Differentiating Organically and Conventionally Grown Oregano Using

Article pubs.acs.org/JAFC

Differentiating Organically and Conventionally Grown Oregano Using Ultraperformance Liquid Chromatography Mass Spectrometry (UPLC-MS), Headspace Gas Chromatography with Flame Ionization Detection (Headspace-GC-FID), and Flow Injection Mass Spectrum (FIMS) Fingerprints Combined with Multivariate Data Analysis Boyan Gao,†,§ Fang Qin,† Tingting Ding,† Yineng Chen,† Weiying Lu,*,† and Liangli (Lucy) Yu*,†,§ †

Institute of Food and Nutraceutical Science, School of Agriculture and Biology, Shanghai Jiao Tong University, Shanghai, China Department of Nutrition and Food Science, University of Maryland, College Park, Maryland 20742, United States

§

ABSTRACT: Ultraperformance liquid chromatography mass spectrometry (UPLC-MS), flow injection mass spectrometry (FIMS), and headspace gas chromatography (headspace-GC) combined with multivariate data analysis techniques were examined and compared in differentiating organically grown oregano from that grown conventionally. It is the first time that headspace-GC fingerprinting technology is reported in differentiating organically and conventionally grown spice samples. The results also indicated that UPLC-MS, FIMS, and headspace-GC-FID fingerprints with OPLS-DA were able to effectively distinguish oreganos under different growing conditions, whereas with PCA, only FIMS fingerprint could differentiate the organically and conventionally grown oregano samples. UPLC fingerprinting provided detailed information about the chemical composition of oregano with a longer analysis time, whereas FIMS finished a sample analysis within 1 min. On the other hand, headspace GC-FID fingerprinting required no sample pretreatment, suggesting its potential as a high-throughput method in distinguishing organically and conventionally grown oregano samples. In addition, chemical components in oregano were identified by their molecular weight using QTOF-MS and headspace-GC-MS. KEYWORDS: UPLC-MS, QTOF-MS, headspace-GC, multivariate data analysis, oregano (Origanum vulgare)

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INTRODUCTION Organic food has attracted more consumers in the past decades, especially in the past 5 years. A survey indicated that in 2012, U.S. consumers spent $31.5 billion on organic food, with an annual growth of 10.3% compared with that in 2011.1 The survey also indicated that private label and contract manufacturing keep increasing in the organic field, which results in a higher demand for analytical technologies to differentiate organic foods from conventionally grown foods. The development of novel methods might prevent food adulteration and protect both consumers and the organic food market. Oregano (Origanum vulgare) is a widely used culinary spice. Besides daily use as a cooking spice, oregano may also be recognized as a functional food because of its bioactivities.2−19 Previous research indicated that oregano extracts have many beneficial effects, including anti-inflammatory,2 antibacterial,3 lipid oxidation reducing,4,5 antimalarial,6 antimicrobial,7−10 and antioxidant10−19 activities. Both conventional and organic oreganos are commercially available and differ significantly in their prices. Therefore, analytical methods for differentiating oregano samples from different growing conditions are in high demand to detect possible adulteration. Previous research indicated that both LC-UV and flow injection electrospray ionization with ion trap mass spectrometry (FIMS) fingerprints are effective in differentiating organically and conventionally grown spices.20−23 Since 2012, conventionally and organically grown peppermint, sage, and © 2014 American Chemical Society

basil samples have been differentiated using both HPLC-UV and flow injection electrospray ionization with ion trap mass spectrometry (FIMS) fingerprinting techniques combined with principal component analysis (PCA) in our laboratory.22−24 As a continuation of our effort in detecting food adulteration, this study examined whether headspace-GC, UPLC-MS, and FIMS fingerprinting combined with statistical approaches could effectively differentiate organically and conventionally grown oregano samples. This was the first effort to distinguish organically and conventionally grown spices by headspace-GC fingerprints of the nonpolar volatile components.

