Research ArticleResearch onDifferentialMetabolites inDistinction ofRice (Oryzasativa L.) Origin Based on GC-MS

Yuchao Feng,1 TianXin Fu,1 Liyuan Zhang ,1,2 Changyuan Wang ,1,2

and Dongjie Zhang 1,2

1College of Food Science, Heilongjiang Bayi Agricultural University, Xinfeng Lu 5, Daqing 163319, China2Key Laboratory of Agro-Products Processing and Quality Safety of Heilongjiang Province, Daqing 163319, China

Correspondence should be addressed to Changyuan Wang; byndwcy@163.com and Dongjie Zhang; byndzdj@126.com

Received 14 October 2018; Accepted 18 December 2018; Published 15 January 2019

Academic Editor: Victor David

Copyright © 2019 Yuchao Feng et al. /is is an open access article distributed under the Creative Commons Attribution License,which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

/e analytical method for the metabolomics of the 60 rice seeds from two main rice origins in Heilongjiang Province wasdeveloped based on gas chromatography coupled with mass spectrum. /e specific differential metabolites between two riceorigins were identified, and the distinguish of the two main origins was illustrated by using the R software platform with XCMSsoftware package for gas chromatography coupled with mass spectrum data processing, combined with multivariate statisticalanalysis software. /e result indicated that the 173 peaks were detected, and 54 of which were structurally identified, coveringamino acids, aliphatic acid, sugar, polyols, and so on. By comparing the data of Wuchang and Jiansanjiang origins, it was foundthat there were 9 special metabolites in Wuchang origin and 8 special metabolites in Jiansanjiang origin. /e 10 differentialmetabolites with significant changes (P< 0.05, VIP≥ 1) were filtrated. It is indicated that the differential metabolites of rice carryinformation of their origin and there are the differences in the metabolites of rice in two main origins. /e proposed method isexpected to be useful for the metabolomic researches of rice.

1. Introduction

Metabonomics is the science of taking low-molecular weightmetabolites in biological samples (such as organic acids, fattyacids, amino acids, and sugar) as the research object,through high-throughput detection and data processing,information integration, and biomarker identification [1].Since the concept of metabolomics was put forward byOliver [2] in 1997, metabolomics has been widely applied invarious fields, becoming a powerful means to explore theinner mechanism of matter [3]. As people emphasis on foodsafety, identification or trace of origin of agriculturalproducts becomes the focus of research in recent years, andmineral elements fingerprints analysis [4] and isotope fin-gerprint analysis [5] are a commonly used means.

And metabolomics, from the point of view of biology,the overall qualitative and quantitative study on all theendogenous metabolites in plant, and explain the metabolitechanges from the angle of systems biology and biological lifeactivity phenomenon [6], will become an effective analytical

platform for the identification of agricultural products.Nicholson et al. had studied the metabolomics of Arabi-dopsis thaliana in different origins and found that the dif-ferences of the growth environment could make differencesin the amino acids and sugars of Arabidopsis thaliana [7].Giansante et al. [8] analyzed composition and content offatty acid of olive oil from four different origins in an Italianby using gas chromatography. /e results showed that thecontent of palmitic acid and linoleic acid in the olive oilexisted significant differences in different origins. With highresolution, sensitivity, and reproducibility, gaschromatography-mass spectrometry (GC-MS) has becomeone of the main analytical platforms in metabolomic re-search [9].

Rice (Oryza sativa L.) is an important food crop, rich innutrients and essential trace elements, suitable for humanneeds [10] and has become a hot topic in plant metabolomicsresearch in recent years [11–16].

/e environment of origin has an important influence onthe growth of rice, so variety and content of the metabolites

HindawiJournal of ChemistryVolume 2019, Article ID 1614504, 7 pageshttps://doi.org/10.1155/2019/1614504

in rice may have the information of origin. /e metabolitesof rice in different origins may exist obvious differences.

In this work, rice seeds from two main rice origins,Wuchang farm (WC) and Jiansanjiang farm (JSJ), in Hei-longjiang Province were comparatively analyzed based on themetabolomics by GC-MS. /e results provide a theoreticalbasis for the origin identification and distinguish of rice.

