Accurate Analysis and Evaluation of Acidic Plant Growth Regulatorsin Transgenic and Nontransgenic Edible Oils with Facile Microwave-Assisted Extraction−DerivatizationMengge Liu,†,‡,∥ Guang Chen,*,†,‡,§,∥ Hailong Guo,†,‡ Baolei Fan,# Jianjun Liu,†,‡ Qiang Fu,⊥ Xiu Li,†,‡

Xiaomin Lu,†,‡ Xianen Zhao,†,‡ Guoliang Li,†,‡ Zhiwei Sun,†,‡ Lian Xia,†,‡ Shuyun Zhu,†,‡

Daoshan Yang,†,‡ Ziping Cao,†,‡ Hua Wang,†,‡ Yourui Suo,§ and Jinmao You*,†,‡,§

†The Key Laboratory of Life-Organic Analysis, Qufu Normal University, Qufu 273165, Shandong, China‡Key Laboratory of Pharmaceutical Intermediates and Analysis of Natural Medicine, Qufu Normal University, Qufu 273165,Shandong, China§Northwest Institute of Plateau Biology, Chinese Academy of Sciences, Xining 810001, China#Hubei University of Science and Technology, Xianning, 437100 China⊥Qinghai Normal University, Xining, 810008 China

*S Supporting Information

ABSTRACT: Determination of plant growth regulators (PGRs) in a signal transduction system (STS) is significant fortransgenic food safety, but may be challenged by poor accuracy and analyte instability. In this work, a microwave-assistedextraction−derivatization (MAED) method is developed for six acidic PGRs in oil samples, allowing an efficient (<1.5 h) andfacile (one step) pretreatment. Accuracies are greatly improved, particularly for gibberellin A3 (−2.72 to −0.65%) as comparedwith those reported (−22 to −2%). Excellent selectivity and quite low detection limits (0.37−1.36 ng mL−1) are enabled byfluorescence detection−mass spectrum monitoring. Results show the significant differences in acidic PGRs between transgenicand nontransgenic oils, particularly 1-naphthaleneacetic acid (1-NAA), implying the PGRs induced variations of components andgenes. This study provides, for the first time, an accurate and efficient determination for labile PGRs involved in STS and apromising concept for objectively evaluating the safety of transgenic foods.

KEYWORDS: plant growth regulators (PGRs), microwave-assisted extraction−derivatization (MAED), transgenic food,signal transduction system (STS)

■ INTRODUCTION

Plant growth regulators (PGRs) play vital roles in cell division,plant reproduction,1 gene expression regulation, etc.2 Recently,much attention has been focused on the signal transductionsystem (STS),3−7 in which PGRs lead to the initial componentchange and then the variation of genetic trait or compositionafter PGR-induced gene expression.3,7 For example, gibberellinA3 (GA3) promotes α-amylase production, thereby regulatingsugar level,8,9 and in return reflects variations of two genes,copalyl pyrophosphate synthase and ent-kaurene synthase,involved in the biosynthesis of GA3.

10 Similarly, indole-3-aceticacid (IAA), indole-3-propionic acid (IPA), indole-3-butyric acid(IBA), 1-naphthaleneacetic acid (1-NAA), and 2-naphthalene-acetic acid (2-NAA) are also extensively involved in thecorresponding PGR signal transduction system.11−14 Therefore,accurate determination of these PGRs is of great significance forstudying the PGR-induced component or gene variation.Especially, despite the intense controversy with nontransgenicfoods, transgenic foods are being increasingly produced.15,16

Thus, a comparative study toward the component/genedifference between transgenic and nontransgenic foods isnecessary, from which an objective evaluation on transgenicfoods is highly desired.

Endogenous PGRs occur naturally in a complex matrix andinfluence physiological processes at low concentrations.17

Among the reported methods for PGR determination,17−37

unsatisfactory low sensitivity,17,19,21,26−28,35 a poor selectiv-ity,24,25,28,37 limited PGRs,17−20,25,26,28−30,36,37 and applicabil-ity25,26,29,30,37 were usually found. Due to the weakchromophore in molecules, PGRs at trace levels were difficultto detect directly without any pretreatment. Althoughpreconcentration could lower detection limits, the interferingcomponents would be also concentrated together. In this case,multistep manual operations such as solid-phase extraction(SPE)18,22,27,29−33 or liquid− liquid microextraction(LLME)23,38,34 were usually required. However, these oper-ations came with certain limitations, coelution of interferingspecies, high sample carry-over, emulsion formation, andanalytes loss, thus tending to cause large error and lowrecovery.17,19,22,27,29,32,33,35 More significantly, GA3, NAA, andIAA were susceptible to light, temperature, or oxygen,24,38−41

which increased the difficulty for the accurate determination of

Received: April 30, 2015Revised: August 18, 2015Accepted: August 25, 2015Published: August 26, 2015