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MATERIALS AND METHODS

Standard Compounds and Other Chemicals. LC-MS grade acetonitrile, methanol, and formic acid were purchased from SigmaAldrich (St. Louis, MO, USA). LC-MS grade water was obtained from a Milli-Q 10 ultrapure water system (Billerica, MA, USA). All other chemical reagents were of analytical grade purchased from SigmaAldrich (St. Louis, MO, USA) and used without further purification. Plant Materials and Sample Preparation. Ten USDA certified organic oregano leaf samples and 10 conventional oregano leaf samples were gifts from the Frontier Natural Products Co-op (Norway, IA, USA). Dry botanical samples were ground to 20 mesh powder using an IKA A11 laboratory grinder (IKA, Staufen, Baden-

Received: Revised: Accepted: Published: 8075

May 23, 2014 July 18, 2014 July 22, 2014 July 22, 2014 dx.doi.org/10.1021/jf502419y | J. Agric. Food Chem. 2014, 62, 8075−8084

Journal of Agricultural and Food Chemistry

Article

Figure 1. Representative headspace-GC fingerprints for commercial conventional and organic oregano samples. Württemberg, Germany) and stored at −20 °C before analysis. One hundred milligrams of every sample powder was accurately weighed and extracted with 10 mL of water/MeOH (1:1, v/v) with ultrasonication at ambient temperature for 30 min. The extracts were filtered with a 0.22 μm GHP syringe filter (Waters, Milford, MA, USA) and ready for LC-MS and FIMS fingerprinting analyses. One hundred milligrams of oregano samples was accurately weighed and directly sealed into headspace sample bottles and ready for GC analyses. Each sample was analyzed in triplicate. Headspace GC and GC-MS Conditions. An Agilent 7890A GC with an Agilent 7697A headspace sampler and an FID was used for GC fingerprinting analysis (Agilent, Loveland, CO, USA). Carrier gas was helium at a flow rate of 2.5 mL/min. An HP-5 phenyl methyl siloxane column (30 m × 320 μm with a 0.25 μm film thickness) from Agilent was used. The injection volume was 1 μL at a split ratio of 10/ 1. FID temperature was 300 °C. The initial oven temperature was 100 °C, which was increased at 10 °C/min to 250 °C and held for 5 min. Headspace oven, loop, and transfer line temperatures were 100, 110, and 120 °C, respectively. An Agilent 7890A/5973N headspace-GC−single-quadruple MS system was selected for the identification of volatile substances. The temperature programming was the same as that for headspace-GC fingerprinting. Quadruple temperature was 150 °C, source temperature was 230 °C, and cone voltage was 70 eV. The mass range was set from 30 to 450 Da. Individual substances were tentatively identified through the molecular weight of substances and the material information in the database. Quantification was based on the area under each substance peak as absolute peak area, and peak area was compared to the reference peak (peak 14) as relative peak area. UPLC-MS and FIMS Conditions. A Waters Acuity UPLC-Xevo G2 QTOF MS system was selected for both UPLC-MS and FIMS analyses. Mobile phase A consisted of 0.1% formic acid in water, and mobile phase B consisted of 0.1% formic acid in acetonitrile for both UPLC-MS and FIMS analyses. For UPLC-MS, a Waters BEH C18

