chung and Land 1997; Park and Cheong 1998; Kimet al. 1999b). The radiogenic signature of carbonate rocks
compared with Phanerozoic marine carbonates can be
explained by the dissolution of metamorphosed carbonates
such as marbles (Park and Cheong 1998). The wider variation in the 87Sr/86Sr ratios of bedrocks in Jeju may berelated to the fact that we compiled previously reported

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Fig. 2 Concentrations of Ca
and K in bedrocks, bulk soils,
soil fractions, and plant parts

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effectively traced based on the 87Sr/86Sr ratio alone, giventhat they were cultivated in regions with different Sr isotope ratios.
Geographical discrimination

Fig. 3 87Sr/86Sr ratios of bedrocks, bulk soils, soil fractions, andplant parts

data and did not conduct our own analyses of bedrock
samples.
Soils
The 87Sr/86Sr ratios of bulk soils were similar to those of
bedrocks in Yeongwol, slightly lower in Gongju and higher
in Jeju (Fig. 3). In Jeju, the 87Sr/86Sr ratios of exchangeable
and carbonate fractions were lower than those of the bulk
soils, while in other locations, the ratios of bulk soil,
exchangeable, and carbonate fractions were almost constant. The silicate fractions of soils had exceptionallyhigher ratios in all locations, which was most remarkable in
Yeongwol with its carbonate bedrocks. In terms of87
Sr/86Sr ratios, it is clear that the silicate fractions cannot
accurately represent the bedrock characteristics.
Plants
In Gongju, the 87Sr/86Sr ratios of all plant parts were similar
to those in the bulk soils and soil fractions except for silicates (Fig. 3). In Yeongwol, although the 87Sr/86Sr ratios offruits were slightly higher, the ratios of other plant parts
were almost identical to those of bedrocks, bulk soils, andsoil fractions (except silicates). In Jeju, the 87Sr/86Sr ratiosof plant parts were in the range of the ratios measured forbulk soils, exchangeable soils, and carbonate fractions.
Overall, the distinct 87Sr/86Sr ratio at each sampling
location was maintained in soils and plant parts except in
the silicate fractions. The results indicate a remarkableconsistency of 87Sr/86Sr ratios throughout the bedrock–soil–plant pathway, which substantiates the utility of Sr
isotopes in geographical discrimination studies. No matter
which plant part is used, the geographical origin can be

Discriminant analysis using Ca, Mg, K, Al, Sr concentrations and 87Sr/86Sr ratios as descriptors revealed that all
locations, and the plants growing there, had significantly
different chemical compositions (p \ 0.05, see Fig. 4).
Elemental patterns of plants’ leaves were well distinguished by locality. The probability of successful classification was also very high in all cases: 100 % for plants androcks, 93.3 % for exchangeable and carbonates fractions,73.3 % for silicate fractions. Furthermore, the significant
Mahalanobis distances (p \ 0.01), which were calculatedwith all pairwise distances between sites for all treatments,
differed significantly regardless of which fraction was used
(Fig. 4). For all cases, the first two discriminant functions
differed significantly between the locations (p \ 0.0001;
Table 2). Because canonical correlation measures thestrength of the overall relationships between the linear
canonical discriminant functions and descriptor variables, ahighly significant canonical correlation between the locations and the first/second discriminant functions (p \ 0.05)
indicates that the canonical functions can explain the differentiation of the locations. Use of discriminant analysis
successfully classified plants’ leaves relative to their distributed grown area. This finding indicates that the chemistry of plant leaves reflects their habitat environment, i.e.,
mainly bedrock lithologies.

Conclusions
The significant differences in bedrock composition [granite(Gongju), carbonate (Yeongwol), and basalt (Jeju)]
apparently influenced the composition of the weathering
products (soil and its fractions) and plants cultivated fromthe region. Concentrations of Ca, K, Mg, Al, Sr, and Sr
isotope ratios exhibited the most variability by the locations of different bedrocks. In particular, Sr isotope ratios
of bedrocks were maintained in soils (except in the silicate
fractions) and plants, and therefore can be used as anunequivocal tracer to identify a plant’s geographical origin.
Use of discriminant analysis based on Ca, Mg, K, Al, Sr,and Sr isotopes successfully classified plants according totheir growth locations, based on the bedrocks and soils.
Our results demonstrate that carefully selected geochemical variables can identify plant habitat information with thehelp of statistical analysis techniques. Although here wesuggested Ca, Mg, K, Al, Sr, and Sr isotopes as key variables for geographical discrimination for use in geological123

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Fig. 4 Scatter plots of linear discrimination analysis for rocks, soils including exchangeable, carbonate, and silicate fractions, and various plantparts with the Mahalanobis distance

Table 2 Results of linear discriminant analysis for all the samples
PlantsRock

Exchangeable fraction

Carbonate fraction

Silicate fraction

Three fractions ? rock

F1

F2

F1

F2

F1

F2

F1

F2

F1

F2

F1

F2

Eigenvalue

48.4

5.5

315

71.9

70.6

4.9

70.1

12.3

9.4

1.2

576

132

Discrimination (%)

78

22

81

19

94

7

85

15

88

12

77

18

Cumulative (%)

90

100

81

100

94

100

85

100

88

100

77

95

Canonical correlations 0.99 0.92 1
0.99 0.99
Coefficients of standardized canonical discriminant function

0.91

0.99

0.96

0.95

0.74

1

1

87

7.8

0.0

0.2

0.7

2.2

0.2

1.9

0.9

0.9

0.6

0.0

0.0

Al

-0.7

1.4

-1.7

-0.8

-0.9

0.1

-0.1

-0.2

-1.0

0.2

1.5

-0.3

Ca

2.7

-1.4

0.6

-0.1

2.6

-0.2

6.2

-0.2

-1.1

-0.3

0.1

1.0

K

-1.3

2.6

0.2

1.7

0.2

-0.4

2.2

-1.3

-1.2

0.4

0.6

0.4

Mg

0.4

-3.0

-1.7

0.6

-1.0

-0.8

-4.7

-0.9

1.4

0.2

1.9

-0.2

Sr

-1.1

1.7

-0.4

0.0

-1.1

0.9

-2.6

1.8

1.1

0.2

0.5

0.1

Sr/86Sr

settings, future research may reveal more geochemical
proxies and extend the applicability to more diverse rocktypes.

123

Acknowledgments This study was supported by a national agenda
program (NAP) of the Korea Research Council of Fundamental Science & Technology and by a KBSI grant (C34701).

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