Article

pubs.acs.org/JAFC

© 2015 American Chemical Society 8058 DOI: 10.1021/acs.jafc.5b02489J. Agric. Food Chem. 2015, 63, 8058−8067

PGRs with multiple operations. In addition, to both theexperimenter and the environment, tedious operation with highreagent consumption was always unsatisfactory. Consequently,although a few works have been done, there are still challengesin the sensitive, accurate, efficient, and convenient determi-nation of endogenous PGRs in complex samples.A precolumn derivatization technique is usually applied to

overcome the above difficulties by improving sensitivity andselectivity as well as accuracy.36,37 Practically, derivatizationmight also contribute to the stabilization of labile PGRs viamolecular modification. To the best of our knowledge, noderivatization was found for the simultaneous determination ofPGRs involved in a signal transduction system to date. Arecently synthesized fluorescent probe, 2-(12,13-dihydro-7H-dibenzo[a,g]carbazol-7-yl)ethyl4-methylbenzenesulfonate(DDCETS), was selected for this study,42 which, however,provided unsatisfactory derivatization yield and low efficiencyin tentative experiments. Microwave proved to be an efficienttechnique for complex samples.43 Thus, the combination ofmicrowave-assisted extraction with derivatization will bebeneficial to establishing the desired method.In this study, a method with microwave-assisted extraction−

derivatization (MAED) followed by high-performance liquidchromatography (HPLC)−fluorescence detection (FLD)coupled with mass spectra (MS/MS) (MAED-HPLC-FLD-MS/MS) has been developed for the quantification of six acidicPGRs (IAA, IPA, IBA, 1-NAA, 2-NAA, and GA3) in transgenicand nontransgenic edible oils including soybean oil, sunfloweroil, and rapeseed oil. The MAED technique allows for anefficient pretreatment for oil samples, with DDCETS used asfluorescent reagent to improve sensitivity as well as stability.Experimental conditions are optimized by single- and multi-variable methods to maximize response of analytes.44 Instead ofgas chromatography (GC), HPLC is selected to avoid thepotential decomposition or isomerization caused by hightemperature. The combination of FLD with tandem MS/MSenables both the sensitive quantification and the real-timemonitoring of labile analyte and interference. This is the firsttime that the MAED technique has been applied to acidic PGRanalysis. Sensitivity, accuracy, precision, recovery, and repeat-ability are significantly improved, especially for labile PGRs.Correlations between PGRs and components/genes areexplored, providing a promising approach to establish anobjective evaluation for transgenic foods.

■ MATERIALS AND METHODSMaterials and Chemicals. Standards of IAA, IPA, IBA, 1-NAA, 2-

NAA, and GA3 were purchased from Shanghai Chemical Reagent Co.(≥98%). HPLC grade methanol, formic acid, and acetonitrile (ACN)were purchased from Sigma-Aldrich Co. (St. Louis, MO, USA). Waterwas purified on a Milli-Q system (Millipore, Bedford, MA, USA).DDCETS was prepared following a literature method.42 Transgenicsoybean oil, nontransgenic soybean oil, transgenic sunflower oil,nontransgenic sunflower oil, transgenic rapeseed oil, and non-transgenic rapeseed oil were all purchased from a supermarket inQufu (Shandong province, China).Preparation of Solutions. Solutions for PGRs (GA3, 5.46 × 10−3

mol L−1; IAA, 5.65 × 10−3 mol L−1; IPA, 5.18 × 10−3 mol L−1; IBA,5.62 × 10−3 mol L−1; 1-NAA, 8.22 × 10−3 mol L−1; 2-NAA, 8.27 ×10−3 mol L−1) were prepared by dissolving an appropriate amount ofcorresponding standard in ACN, respectively. The stock solution (5 ×10−4 mol L−1) was prepared by diluting the mixed solution with ACN.Then, in a similar way, the working solutions for calibration curves andvalidation (30, 50, 100, 200, 1000, 1500, and 3500 ng mL−1) wereprepared. Derivatizing reagent solution (1.04 × 10−2 mol L−1) was

prepared by dissolving 120.5 mg of DDCETS in 25 mL of anhydrousACN. When not in use, all solutions were stored at 4 °C in arefrigerator.

Microwave-Assisted Extraction−Derivatization (MAED). Asample of 3 g was weighed and transferred to a Teflon vessel, to which6 mL of methanol, 5 mL of N,N-dimethylformamide (DMF), 1 mL ofDDCETS (1.04 × 10−2 mol L−1), and 40 mg of K2CO3 were addedsuccessively. The screwed vessel was placed into a microwave oven,which was then set at 130 °C (power set as 1000 W) for 35 min withmagnetic stirring. The resulting mixture was transferred into a tube forcentrifugation (2500g for 5 min). The supernatant was concentratedby nitrogen stream (55 kPa) until a final volume of 4 mL, which wasthen filtered through a 0.22 μm nylon film and injected to HPLC-FLD-MS/MS system. Chemical structures of PGRs and derivatizationscheme are shown in Figure 1.