2. Materials and Methods

2.1. Plant Material. Rice plants, Oryza sativa L., were col-lected from the two geographical indication rice reserves,which are located in Jiansanjiang farm (JSJ) and Wuchang(WC) in Heilongjiang Province. /e main varieties wererandomly collected with the checkerboard sampling methodaccording to the representative sampling principle [17] inthe scope of protection. In each sampling point, rice panicles(Japonica rice, 1-2 kg) were collected according to differentdirections. 30 samples were selected from each origin.

2.2. Chemicals and Materials. Methanol and pyridine(≥99.0% purity) (chromatographic grade) were purchasedfrom Aladdin Reagent Co., Ltd. (Shanghai, China). 2-Chlorophenylalanine (98.5% purity), methoxyamine hydro-chloride (98.5% purity), and N,O-bis(trimethylsilyl)trifluoroacetamide (99%BSTFA+1%TMCS) were purchasedfrom Macklin Reagent Co., Ltd. (Shanghai, China). HPLC-grade water was obtained from a Milli-Q water purificationsystem (Millipore Corp., USA) and used to prepare all aqueoussolutions. All other reagents of analytical grade were pur-chased from Beijing Chemical Factory (Beijing, China).

2.3. Apparatus. /e 7890A/5975C GC-MS (Agilent J&WScientific, USA) equipment was used. Chromatographicseparation of metobolites was performed on an HP-5ms(30m× 0.25mm× 0.25 μm) (Agilent J&W Scientific). Ter-movap Sample Concentrator (Automatic Science In-strument Co., Ltd., China), DHG-9123A drying oven ofelectric heating (Jinghong Laboratory Equipment Co., Ltd.,Shanghai, China), FC2K Rice huller (Dazhu Production Co.,Ltd., Japan), Incubator (Senxin Instrument Co., Ltd.,Shanghai, China), and TGL-16B High speed centrifuge(Anting Instrument Co., Ltd., Shanghai, China) were used inthe experimental procedure.

2.4. SamplePreparation. /e sample processing method andchromatographic method refer to the literatures [18, 19]with slight modifications.

Under liquid nitrogen, rice seed was pulverized to obtainthe powdered samples. 50mg of powdered samples, 800 µLof methanol, and 10 µL of internal standard (2-chlorphe-nylalanine) were mixed by vortex for 30 s. Subsequently, themixture solution was centrifuged at 12,000 rpm for 15.0minat 4°C. After centrifugation, 200 µL supernatant was trans-ferred to a GC bottle (1.5mL automatic sample bottle), andthen the bottle was dried with nitrogen blowing. /e driedresidue was completely dissolved for 90min at 37°C (in 30 µL

of 20mg·ml−1 methoxyamine hydrochloride in pyridine)followed adding 30 µL BSTFA to derive for 60min at 70°C.All samples were analyzed within 24 h after derivatizationtreatment.

2.5. GC-MS Analysis. 1 µL of sample volume was injectedwith autosampler. Gas chromatography was performed on a30m HP-5ms column with 0.25mm inner diameter and0.25mm film thickness (Agilent J&W Scientific). Injectiontemperature was 280°C, the interface was set to 250°C, andion source was adjusted to 230°C and quadrupole to 150°C.Helium (>99.999% purity) was used as the carrier gas set at aconstant flow rate of 2mL·min−1./e temperature was 2minisothermal heating at 80°C, followed by a 10°C·min−1 oventemperature ramp to 320°C and a final 6min heating at320°C. /e system was then temperature equilibrated for6min at 80°C prior to injection of the next sample. Fullscanning mode was used, and scanning range was 50–550(m/z).