column (2.1 mm i.d. × 100 mm, 1.7 μm) was used at 40 °C; the elution gradient started with 5% phase B, changed linearly to 50% in 10 min, increased linearly to 90% B at 15 min, was maintained for 2 min and then returned to its initial conditions for 2 min to reequilibrate the column for the next injection. The flow rate was 0.4 mL/min with an injection volume of 2 μL. The MS detector conditions were as follows: capillary voltage, 2.50 kV; sampling cone voltage, 60 V; extraction cone voltage, 4.0 V; source temperature, 120 °C; and desolvation temperature, 450 °C. The cone gas flow rate was 100 L/h, and the desolvation gas was 800 L/h. An MSE method was used with a mass range from m/z 100 to 1000 in ESI negative mode, scan time was 0.3 s, and the ramp collision energy was 30 eV. For FIMS, the mobile phase of methanol/water in a ratio of 1:1 (v/ v) was used, and the flow rate was 0.2 mL/min. Oregano extracts were diluted 10 times before injection, and the injection volume was 2 μL. MS spectra were collected from 0.05 to 0.55 min, and MS conditions were the same as those used in UPLC-MS analysis. Masslynx 4.1 software (Waters) was used for alignment of peaks and accurate mass weight calculation and identification. Data Processing. Thirty-seven major chromatographic peaks were detected in the UPLC-MS fingerprints in both organically and conventionally grown oregano samples. Their absolute and relative peak areas were used for orthogonal partial least-squares discriminant analysis (OPLS-DA). For absolute peak area analyses, 37 peak areas in all of the UPLC-MS chromatograms were analyzed with OPLS-DA directly. For relative peak area analyses, peak 36 was selected as a reference peak, and other peak areas were divided to the reference peak in each chromatogram to obtain the relative peak area. Peaks with areas of >5% of the reference peak area were recognized as major peaks and taken into account for calculation. For FIMS fingerprints, one-dimensional spectra ranging from m/z 100 to 1000 were collected for analysis. For headspace-GC fingerprints, both absolute and relative peak areas were selected for analyses. Peak 14, as the largest peak in headspace-GC, was selected as a reference peak, and other peak areas were divided by the reference peak area in each chromatogram to 8076

dx.doi.org/10.1021/jf502419y | J. Agric. Food Chem. 2014, 62, 8075−8084


Article

Figure 2. Orthogonal partial least-squares discriminant analysis (OPLS-DA) (A) scores plot and (B) loading plot for headspace-GC absolute peak areas of the organically and conventionally grown oregano samples. performed for one-dimensional FIMS fingerprints obtained from m/z 100 to 1000 using the Markerlynx software (Waters).

obtain the relative peak area. PCA and OPLS-DA were performed using SIMCA-P software (Umetrics, Malmo, Skånelän, Sweden) to analyze all of the data: containing 37 absolute peak areas in all 60 chromatograms and 36 relative peak areas in all 60 chromatograms (20 samples with triplicate analyses each) in UPLC-MS fingerprints; 23 absolute peak areas in all 60 chromatograms and 22 relative peak areas in all 60 chromatograms in headspace-GC fingerprints. PCA was also

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RESULTS AND DISCUSSION

Headspace-GC Fingerprinting Analysis. This was the first time that headspace-GC fingerprints were examined for their possible application to differentiate organically and 8077



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Figure 3. Orthogonal partial least-squares discriminant analysis (OPLS-DA) (A) scores plot and (B) loading plot for headspace-GC relative peak areas of the organically and conventionally grown oregano samples. All peak areas were divided by the area of peak 14 in each sample, and the results were used for OPLS-DA.

between two matrices to model the covariance structures in these two spaces. Headspace-GC fingerprinting is an effective method in the analysis of volatiles. It has been widely used because it requires less sample pretreatment and has high sensitivity. The OPLS-DA scores plot and loading plot of the headspace-GC fingerprints for absolute peak area are provided in panels A and B, respectively, of Figure 2. As shown in Figure 2A, the conventional oregano samples are all on the left side of

conventionally grown spices. A total of 23 peaks were detected in a gas chromatograph (Figure 1). As shown in Figure 1, there was no characteristic component or deterministic large peak to separate the conventional and organic oregano samples by manual observation. OPLS-DA is a statistical method that finds a linear regression model by projecting the predictor and observation variables to a new space. It can be used to find the fundamental relationships 8078



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Figure 4. Representative UPLC-MS fingerprints for commercial conventionally and organically grown oregano samples.