Optimization of MAED. First, single-variable experiments werecarried out to evaluate factors including extraction solvents (methanol;ACN; chloroform; ethanol; DMF), cosolvents (DMF; ethyl acetate;acetone; dimethyl sulfoxide (DMSO); chloroform), 40 mg of basiccatalysts (pyridine; 2-methylpyridine; K2CO3; Na2CO3; K2C2O4),derivatization reagent (concentration = 1.04 × 10−2 mol L−1; volume =0.6, 0.8, 1.0, 1.2, or 1.4 mL), temperature (80, 100, 130, 135, or 140°C), and microwave time (15, 25, 35, 45, or 55 min). All experimentswere performed at three sample levels (weight = 1.0, 3.0, and 5.0 g).Considering the interaction of factors with each other, Box−Behnkendesigns45 with five variables, microwave exposition time (X1, min),temperature (X2, °C), DMF volume (X3, mL), DDCETS volume (X4,mL), and methanol volume (X5, mL) were applied to optimizeconditions for MAED, which were statistically analyzed by softwareDesign-Expert 7.1.3 Trial.

Instrumental Analysis. An XH-100A microwave synthesisapparatus (Beijing Xiang Hu Science and Technology DevelopmentCo. Ltd.) was used to perform MAED operation. HPLC analysis wasperformed using an Agilent 1100 system equipped with online vacuumdegasser (G1322A), quaternary pump (G1311A), autosampler(G1329A), and thermostated column compartment (G1316A).Chromatographic separation was achieved on an Aglient ZORBAXSB-C18 column (150 mm × 4.6 mm, 5 μm i.d., USA). Elutionconditions were as follows: 0 min, 35% A and 65% B; 20 min, 20% Aand 80% B; 25 min, 0% A and 100% B (stop time, 5 min), where Aand B were 5% ACN solution (ACN/H2O: 5/95, v/v) and 100%ACN, respectively. Flow rate was constant at 1 mL/min, and column

Figure 1. Chemical structures of PGRs (gibberellin A3 (GA3), indole-3-acetic acid (IAA), indole-3-propionic acid (IPA), indole-3-butyricacid (IBA), 1-naphthaleneacetic acid (1-NAA), and 2-naphthalene-acetic acid (2-NAA)) and derivatization scheme of DDCETS withPGRs.

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temperature was kept at 30 °C. The HPLC system was followed in

sequence by a fluorescence detector (FLD, G1321A, λex= 292 nm and

λem= 402 nm) and an online mass spectrometer 1100 series LC-MSD

Trap-SL (ion trap) with an atmospheric pressure chemical ionization

(APCI) source (Bruker Daltonik, Bremen, Germany). The auto-MS

operation parameters were as follows: APCI in positive ion detection

mode; nebulizer pressure, 60 psi; dry gas temperature, 350 °C; dry gas

flow, 5.0 L/min; APCI Vap temperature, 350 °C; corona current, 4000nA; capillary voltage, 3500 V. The injection volume was 10 μL.

Validation of the Proposed Method. Linearity was measuredfrom 30 to 3500 ng mL−1 for each PGR. Limit of detection (LOD)and limit of quantification (LOQ) were defined as the minimumdetectable amount of analyte with a signal-to-noise ratio (S/N) of 3and 10, respectively. FLD repeatability was investigated by measuringthe relative standard deviations (RSD) for peak area and retention

Table 1. Box−Behnken Design (BBD), Validation by Multicriteria, Nonparametric Tests, and Optima