2.6. Data Analysis. GC-MS data analysis was performed atSuzhou Bionovogene (Suzhou, China). /e original data ofGC-MS were pretreated with XCMS software packages inthe R software platform. /en, the edited data matrix wasimported into SIMCA-P software (Umetrics AB, Umea,Sweden) for principal component analysis (PCA) and partialleast squares-discriminant analysis (PLS-DA) and othermultivariate statistical analysis./e differences of the samplemetabolome between the groups were analyzed through theanalysis of PCA and PLS-DA score map, and the differencemetabolites were screened according to the difference be-tween the value of group contribution (VIP) and the sig-nificance (P< 0.05). Compared with the standard spectrumlibrary of National Institute of Standards and Technology(NIST) and Wiley Registry metabolomic database, mostmetabolites were analyzed. /e paraffin retention index ofmetabolites is further qualitative identified based on theretention index provided by the Golm Metabolome Data-base (GMD). At the same time, most substances were furtherconfirmed by standard products.

3. Results and Discussion

3.1. 6e Result Analysis of GC-MS

3.1.1. GC-MS Total Ion Chromatogram. In all samples, 173peaks were detected, and 54 metabolites (Table 1), including11 kinds of sugars, 9 kinds of fatty acids, 12 kinds of polyols,14 kinds of other derivatives, 14 kinds of organic acid, 2kinds of amino acids, 1 kind of phosphoric acid, and 1 kindof nucleotide, were identified by analysis of GC-MS raw data.

/e comparison and analysis of the total ion flowchromatogram of two groups of samples (Figure 1) showedthat the total ion chromatogram of rice in different origins issimilar but slightly different. As can be seen from Figure 1,the baseline of the peak diagram is stable and the instrumentis in good stability.

2 Journal of Chemistry

3.1.2. 6e Differential Metabolites of Rice in Two Origins./e metabolites common in 30 rice samples were found withthe statistical analysis. A total of 26 metabolites were found inthe WC origin, and 25 were found in the JSJ origin. Removethe same metabolites, and ultimately, the nine kinds ofcharacteristic metabolites, including pentanoic acid, pyranglucose, stearic acid, eicosanoic acid, docosanoic acid, 1-monooctadecyl glycerol, trehalose, β-tocopherol, and β-si-tosterol, respectively, were found inWC origin./e 8 kinds ofcharacteristic metabolites, including benzoic acid, fumaricacid, xylose, xylitol, glucose, inositol, sorbitol, and raffinose,respectively, were found (Table 1).

3.2. 6e Differences Analysis of Rice Metabonomics inTwo Origins

3.2.1. PCA Analysis. PCA [20] can degrade the data,eliminate overlapping information, and explain most in-formation with a small number of factors, so as to distin-guish similar variables and find differences. A PCA analysis(SIMCA-P) was performed for processing the data of ricesamples from two groups to research the effect of differentgrowth environment on rice metabolites. /is analysis hasthree main components. In the PCA part of this paper, thecumulative R2X� 0.664 and Q2 � 0.572. /e values of R2Xand Q2 are both greater than 0.5, and the difference betweenthe two is less than 0.2. /e metrics of the two indicatorsgiven in the comprehensive literature [21] indicate that thePCA model has good fit and predictability, and there is nooverfitting. It is indicated that the fitting accuracy of themodel is better. As can be seen from Figure 2, except for the 5abnormal samples, the remaining 55 rice samples are in theconfidence interval. /e data of the abnormal sample rep-resent that the sample data are quite different from the othersample data of the same group, so the display in the figureshows that it is far away from other samples in the same

group. /ere are two reasons for the anomaly. One is theerror in the sample pretreatment process, and the other iscaused by the large individual difference between the sampleitself and other samples. According to the PCA score map,the samples in the JSJ origin are distributed on the left side ofthe confidence interval, and the samples in the WC originare mostly distributed on the right side of the confidenceinterval. Explain that there are differences between the twoorigin samples. /ere is overlap in the sample group of theJiansanjiang origin, indicating that the similarity betweenthe samples is large, and there is also a large distance betweenthe sample points. It shows that the similarity betweensamples is small and the difference is large. Comparison ofsamples from both regions and comparisons betweensamples from the same place indicate differences betweensamples.