whereas all of the organic samples are located on the left side of the scores plot, separating well the LV1. In the loading plot for the headspace-GC relative peak areas (Figure 3B), peaks 13 (thymol) and 1 were on the right side of loading scatter plot, indicating that these two substances had higher concentrations in the conventional oregano samples, and peaks 6 (3,7dimethyl-1,6-octadien-3-ol), 12 (thymoquinone), 4, and 5 (γterpinene) were greater in the organic oregano samples. Because peak 14 was the RP, the results indicated that the levels of peak 13 (thymol), 6 (3,7-dimethyl-1,6-octadien-3-ol), 12 (thymoquinone), 4, and 5 (γ-terpinene) might be important in determining if an oregano sample was organically or conventionally produced according to its headspace-GC fingerprints. LC-MS Fingerprinting Analysis. The 50% MeOH extract of oregano samples were analyzed with UPLC-QTOF-MS. As shown in Figure 4, organic oregano samples had the same peak numbers as their conventional counterparts, but relative peak areas were visually different in these two groups, especially for peaks 3, 11, 22, and 34. The OPLS-DA scores plot and loading plot of the UPLC-MS chromatographic absolute peak areas are provided in Figure 5, panels A and B, respectively. The conventional oregano samples are all on the left side of the OPLS-DA scores plot, whereas all of the organic oregano samples are located on the right side. OPLS-DA in LC fingerprints was able to visualize the results and made the comparison of the chromatographic fingerprints easy to read and understand without subjective decisions. In the loading plot of the OPLS-DA absolute peak areas (Figure 5B), peak 34 (ursolic acid) contributed most to separate the organic samples from their conventional counterparts, followed by peaks 11 (catechin 7-xyloside), 18 (luteolin), 32 (6-methylscutellarein), and 3 (baicalin). On the other hand, peaks 36 (methylgalangin) and 23 (thymol) showed significant contribution to the separation of conventional oregano

the scores of latent variable (LV) 1, whereas all of the organic samples are located on the right. The scores plot indicated that headspace-GC combined with OPLS-DA could effectively differentiate the organically and conventionally grown oregano samples by determining their volatile components. This is the first report on the potential application of headspace-GC fingerprints combined with OPLS-DA in differentiating commercial organically and conventionally grown botanicals. In the loading plot of the headspace-GC absolute peak areas (Figure 2B), peaks 13, 1, and 8 contributed significantly to the separation of conventional samples, whereas peaks 6, 12, 5, and 4 contributed significantly to the separation of organic oregano samples (Figure 2B). In addition, GC-MS analysis was performed, and 10 compounds were tentatively characterized by comparing their MS result with that in the database, including p-cymene (peak 3), γ-terpinene (peak 5), 3,7dimethyl-1,6-octadien-3-ol (peak 6), 5-methyl-2-(1methylethyl)cyclohexanone (peak 8), isoborneol (peak 9), thymoquinone (peak 12), thymol (peak 13), 1-(N-methylcarbamate), 3-methyl-5-(1-methylethyl)phenol (peak 14), caryophyllene (peak 19), and β-bisabolene (peak 21). Relative peak area represents the relative amount of each component against a selected base peak. Previous studies showed the different degrees of effectiveness in differentiating organically and conventionally grown botanicals using the absolute peak area and the relative peak area.22,23 Therefore, the relative peak areas for the headspace-GC fingerprints were also analyzed using OPLS-DA in the present study, and Figure 3 shows the scores plot and loading plot of the relative peak areas for the organic and conventional oreganos. Among the 23 peaks, peak 14 showed the highest peak area and was selected as the reference peak (RP) for calculating the relative peak area. Other major peaks were defined as at least 1% of the area of the RP. In Figure 3A, all of the conventional oregano samples are clustered on the right side of the OPLS-DA scores plot, 8079



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Figure 5. Orthogonal partial least-squares discriminant analysis (OPLS-DA) (A) scores plot and (B) loading plot for UPLC-MS absolute peak areas of the organically and conventionally grown oregano samples.