independent variablea responseb

runc X1 X2 X3 X4 X5 exptl BBD validationd

1 20 130 7 1.0 6 477.82 319.33 multicriteria2 35 140 3 1.0 6 170.51 163.23 AME 7.72003 20 140 5 1.0 6 169.30 163.58 CE 0.99964 50 140 5 1.0 6 295.94 301.03 MAE −3.43785 20 130 5 1.4 6 332.91 339.22 RMSE 23.79006 35 130 3 0.6 6 396.90 404.62 MRE −0.88007 50 130 5 1.0 4 382.31 387.39 R2 0.98758 20 120 5 1.0 6 115.50 107.849 20 130 3 1.0 6 327.31 333.84 nonparametric tests10 35 130 5 1.0 6 798.90 803.45 p value 0.117711 35 130 5 1.0 6 800.50 803.4512 50 130 5 1.4 6 722.20 719.40 optima13 35 130 5 0.6 8 513.90 510.99 X1 34.8714 20 130 5 1.0 8 370.22 363.23 X2 130.0515 35 130 5 1.4 4 459.17 459.53 X3 4.6916 35 130 3 1.0 8 411.90 413.74 X4 1.0717 50 120 5 1.0 6 419.06 422.20 X5 6.2418 35 130 5 0.6 4 380.10 380.30 BBD 803.4519 50 130 5 0.6 6 420.80 414.83 exptl 809.5120 35 120 7 1.0 6 390.64 393.8121 35 140 7 1.0 6 401.90 404.3922 50 130 5 1.0 8 754.14 747.8523 35 130 5 1.0 6 805.80 803.4524 35 130 3 1.0 4 405.42 400.7325 50 130 7 1.0 6 760.00 757.6126 35 130 7 0.6 6 454.46 456.4227 35 130 3 1.4 6 410.51 408.8528 35 130 5 1.0 6 803.10 803.4529 35 140 5 0.6 6 202.58 199.8930 50 130 3 1.0 6 343.22 347.3731 35 130 7 1.0 4 412.00 409.8532 35 130 7 1.0 8 795.98 800.3533 35 130 7 1.4 6 760.20 752.7734 35 140 5 1.4 6 393.60 396.9735 35 130 5 1.0 6 200.22 202.7536 35 120 5 0.6 6 280.85 279.3937 35 120 5 1.0 8 477.82 484.2138 35 130 5 1.0 6 809.51 803.4539 20 130 5 0.6 6 340.05 343.2040 35 140 5 1.0 8 341.78 348.1341 35 120 5 1.0 4 180.68 179.1042 35 140 5 1.0 4 251.35 249.7443 35 130 5 1.4 8 735.10 732.3544 35 120 3 1.0 6 245.84 239.2445 20 130 5 1.0 4 315.80 320.1946 35 120 5 1.4 6 800.50 803.45

aIndependent variables were X1, derivatization time (min); X2, derivatization temperature (°C); X3, DMF volume; X4, DDCETS volume; X5,methanol volume. bAverage experimental and predicted total peak areas of derivatives (n = 6). cThe 46 runs from the BBD were given by thesoftware Design-Expert 7.1.3 trial. dValidation: multicriteria (AME, absolute maximum error; CE, coefficient of efficiency; MAE, mean absoluteerror; RMSE, root mean squared error; MRE, mean relative error; R2, correlation of determination and nonparametric tests (p value with Wilcoxonrank sum method)).

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time with 10 μL of standard sample (0.5 ng of PGR injected; sixreplicates (n = 6)). Recovery was investigated with the equation (S2 −S1)/S0 × 100%, where S0, S1, and S2 are the peak areas of qualitycontrol (QC) solution (20, 50, 100 ng mL−1 of standard solutions),samples, and samples spiked with QC solution, respectively. The

matrix effect was evaluated as follows: matrix effect (%) = Sp/Ss ×100%, where Ss and Sp are the peak areas of QC solution andpostextraction sample spiked with QC solution, respectively. Intradayand interday precisions were determined by running samples spikedwith QC solutions at three levels (50, 200, and 1000 ng mL−1) (n = 6).

Figure 2. Response surface of the extraction−derivatization yield (expressed as peak area) affected by time and temperature (A), time and DMFvolume (B), time and DDCETS volume (C), time and methanol volume (D), temperature and DMF volume (E), temperature and DDCETSvolume (F), temperature and methanol volume (G), DMF volume and DDCETS volume (H), DMF volume and methanol volume (I), andDDCETS volume and methanol volume (J).

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Table2.

Linear

RegressionEqu

ation,

Coefficientof

Determination(r

2 ),R

epeatabilityof

Retention

Tim

eandPeakArea,Lim

itof

Detection

(LOD),Lim

itof

Quantification

(LOQ)withFluo

rescence

Detection

,and

Intra-andInterday

Accuracy(R

E%)andPrecision

(RSD

%)forGiberellin

A3(G

A3),Ind

ole-3-aceticAcid(IAA),Indo

le-3-propion

icAcid(IPA),Indo

le-3-butyric

acid

(IBA),1-Naphthaleneacetic

Acid(1-N

AA),and2-Naphthaleneacetic

Acid(2-N

AA)(n

=6)

repeatability

(RSD

%)

fluorescence

detection

(ngmL−

1 )intraday

interday

analyte

linearregression

eqaY=(a

±SD

)X+(b

±SD

)r2

linearrange

(ngmL−

1 )retention

time

peak

area

LODb

LOQb

spike

(ngmL−

1 )meanc

±SD

dRE%e

RSD %f

meanc

±SD

dRE%e

RSD %f

GA3

Y=(0.7142±

0.03953)X+

(−0.1157

±0.11786)