For intergroup samples, the samples from the JSJ originare distributed on the left side of the confidence interval, whilethe samples from theWC origin are mainly distributed on theright of the confidence interval. It is showed that there weresignificant differences between the two groups of samples, butthere was still overlap between the samples of similar groups./rough unsupervised principal component analysis, ricesamples from different origins could not be distinguished.

3.2.2. PLS-DA Analysis. At the same time as the reducingdimension, PLS-DA (Figure 3) combines with regressionmodel and makes a discriminant analysis of regression resultswith a certain discriminant threshold, which is conducive tomore efficient discovery of intergroup differences and dif-ferential compounds./is analysis has twomain components,R2X� 0.715, R2Y� 0.87, and Q2 � 0.629, and the values of thetwo principal components are similar. When PLS-DA is usedfor data correlation or discriminant model analysis, the re-placement test mode can be used (Figure 4). As can be seenfrom Figure 4, the abscissa indicates the displacement re-tention of the permutation test (the ratio that coincides withthe order of the originalmodelY variable, and the point wherethe permutation retention is equal to 1 is the R2Y and Q2

values of the original model). /e ordinate indicates the valueof R2Y or Q2. /e green dot indicates the R2Y value obtainedby the displacement test. /e blue square point indicates theQ2 value obtained by the displacement test. /e two brokenlines represent the regression lines ofR2Y andQ2, respectively./e original model R2Y is 0.87. Between 0.5 and 1, closer to 1,indicating that the established model is more in line with thereal situation of the sample data. /e original model Q2 is0.629, which is greater than 0.5, indicating that if a newsample is added to the model, an approximate distributionwill be obtained. In general, the original model can betterexplain the difference between the two sets of samples./eQ2

value of the random test for the displacement test is smallerthan the Q2 value of the original model. /e regression line ofQ2 and vertical axis intercept is less than zero. At the sametime, as the retention decreases, the proportion of thesubstituted Y variable increases, and the Q2 of the stochasticmodel gradually decreases. It shows that the original modelhas good robustness and there is no overfitting phenomenon.

Table 1: Special metabolites of rice in two origins.

rt m/z NameJSJ origin6.8 105.08 Benzoic acid15.17 245.04 Fumaric acid15.26 160.09 Xylose15.89 307.06 Xylitol18.51 118.10 Glucose20.07 129.13 Myo-inositol25.62 319.15 Sorbitol33.69 193.00 RaffinoseWC origin12.72 60.02 Pentanoic acid19.58 118.10 Glucopyranose22.83 143.12 Octadecanoic acid23.51 138.15 Eicosanoic acid25.93 151.09 Docosanoic acid26.45 201.04 1-Monohexadecanoylglycerol27.93 361.13 Trehalose30.18 209.00 β-Tocopherol33.49 107.12 β-Sitosterolrt: retention time, min.

Journal of Chemistry 3

In view of the above, it shows that the PLS-DAmodel hasbetter predictability and there is no overfitting. Comparedwith the PCA score chart (Figure 2), Q2 is increased, whichindicates that the concentrated repeatability of test is well,and the accuracy of the model is very high.

Figure 3 shows that samples of two groups completelyseparate, no overlapping samples. For intergroup samples, thesamples from the JSJ origin are distributed on the left side ofthe confidence interval, while the samples from theWCoriginaremainly distributed on the upper of the confidence interval./ere are two abnormal samples that are not within theconfidence interval. It is proved that different origins (growthenvironment) have a great influence on rice metabolites.

3.2.3. Exploration and Identification of Differential Metab-olites between the Two Groups. In this experiment, the

variable importance in the projection (VIP, the thresholdvalue> 1) of the PLS-DA model coupled with the P value ofStudent’s t-test (t-test, the threshold value≤ 0.05) were usedfor looking for the metabolites of differential expression./erice samples of two origins were analyzed, and the tendifferential metabolites with significant changes werescreened, covering 4 kinds of fatty acids, 3 kind of otherderivatives, a kind of polyols, a kind of sugars, and a kind ofnucleotides, the differences metabolites contain both pri-mary metabolites and also contains the secondary metab-olites (Table 2). Among the two rice origins, eight differentialmetabolites were higher in rice in the WC origin than in theJSJ origin and were upregulated and increased by 1.51–4.24times. /e content of glycerol and α-D-Methylfructofur-anoside was lower than in rice in the WC origin than in theJSJ origin and were downregulated and decreased by 0.52and 0.06 times, respectively.

t (1)

JSJWC

–10–15 –5 0 5 10 15

8

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1

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–1

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–4

Figure 3: PLS-DA score plot of samples in two origins.