samples. With the UPLC-MS chromatographic fingerprint taken into account, the result also confirmed the fact that organic oregano samples contained higher levels of ursolic acid, catechin 7-xyloside, luteloin, and 6-methylscutellarein. Including the peaks mentioned above, there were a total of 33 identified substances in oregano according to their accurate mass weight and previous research results.25−28 Peaks were identified as scutellarein (peak 1), baicalin (peak 3), catechin 7xyloside isomer (peak 4), catechin 7-xyloside isomer (peak 5), catechin 7-xyloside isomer (peak 7), acacetin (peak 9), aromadendrin/eriodictyol (peak 10), catechin 7-xyloside

(peak 11), luteolin-7-O-glucoside (peak 12), apigenin-7-Oglucoside (peak 13), thujene (peak 14), kaempferol 3-xyloside (peak 15), kaempferol 3-O-(β-D-xylopyranosyl-(1→2)-α-Dribopyranoside) (peak 16), apigenin (peak 17), luteolin (peak 18), fortunellin (peak 19), cassiaoccidentalin B (peak 21), rehin (peak 22), thymol (peak 23), 6,7-dimethylscutellarein (peak 24), 3,3′,4-tri-omethlellagic acid (peak 25), acacetin isomer (peak 26), tricalysioside I (peak 27), hederagenin isomer (peak 28), hederagenin isomer (peak 29), hederagenin (peak 30), 6methylscutellarein isomer (peak 31), 6-methylscutellarein (peak 32), oleanolic acid (peak 33), ursolic acid (peak 34), 8080



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Figure 6. Orthogonal partial least-squares discriminant analysis (OPLS-DA) (A) scores plot and (B) loading plot for UPLC-MS relative peak areas of the organically and conventionally grown oregano samples. All peak areas were divided by the area of peak 36 in each sample, and the results were used for OPLS-DA.

pinocembrin (peak 35), methylgalangin (peak 36), and methylgalangin isomer (peak 37). The major peaks that have been detected and identified in oregano by UPLC-MS were flavonoids, flavonoid glycosides, and pentacyclic triterpene acids. The results also confirmed the fact that UPLC-MS is an effective tool in analyzing the polar components in botanicals. The OPLS-DA scores plots of the relative UPLC-MS peak areas for the organic and conventional oreganos are shown in Figure 6, panels A and B, respectively. In all 37 chromatographic peaks, all of the other 36 peaks were taken into OPLSDA analysis except peak 36 (methylgalangin) was selected as the reference peak (RP) for calculating the relative peak area. As shown in Figure 6A, the conventional oregano samples are

located on the left side of the scores plot, whereas the organic oregano samples are all on the right side of the scores plot. The same trend of scores plot between the absolute peak areas and relative peak areas indicated that all of the oregano samples had a uniformal chemical profile regardless of their growing condition. In the loading plot of UPLC-MS relative peak areas (Figure 6B), the same trend was observed in both organically and conventionally oregano samples. Peak 36 was selected as RP and not taken into account, and peak 23 (thymol) was on the left side of loading scatter plot, indicating that the relative area of thymol was greater in conventional samples, whereas peaks 34 (ursolic acid), 11 (catechin 7xyloside), 18 (luteolin), 32 (6-methylscutellarein), and 3 8081



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Figure 7. Representative FIMS fingerprints for commercial conventionally and organically grown oregano samples.

(baicalin) showed higher relative peak areas in organic oregano samples and are located on the right side of the loading scatter plot. FIMS Fingerprint Analysis. Typical FIMS fingerprints for both organically and conventionally grown oregano samples are shown in Figure 7. The highest natural abundant peak in both conventional and organic oregano spectra is m/z 191.0548, followed by m/z 359.0765, 161.0240, 285.0393, and 461.0724. All of these peaks are not the molecular ion peaks of chemical substances identified in oregano, indicating that these spectral peaks might be the major fragment ion peaks. The FIMS PCA scores plot and loading plot are shown in Figure 8. As one of the most widely used data analysis methods, PCA is a mathematical approach transforming a large number of correlated variables into a small group of uncorrelated variables, the principal components (PCs). In the PCA scores plot of oregano FIMS fingerprints, all of the conventional samples were on the lower side, whereas all of the organic samples were on the upper side (Figure 8A). The scores plot indicated that FIMS fingerprints could effectively differentiate conventionally and organically grown oregano samples in a 1 min analytical time. In the loading plot of FIMS fingerprints (Figure 8B), most of the ions were clustered near the abscissa, but there were still a few ions dispersed far away from the center, indicating that these ions and their natural abundance might be more