0.9998

50−3500

0.26

1.11

1.36

4.22

5048.6

±0.32

−2.72

0.66

48.1±

0.89

−3.84

1.85

200

197.8±

1.28

−1.11

0.65

193.9±

3.25

−3.05

1.68

1000

993.5±

5.21

−0.65

0.52

988.6±

7.47

−1.14

0.76

IAA

Y=(1.3064±

0.02688)X+

(−0.1713

±0.4812)

0.9999

30−1500

0.17

1.51

1.05

3.16

5051.5

±1.52

2.20

2.97

48.5±

2.30

−3.00

4.74

200

195.6±

5.65

−2.21

2.89

192.9±

9.30

−3.55

4.82

1000

997.4±

8.26

2.27

0.83

968.0±

8.54

−3.20

0.88

IPA

Y=(1.3309±

0.01885)X+

(−0.2052

±0.2563)

0.9999

30−1500

0.17

1.31

1.01

3.54

5051.0

±1.29

2.00

2.53

48.1±

2.32

−3.80

4.82

200

196.8±

6.92

−1.60

3.52

194.4±

10.70

−2.80

5.50

1000

1003.4

±7.55

0.34

0.75

989.0±

10.43

−1.10

1.05

IBA

Y=(1.6114±

0.02852)X+

(−0.4006

±0.3804)

0.9998

30−3500

0.14

1.21

1.13

3.59

5050.7

±1.21

1.40

2.39

48.0±

0.95

−3.96

1.99

200

203.2±

4.92

1.60

2.42

195.2±

6.11

−2.40

3.13

1000

1007.7

±4.92

0.77

0.49

978.0±

26.45

−2.20

2.70

1-NAA

Y=(1.0484±

0.01536)X+

(−0.1427

±0.1942)

0.9998

30−1500

0.08

1.28

0.37

1.20

5048.9

±2.19

−2.16

4.48

48.2±

3.18

−3.60

6.60

200

202.4±

4.19

1.20

2.07

193.4±

10.36

−3.30

5.36

1000

970.4±

25.6

−2.96

2.64

957.0±

45.35

−4.30

4.74

2-NAA

Y=(1.1675±

0.02929)X+

(0.2053±

0.7177)

0.9996

30−1500

0.07

1.13

0.37

1.11

5048.9

±2.19

−2.22

4.48

48.2±

2.21

−3.60

4.59

200

205.2±

7.28

2.60

3.55

194.2±

10.96

−2.90

5.64

1000

978.9±

18.90

−2.11

1.93

1036.8±

32.22

3.68

3.11

aY,peak

area;X

,injectedmole(pmol);SD

,standarddeviations

ofsixduplicates

(n=6).bLO

DsandLO

Qswereobtained

onthebasisof

signal-to-noiseratio

sof

3and10,respectively.c The

meanvalue

obtained

from

allof

thespiked

blanksamples.dStandard

deviation(n

=6).eThe

relativeerrors

percentage

foraccuracy.fThe

relativestandard

deviationpercentage

forprecision(n

=6).

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In view of the potential instability of GA3, NAA, and IAA,24,38,39 allPGRs and their DDCETS-labeled derivatives were investigated forshort-term (12 h) and long-term stability (7 days). Storage stabilitywas evaluated at 4 °C for 7 days. Freeze−thaw stability was evaluatedafter two cycles of freeze (−20 °C for 7 days)−thaw (roomtemperature, spontaneously) performance. Six duplicates spiked ateach of three concentration levels (100, 500, and 1000 ng mL−1) wereanalyzed, and the mean ± standard deviation (SD) with percentrelative error (RE%) was calculated.

■ RESULTS AND DISCUSSIONOptimization of MAED. Single-Variable Optimization.

On the basis of solvent investigation, absolute methanol wasfound to be the best medium for high response (Figure S1, 1a−1e). Due to the large structure of derivatized analyte, cosolventis necessary to improve the solubility. As shown in Figure S1,2a−2e, DMF proved to be the most suitable cosolvent.Moreover, addition of DMF effectively avoided the precip-itation of hydrophobic derivative found in ethyl acetate.Catalyst investigation showed the enhanced response when40 mg of K2CO3 was added, owing to its specifically strongbasicity in DMF (Figure S1, 3a−3e). Temperature investigation(Figure S1, 5a−5e) from 80 to 140 °C revealed that, for theefficient extraction−derivatization of PGRs, the system shouldbe heated to a temperature lower than 135 °C, otherwisedecomposition might occur. In addition, 35 min of MAED wassufficient for the complete extraction−derivatization of PGRs,which can be seen from Figure S1, 6a−6e, for timeinvestigation.Multivariate Optimization. For efficient pretreatment, a

thorough optimization with 46 runs of experiments forinteractive variables is listed in Table 1, and response surfacesare plotted in Figure 2. Results for correlation (R2 = 0.9875)and coefficient of efficiency (CE = 0.9996 (approximate to 1))indicated the multivariate model was satisfactorily established.A nonparametric test gave a p value of 0.1177 (>0.05), showingthat the differences between experimental and predicted valueswere statistically insignificant (at the 95% confidence level).46