100908070605040Re

lativ

e int

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)

302010

3 4 5 6 7 8 9 10 12 14 16Retention time (min)

JSJ17, TIC: 7.29e + 07WC23, TIC: 4.48e + 07

18 20 22 24 26 28 30 32 34 36

Figure 1: GC-MS total ion chromatogram.

8

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0t (2)

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Figure 2: PCA score plot of samples in two origins.

4 Journal of Chemistry

3.2.4. Hierarchical Cluster Analysis. /e data set is scaled bythe pheatmap package in R software (v3.3.2), and the bi-directional clustering analysis of samples and metaboliteswas conducted. Figure 5 is a hierarchical clustering diagramof relative quantitative values of metabolites in this exper-iment. As can be seen from Figure 5, the heat map is dividedinto two parts: red and green, indicating that the content ofmetabolites is very different, and the difference betweenthem is obvious. At the top of the graph is the clustering ofsamples in two origins, and it can be found that the clus-tering effect is very good, the samples on the left are frommainly JSJ origin, and the samples on the right are from theWC origin. It can be seen that, through ten differentialmetabolites of the selection, the samples in two origins canbe distinguished and the results are good.

3.2.5. 6e Pathway Analysis of Differential Metabolites./e related metabolic pathway analysis was carried out onthe differential metabolites of two rice origins by using theenrichment analysis of MetaboAnalyst and metabolicpathway retrieval of KEGG [22]. Five metabolic pathwayswere found, and the specific information is shown in Table 3.

/e 10 kinds of differential metabolites in two originswere analyzed through retrieval of metabolic pathways, andfive differential metabolites, including glycerol, indole,thymidine, myristic acid, and 9-(Z)-octadecanoic acid, werefound in target metabolic pathways. /e metabolic path-ways, including the biosynthesis of fatty acids, glycerolmetabolism, the biosynthesis of phenylalanine, tyrosine, andtryptophan, galactose metabolism, and pyrimidine meta-bolism, were found and matched. It is indicated that thedifference of origins has an obvious effect on the variousterminal metabolic pathways of rice.

4. Conclusions

In this experiment, the metabonomics of rice seeds in twomain rice origins (JSJ and WC) in Heilongjiang Provincewere researched based on the GC-MS, 173 peaks were de-tected, and 54 metabolites were identified. Compared withthe two origins, 9 unique metabolites were found in WCorigin, 8 of which were unique metabolites in JSJ origin, andthe 10 differential metabolites of distribution between JSJand WC origins were selected. It is proved that the me-tabolites of rice in a different origin carry the information of

Table 2: /e difference metabolites of rice in two origins.

Name rt (min) m/z VIP P value log2fc_WC/JSJ1 Glycerol 8.83 205.07 1.069 0.016954881 −0.93092 Indole 9.28 117.10 1.130 0.000526404 +0.65073 2,4,6-Tri-tert-butylbenzenethiol 13.38 117.10 1.050 5.53286E− 08 +2.08434 /ymidine 14.88 81.07 1.073 4.31061E− 08 +1.60665 1,6-Anhydro-beta-D-glucose 15.82 204.05 1.046 5.18568E− 07 +1.14126 alpha-D-Methylfructofuranoside 16.46 346.05 1.832 0.00037704 −4.10317 Tetradecanoic acid 16.64 228.13 1.012 1.3111E− 08 +1.65798 9,12-Octadecadienoic acid 21.10 153.14 1.292 0.004032978 +0.59879 9,12-(Z,Z)-Octadecadienoic acid 22.48 81.09 1.007 0.003338612 +1.176010 9-(Z)-Octadecenoic acid 22.54 112.10 1.063 1.59641E− 07 +1.5769rt: retention time; VIP: the contribution rate of different substances to the PLS-DA model; P value: a significance value for t-test; log2fc_WC/JSJ: log2 of theratio of the mean value of the WC origin to the construction of the JSJ origin; +: rising; −: falling.