important in differentiating the growth conditions of oregano. In the upper side of the loading plot, ions m/z 200.9919, 127.9888, 290.0744, 230.9881, 199.9843, 154.9736, and 156.9659 yielded higher scores and would lead to positions in the upper side of the PCA scores plot. On the other hand, the most important ions that contributed positively to conventional oregano samples were ions at m/z 867.1962, 614.1596, 798.1968, and 572.1494. All of the ions mentioned above were not the major chemical substances detected in UPLC-MS analysis, indicating that some trace chemical materials or the fragment ions of major chemical materials played the most important role in differentiating organic oreganos from conventional ones in FIMS fingerprints. In conclusion, headspace-GC, UPLC-MS, and FIMS fingerprints combined with multivariate data analysis tools may have potential application in effectively differentiating commercial organically and conventionally grown oregano and other botanical samples. This was the first study in which headspace-GC fingerprinting showed potential in rapidly differentiating organically and conventionally grown oregano samples. UPLC-MS fingerprinting may obtain detailed chemical composition information about the samples, but requires a longer analysis time. FIMS fingerprinting may provide a rapid and accurate test and has the potential for high-throughput applications. In addition, the results from UPLC-MS fingerprints and GC-MS analyses suggested that thymol might play 8082



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Figure 8. Principal component analysis (PCA) (A) scores plot and (B) loading plot for FIMS fingerprints of the organically and conventionally grown oregano samples.

the most important role in separating the conventionally grown oregano samples from the organically grown ones, and the concentration difference of thymol might be due to the different growth condition, genotype, and the interactions between genotype and growing environment. In practical application, headspace-GC fingerprints and FIMS combined with multivariate data analysis may be selected first for unknown spice samples, and additional UPLC-MS and GCMS identifications may be performed if necessary. Besides, the results also indicated that, compared with PCA, other multivariate data analysis methods such as OPLS-DA may also be utilized for differentiating organically and conventionally grown oregano samples. These data analysis methods and

chemical analysis techniques might be used in combination for distinguishing botanical samples.

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AUTHOR INFORMATION

Corresponding Authors

*(W.L.) E-mail: [email protected]. *(L.Y.) Mail: Department of Nutrition and Food Science, University of Maryland, College Park, MD 20742, USA. Phone: (301) 314-3313. E-mail: [email protected]. Funding

This research was supported by two grants from the National High Technology Research and Development Program of China (Grants 2013AA102202 and 2013AA102207), a special 8083