Together, these results demonstrated that values correlatingwell with experimental data can be predicted by regression ofthe multivariate model. Accordingly, the optimal variablescombination (X1, 34.87; X2, 130.05; X3, 4.69; X4, 1.07; and X5,6.24) was achieved, which was confirmed by being applied tothree independent replicates of MAED operations, giving apeak area of 809.51, an experimental value quite approximate tothe predicted 803.45.Thus, the optimal conditions for MAED are established: to a

50 mL vessel were added successively 3 g of edible oil, 6 mL ofmethanol, 5 mL of DMF, 40 mg of K2CO3, and 1 mL ofDDCETS (1.04 × 10−2 mol L−1); then the vessel was placed inthe microwave apparatus at 130 °C with shaking for 35 min.Method Validation. It can be seen from Table 2 that linear

equations with excellent correlation coefficients of ≥0.9996were developed over the wide ranges. As expected, quite lowLODs (0.37−1.36 ng mL−1) and LOQs (1.11−4.22 ng mL−1)were obtained with higher analytical sensitivity than most ofthose reported,17,19−22,26−28,35,36 which should be attributed tothe strong fluorescence responses and low interferences of thismethod. The satisfactory repeatability for retention times(0.07−0.26%) and peak areas (1.11−1.51%) demonstrated thatFLD quantification was reliable, whereas no obvious analyteloss was observed. Benefiting from the high efficiency, thesensitivity and accuracy (RE%, intraday, −2.96 to 2.60%;interday −4.30 to 3.68%) and precision (RSD%, intraday,

0.49−4.48%; interday, 0.76−6.60%) for these PGRs (Table 2)were improved.18,31,32 Especially, GA3 was usually determinedwith rather unsatisfactory results: accuracy (RE%, intraday, −22to −2%; interday, −21 to −9%) and precision (RSD%,intraday, 3.5−6.8%, 2.66−10.9%; interday, 4.4−10.8%, 1.73−11.95%),18,31 whereas with this method, obvious improvementsof accuracy (RE%, intraday −2.72 to −0.65%; interday −3.84 to−1.14%) and precision (RSD%, intraday, 0.52−0.66%; inter-day, 0.76−1.85%) for GA3 were achieved.To assess the effect of matrix in blank and real samples,

corresponding investigations were performed (Table S1).Results for matrix effects (95.5−103.9%) showed that noobvious interference was caused by blank matrices. Meanwhile,satisfactory recovery (96.1−104.4%) indicated an insignificanteffect of real sample matrix on analytes. Therefore, this methodwas almost not affected by coexistent components, revealing itshigh selectivity for PGRs.In addition, stability was studied. From Table S2 it can be

found that the loss of PGRs (particularly for GA3) caused bylight or temperature seems to be unavoidable. Meanwhile, ashort-term experiment showed the obviously low RE% relativeto long term. In view of these facts, it was reasonable to haveextraction and quantification finished as soon as possible withanalyte being protected. In practice, the rapid experimentalprocess of MAED saved much time, thereby reducing thesusceptibility of analytes. Moreover, when PGRs werederivatized, the RE% values were improved, particularly forGA3, with a significant improvement from −9.91 to −2.70%,indicating the protective effect of MAED pretreatment on labileanalyte. Besides, comparing RE% values in 4 °C with those in−20 °C, the storage temperature of 4 °C should be beneficialfor all stock solutions and samples.

Method Development and Comparison. Comparisonsbetween previous and present methods are listed in Table S3.GC-MS was usually applied for PGR detection,21,22 but theelevated temperature was disadvantageous to labile GA3.

38,39

SPE, LLME, ultrasonic-assisted extraction (UAE), and MAEintegrated with LC-MS17,18,23,27,30−35 were widely used,providing low detection limits but meanwhile needing multipleoperations with rather long time (2.5 h−1.5 days). Complicatedoperations tended to cause high system errors, as poorrecoveries for labile GA3 were usually observed.18,27,31,33