0.8

R2 = (0.0, 0.379), Q2 = (0.0, –0.23)

0.6

0.4

0.2

0

–0.2

–0.4

–0.6–0.2 0.2

100 permutations 4 components0

R2

Q2

0.4 0.6 0.8 1

Figure 4: /e PLS-DA replacement test chart.

Journal of Chemistry 5

their origin, and the difference of metabolites is feasible forthe distinguishing of rice origins. /e purposed method isfeasible for the separation and identification of metabolitesin rice seeds. Compared with the literatures [19, 23], theorigin has an effect on rice metabolites, and the metaboliteswill be different in different origins. /e method can be usedfor the isolation and identification of metabolites in riceseeds. /e analysis of metabolic pathways shows that thedifference of origins has an obvious effect on the variousterminal metabolic pathways of rice./is research provides areference for plant metabolomics. So it seems possible toextend this method to the separation and identification ofthe metabolites in other similar samples by varying theexperimental conditions.

Data Availability

/e data used to support the findings of this study areavailable from the corresponding author upon request.

Consent

Informed consent was obtained from all individual partic-ipants included in the study.

Conflicts of Interest

/e authors declare that they have no conflicts of interest.

Authors’ Contributions

ChangyuanWang andDongjie Zhang conceived and designedthe study. Yuchao Feng and Tianxin Fu performed acquisitionof data. Liyuan Zhang andChangyuanWang analyzed the data.

Changyuan Wang and Yuchao Feng drafted the manuscript.Dongjie Zhang revised the manuscript.

Acknowledgments

/is study was funded by the Heilongjiang Province Post-doctoral Science Foundation (Grant no. LBH-Z15217),Science and Technology Research Project of HeilongjiangAgricultural Reclamation Administration (Grant no.HNK135-05-01), Special Funding of Postdoctoral in Hei-longjiang Bayi Agricultural University, the Innovation Fundof Postgraduate in Heilongjiang Bayi Agricultural Univer-sity, and Program for Young Scholars with Creative Talentsin Heilongjiang Bayi Agricultural University (Grant no.CXRC2017011).

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Table 3: Metabolic pathways of the differential metabolites.

Metabolic pathways Total Expected Hits Raw p Compounds PathwayFatty acid biosynthesis 47 0.20668 2 0.01545 C00712, C06424 osa00061Glycerolipid metabolism 14 0.061566 1 0.06017 C00116 osa00561Phenylalanine, tyrosine, and tryptophan biosynthesis 22 0.096746 1 0.09323 C00463 osa00400Galactose metabolism 26 0.11434 1 0.10941 C00116 osa00052Pyrimidine metabolism 39 0.1715 1 0.1604 C00214 osa00240Total: the total number of metabolites in the target metabolic pathway; hits: the number of differential metabolites in the target metabolic pathway; raw p: P

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WC5

Class4

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–4

2,4,6-Tri-tert-butylebenzenethiolthymidine1,6-Anhydro-beta-D-glucosetetradecanoic acid9,12-(Z,Z)-Octadecadienoic acid9-(Z)-Octadecenoic acidindole9,12-Octadecadienoic acidGlycerolalpha-D-Methylfructofuranoside

W18

WC6

WC4

WC29

WC30

WC28

WC1

WC27

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WC24

WC3

WC12

WC23

WC13

WC14

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WC25

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C11JSJ29JSJ30W

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C9W

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JSJ14JSJ18JSJ15JSJ21JSJ17JSJ19JSJ22JSJ20JSJ24JSJ8JSJ11JSJ9JSJ6JSJ23JSJ10JSJ5JSJ12

Class

JSJ

WC

WC7

JSJ13JSJ2JSJ1

WC19

Figure 5: /e hierarchical cluster diagram of differential metabolites.

6 Journal of Chemistry

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