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(16) Rababah, T. M.; Banat, F.; Rababah, A.; Ereifej, K.; Yang, W. Optimization of extraction conditions of total phenolics, antioxidant activities, and anthocyanin of oregano, thyme, terebinth, and pomegranate. J. Food Sci. 2010, 75, C626−C632. (17) Rodriguez-Meizoso, I.; Marin, F. R.; Herrero, M.; Senorans, F. J.; Reglero, G.; Cifuentes, A.; Ibanez, E. Subcritical water extraction of nutraceuticals with antioxidant activity from oregano. Chemical and functional characterization. J. Pharm. Biomed. 2006, 41, 1560−1565. (18) Ozkan, G.; Baydar, H.; Erbas, S. The influence of harvest time on essential oil composition, phenolic constituents and antioxidant properties of Turkish oregano (Origanum onites L.). J. Sci. Food Agric. 2010, 90, 205−209. (19) Kulisic, T.; Radonic, A.; Katalinic, V.; Milos, M. Use of different methods for testing antioxidative activity of oregano essential oil. Food Chem. 2004, 85, 633−640. (20) Xie, Z.; Zhao, Y.; Chen, P.; Jing, P.; Yue, J.; Yu, L. L. Chromatographic fingerprint analysis and rutin and quercetin compositions in the leaf and whole-plant samples of di- and tetraploid Gynostemma pentaphyllum. J. Agric. Food Chem. 2011, 59, 3042−3049. (21) Zhao, Y.; Xie, Z.; Niu, Y.; Shi, H.; Chen, P.; Yu, L. Chemical compositions, UPLC/MS fingerprinting profiles and radical scavenging properties of commercial Gynostemma pentaphyllum (Thunb.) Makino samples. Food Chem. 2012, 134, 180−188. (22) Gao, B.; Lu, Y.; Sheng, Y.; Chen, P.; Yu, L. Differentiating organic and conventional sage by chromatographic and mass spectrometry flow injection fingerprints combined with principal component analysis. J. Agric. Food Chem. 2013, 61, 2957−2963. (23) Gao, B.; Lu, Y.; Qin, F.; Chen, P.; Shi, H.; Charles, D.; Yu, L. Differentiating organic from conventional peppermints using chromatographic and flow-injection mass spectrometric (FIMS) Fingerprints. J. Agric. Food Chem. 2012, 60, 11987−11994. (24) Lu, Y.; Gao, B.; Chen, P.; Charles, D.; Yu, L. Characterisation of organic and conventional sweet basil leaves using chromatographic and flow-injection mass spectrometric (FIMS) fingerprints combined with principal component analysis. Food Chem. 2014, 154, 262−268. (25) Cavero, S.; Garcıa-Risco, M. R.; Marın, F. R.; Jaime, L.; Santoyo, S.; Senorans, F. J.; Reglero, G.; Ibanez, E. Supercritical fluid extraction of antioxidant compounds from oregano chemical and functional characterization via LC−MS and in vitro assays. J. Supercrit. Fluids 2006, 38, 62−69. (26) Lin, L. Z.; Mukhopadhyay, S.; Robbins, R. J.; Harnly, J. M. Identification and quantification of flavonoids of Mexican oregano (Lippia graveolens) by LC-DAD-ESI/MS analysis. J. Food Compos. Anal. 2007, 20, 361−369. (27) Shen, D.; Pan, M. H.; Wu, Q. L.; Park, C. H.; Juliani, H. R.; Ho, C. T.; Simon, J. E. LC-MS method for the simultaneous quantitation of the anti-inflammatory constituents in oregano (Origanum species). J. Agric. Food Chem. 2010, 58, 7119−7125. (28) Exarchou, V.; Godejohann, M.; van Beek, T. A.; Gerothanassis, I. P.; Vervoort, J. LC-UV-solid-phase extraction-NMR-MS combined with a cryogenic folw probe and its application to the identification of compounds present in greek oregano. Anal. Chem. 2003, 75, 6288− 6294.

fund for Agro-scientific Research in the Public Interest (Grant 201203069), the SJTU startup fund for young talent (Grant 13X100040047), and the SJTU 985-III disciplines platform and talent fund (Grants TS0414115001 and TS0320215001). Notes

The authors declare no competing financial interest.