Among the reported methods, HPLC-FLD with derivatizationtechnique provided more accurate determination with excellentrecoveries,24,36,37 probably because of the stabilizing effect ofderivatization (see Table S2). In the present work, instead ofusing conventional SPE or LLME, this method is improved byintegrating the extraction and derivatization in a closed Teflonvessel, thus allowing the pretreatment to be finished with afacile operation in 1.5 h. Moreover, the microwave oven cancontain 10 Teflon vessels at least, which makes this methodmore suitable for batch analysis than conventional 10 sets ofinstruments equipped with an oil bath or a sand bath,demonstrating the remarkable capacity, efficiency, and conven-ience relative to conventional methods.17−37 With thederivatization technique, the detection limits are improvedand, more importantly, labile analytes can be stabilized via themolecular modification. Nevertheless, in view of the suscept-ibility of GA3, further identification by online MS/MS wasfollowed. As shown in Figure S2, a molecular ion peak (m/z641.0) and specific fragment ion peaks (m/z 295.7, 346.3, and374.1) identified the DDCETS-derivatized GA3. No impuritybut excessive probe was observed, showing the good selectivity

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of this method. Thus, the proposed MAED-HPLC-FLD-MSmethod proved to be more powerful for the accurate, rapid, andconvenient determination of PGRs.Sample Analysis. The developed method was applied to

the simultaneous determination of six PGRs in edible oilsamples including transgenic and nontransgenic soybean oil,sunflower oil, and rapeseed oil. Chromatography is shown inFigure 3, and PGRs contents are summarized in Table 3.Overall, remarkable differences were observed between trans-genic and nontransgenic samples: 1-NAA was detected only innontransgenic samples; IPA was detected only in transgenicrapeseed oil and nontransgenic soybean oil samples; GA3, IAA,IBA, and 2-NAA were found in all of the samples at different

contents. In the signal transduction system, release ofcomponent depends on the PGRs in biosynthesis. GA3

promoted production of α-amylase, thereby regulating thecontent of sugar; thus, its higher level in nontransgenic samples(rapeseed oil, 8.86 μg g−1; sunflower oil, 7.66 μg g−1) should beconsistent with the fact that nontransgenic foods containedhigher levels of sugar than transgenic foods.47,48 Serine wasadjusted by IAA; thus, its contents in transgenic rapeseed oil,nontransgenic soybean oil, and transgenic sunflower oil wouldbe higher.11 NAA was much higher in nontransgenic rapeseedoil than in the other samples, probably indicating the highercontent of fatty acid in the nontransgenic rapeseed.49

Transgenic food has been criticized and unrecognized, but

Figure 3. Typical chromatograms for PGR standards (A), nontransgenic soybean oil (B), transgenic soybean oil (C), nontransgenic sunflower oil(D), transgenic sunflower oil (E), nontransgenic rapeseed oil (F), and transgenic rapeseed oil (G). Peaks: 1, gibberellin A3 (GA3); 2, indole-3-aceticacid (IAA); 3, indole-3-propionic acid (IPA); 4, indole-3-butyric acid (IBA); 5, 1-naphthaleneacetic acid (1-NAA); 6, 2-naphthaleneacetic acid (2-NAA); 7, excessive DDCETS.

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Table3.Con

tentsof

GibberellinA3(G

A3),Ind

ole-3-aceticAcid(IAA),Indo

le-3-propion

icAcid(IPA),Indo

le-3-butyricacid

(IBA),1-NaphthaleneaceticAcid(1-N

AA),and2-

Naphthaleneacetic

Acid(2-N

AA)in

SixOilSamples

(n=6)

rapeseed

oil

soybeanoil

sunflow

eroil

nontransgenic

transgenic

nontransgenic

transgenic

nontransgenic

transgenic

PGRs

weighed

contenta

gmean

(μg)

avb

(μg/g)

RSD

c

(%)

mean

(μg)

avb

(μg/g)

RSD

c

(%)

mean

(μg)

avb

(μg/g)

RSD

c

(%)

mean

(μg)

avb

(μg/g)

RSD

c

(%)

mean

(μg)

avb

(μg/g)

RSD

c

(%)

mean

(μg)

avb

(μg/g)

RSD

c

(%)