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REFERENCES

(1) The organic trade association’s 2013 organic industry survey. March 10, 2014; http://www.thepacker.com/fruit-vegetable-news/ 222580921.html. (2) Shen, D.; Pan, M. H.; Wu, Q. L.; Park, C. H.; Juliani, H. R.; Ho, C. T.; Simon, J. E. LC-MS method for the simultaneous quantitation of the anti-inflammatory constituents in oregano (Origanum species). J. Agric. Food Chem. 2010, 58, 7119−7125. (3) Dadalioglu, I.; Evrendilek, G. A. Chemical compositions and antibacterial effects of essential oils of Turkish oregano (Origanum minutif lorum), bay laurel (Laurus nobilis), Spanish lavender (Lavandula stoechas L.), and fennel (Foeniculum vulgare) on common foodborne pathogens. J. Agric. Food Chem. 2004, 52, 8255−8260. (4) Botsoglou, N. A.; Florou-Paneri, P.; Christaki, E.; Fletouris, D. J.; Spais, A. B. Effect of dietary oregano essential oil on performance of chickens and on iron-induced lipid oxidation of breast, thigh and abdominal fat tissues. Br. Poult. Sci. 2002, 43, 223−230. (5) Vekiari, S. A.; Oreopoulou, V.; Tzia, C.; Thomopoulos, C. D. Oregano flavonoids as lipid antioxidants. J. Am. Oil Chem. Soc. 1993, 70, 483−487. (6) Babili, F. E.; Bouajila, J.; Souchard, J. P.; Bertrand, C. e.; Bellvert, F.; Fouraste, I.; Moulis, C.; Valentin, A. Oregano: chemical analysis and evaluation of its antimalarial, antioxidant, and cytotoxic activities. J. Food Sci. 2011, 76, 512−518. (7) Lambert, R. J.; Skandamis, P. N.; Coote, P. J.; Nychas, G. J. A study of the minimum inhibitory concentration and mode of action of oregano essential oil, thymol and carvacrol. J. Appl. Microbiol. 2001, 91, 453−462. (8) Becerril, R.; Gomez-Lus, R.; Goni, P.; Lopez, P.; Nerin, C. Combination of analytical and microbiological techniques to study the antimicrobial activity of a new active food packaging containing cinnamon or oregano against E. coli and S. aureus. Anal. Bioanal. Chem. 2007, 388, 1003−1011. (9) Skandamis, P.; Tsigarida, E.; Nychas, G.-J. E. The effect of oregano essential oil on survival/death of Salmonella typhimuriumin meat stored at 51C under aerobic,VP/MAP conditions. Food Microbiol. 2001, 19, 97−103. (10) Tiziana Baratta, M.; Damien Dorman, H. J.; Deans, S. G.; Biond, D. M.; Ruberto, G. Chemical composition, antimicrobial and antioxidative activity of laurel, sage, rosemary, oregano and coriander essential oils. J. Essent. Oil Res. 1998, 10, 618−627. (11) Exarchou, V.; Nenadis, N.; Tsimidou, M.; Gerothanassis, I. P.; Troganis, A.; Boskou, D. Antioxidant activities and phenolic composition of extracts from Greek oregano, Greek sage, and summer savory. J. Agric. Food Chem. 2002, 50, 5294−5299. (12) Pizzale, L.; Bortolomeazzi, R.; Vichi, S.; Beregger, E. U.; Conte, L. S. Antioxidant activity of sage (Salvia of f icinalis and S. fruticosa) and oregano (Origanum onites and O. indercedens) extracts related to their phenolic compound content. J. Sci. Food Agric. 2002, 82, 1645−1651. (13) Quiroga, P. R.; Grosso, N. R.; Nepote, V. Antioxidant effect of poleo and oregano essential oil on roasted sunflower seeds. J. Food Sci. 2013, 78, S1904−S1912. (14) Milos, M.; Mastelic, J.; Jerkovic, I. Chemical composition and antioxidant effect of glycosidically bound volatile compounds from oregano (Origanum vulgare L. ssp. hirtum). Food Chem. 2000, 71, 79− 83. (15) Puertas-Mejı ́a, M.; Hillebrand, S.; Stashenko, E.; Winterhalter, P. In vitro radical scavenging activity of essential oils from Columbian plants and fractions from oregano (Origanum vulgare L.) essential oil. Flavour Fragrance J. 2002, 17, 380−384. 8084


Differentiating Organically and Conventionally Grown Oregano Using

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