GA3

18.71

8.860

1.9

5.61

5.900

1.1

3.23

3.300

1.8

3.24

3.340

2.0

7.56

7.660

2.1

3.35

3.530

0.7

326.98

0.9

18.21

1.8

9.85

2.5

10.05

1.4

23.16

3.6

10.97

1.3

544.46

2.5

30.01

2.9

16.99

3.1

17.10

1.9

38.51

2.4

17.99

1.9

IAA

12.87

2.930

2.4

4.48

4.830

1.5

3.03

3.150

0.6

1.31

1.360

2.3

2.05

2.160

0.9

2.93

3.060

1.1

38.98

2.8

15.01

1.7

9.77

0.7

4.13

2.7

6.71

1.3

9.22

1.9

514.68

1.4

25.10

2.1

15.82

1.1

6.99

2.6

11.01

1.8

15.93

3.1

IPA

1_d

__

0.03

0.030

0.7

0.02

0.018

1.3

__

__

__

__

_3

__

0.09

0.9

0.06

1.8

__

__

__

5_

_0.15

1.5

0.09

2.6

__

__

__

IBA

13.67

3.790

3.4

2.79

2.870

2.1

1.78

1.910

0.8

2.05

2.160

1.8

2.79

2.880

0.9

2.10

2.270

1.6

311.65

2.1

8.68

3.0

5.99

1.2

6.67

1.2

8.59

0.7

7.00

0.6

519.12

1.8

14.57

1.3

9.76

0.7

11.01

1.2

14.99

1.5

11.90

2.4

1-NAA

18.15

8.290

1.6

__

_0.03

0.042

1.1

__

_0.03

0.037

0.9

__

_3

24.88

1.9

__

0.13

1.4

__

0.11

1.6

__

542.10

1.0

__

0.26

1.8

__

0.19

1.2

__

2-NAA

110.67

10.850

3.8

0.03

0.034

1.5

0.04

0.039

3.1

0.02

0.017

1.8

0.04

0.040

1.9

0.03

0.026

3.2

332.73

2.5

0.10

1.7

0.12

2.7

0.06

0.7

0.12

2.0

0.08

1.3

554.79

2.3

0.19

1.2

0.19

2.6

0.08

1.3

0.21

1.5

0.14

1.9

aWeighed

threeam

ount

levelsof

samples.bThe

averagecontentsof

GA3,IAA,IPA

,IBA,1

-NAA,and

2-NAAin

oilsamples.cThe

relativestandard

deviation(n

=6).d−,n

otfound.

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this study reflects the advantages of nontransgenic food as wellas transgenic foods in terms of some nutrients. Consequently,PGR-induced nutrient variation is promising to be an importantindex for the objective evaluation of transgenic foods.On the other hand, for transgenic foods, what should be

explored is which and how many genes are directly or indirectlymodified. As the ending point of the signal transduction system,variations of genetic trait and composition are reflected byPGRs.3−5 GA3 was biosynthesized with the aid of two essentialgenes (copalyl pyrophosphate synthase and ent-kaurenesynthase); thus, its higher levels in nontransgenic samples(rapeseed oil, 8.86 μg g−1; sunflower oil, 7.66 μg g−1) indicatedthese two genes would decrease after the plant was gene-modified.10 Similarly, tryptophan monooxygenase and indolea-cetamide hydrolase were higher in transgenic rapeseed oil.12,50

IPA was found in transgenic rapeseed oil (0.03 μg g−1) andnontransgenic soybean oil (0.018 μg g−1), implying that thetryptophan aminotransferase genes in transgenic rapeseed andnontransgenic soybean were relatively higher.14 The higher IBAlevel in nontransgenic rapeseed oil (3.79 μg g−1), transgenicsoybean oils (2.16 μg g−1), and nontransgenic sunflower oils(2.88 μg g−1) might indicate the higher content of glutamate-γ-semialdehyde participating in the biosynthesis of IBA via thetryptophan pathway.13 Therefore, the results might reflect thePGR-induced genetic variation in addition to artificial genemodification, which is an added benefit of PGRs determination.

■ ASSOCIATED CONTENT*S Supporting InformationThe Supporting Information is available free of charge on theACS Publications website at DOI: 10.1021/acs.jafc.5b02489.

Figures of single-variable experiments and mass spectrumand tables of recovery, matrix effect, stability, methodcomparison, and content (PDF)

■ AUTHOR INFORMATIONCorresponding Authors*(G.C.) Mail: Key Laboratory of Pharmaceutical Intermediatesand Analysis of Natural Medicine, Qufu Normal University,Qufu, China. Phone: +86 537 4456305. Fax: +86 537 4456305.E-mail: chenandguang@163.com.*(J.Y.) E-mail: jmyou6304@163.com.

Author Contributions∥M.L. and G.C. contributed equally to this work.

FundingThis work was supported by the Natural Science Foundation ofShandong Province, China (ZR2013BQ019) and the NationalNatural Science Foundation of China (General Program)(21475074). This research work was supported by the OpenFunds of the Shandong Province Key Laboratory of DetectionTechnology for Tumor Markers (KLDTTM2015-6), theUnderg r adua t e Techno logy Innova t i on P ro j e c t(201410446021), the National Natural Science Foundation ofChina (General Program) (21475075), the 100 TalentsProgram of The Chinese Academy of Sciences (328), theNational Natural Science Foundation of China (GeneralProgram) (21275089), the National Natural Science Founda-tion of China (21405093, 21405094, 81303179, 31301595,21305076, 21302110, 21402106), the Natural ScienceFoundation of Shandong Province, China (ZR2014BM029),the Key Laboratory of Bioorganic Analysis Shandong Province,

and the Key Laboratory of Pharmaceutical Intermediates andAnalysis of Natural Medicine Shandong Province.

NotesThe authors declare no competing financial interest.

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