Food Chemistry 145 (2014) 941–949

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Analytical Methods

Floral classification of honey using liquid chromatography–diode array detection–tandem mass spectrometry and chemometric analysis Jinhui Zhou a,b,c, Lihu Yao d,e, Yi Li a,b,c, Lanzhen Chen a,b,c, Liming Wu a,b,c, Jing Zhao a,b,c,⇑ a

Bee Research Institute, Chinese Academy of Agricultural Sciences, Beijing 100093, China Bee Product Quality Supervision and Testing Center, Ministry of Agriculture, Beijing 100093, China c Laboratory of Risk Assessment for Quality and Safety of Bee Products, Ministry of Agriculture, Beijing 100093, China d South China Botanical Garden, Chinese Academy of Sciences, Guangzhou, Guangdong 510650, China e Food Safety & Quality, Ontario College of Technology, Toronto, ON M1S 1S6, Canada b

a r t i c l e

i n f o

Article history: Received 23 May 2012 Received in revised form 13 January 2013 Accepted 28 August 2013 Available online 4 September 2013 Keywords: HPLC–DAD–MS/MS Marker compounds Fingerprinting Chemometrics Floral origin

a b s t r a c t A high performance liquid chromatography–diode array detection–tandem mass spectrometry (HPLC–DAD–MS/MS) method for the floral origin traceability of chaste honey and rape honey samples was firstly presented in this study. Kaempferol, morin and ferulic acid were used as floral markers to distinguish chaste honey from rape honey. Chromatographic fingerprinting at 270 nm and 360 nm could be used to characterise chaste honey and rape honey according to the analytical profiles. Principal component analysis (PCA), partial least squares (PLS), partial least squares-discrimination analysis (PLS-DA) and soft independent modeling of class analogy (SIMCA) were applied to classify the honey samples according to their floral origins. The results showed that chaste honey and rape honey could be successfully classified by their floral sources with the analytical methods developed through this study and could be considered encouraging and promising for the honey traceability from unifloral or multifloral nectariferous sources. Ó 2013 Elsevier Ltd. All rights reserved.

1. Introduction Honey is produced and processed by honey bees (Apis mellifera) from the nectar and honeydew of plants. Honey contains natural sugars such as glucose and fructose, amino acids, enzymes, minerals, antioxidants such as flavonoids and phenolic acids, and others like pollen and wax (Elke, 1998). Of those compounds, polyphenolic flavonoids are ubiquitous in natural plant and are structurally categorized into flavonols, flavones, flavanones, isoflavones, catechins, anthocyanidins, and chalcones (Wollgast & Anklam, 2002). These compounds have the same basic skeleton containing 15 carbon atoms (a C6–C3–C6 configuration) comprised of two benzene rings linked through a heterocyclic pyran or pyrone (with a double bond) ring in the middle. The flavonoids differ in the numbers and position of hydroxyl groups, oxygen and hydrogen atoms bonded to the rings (Marie-Hélène et al., 1996). Flavonoids play an important role in the regulation of plant growth and development (Pietta & Simonetti, 1999; Shirley, 1996), and exhibit multiple biological effects on human health such as anti-allergic, anti-inflammatory, anti-microbial, anti-viral, anti-cancer, anti-thrombotic and vasodilating activities (Gheldof, ⇑ Corresponding author at: Bee Research Institute, Chinese Academy of Agricultural Sciences, Beijing 100093, China. Tel.: +86 10 6259 3411; fax: +86 10 6259 4054. E-mail address: [email protected] (J. Zhao). 0308-8146/$ - see front matter Ó 2013 Elsevier Ltd. All rights reserved. http://dx.doi.org/10.1016/j.foodchem.2013.08.117

Wang, & Engeseth, 2002; Pier-Giorgio, 2000; Vinson, Hao, Su, & Zubik, 1998). Of these health studies, for example, flavonoids act as potent metal chelators and free radical scavengers to exert their antioxidants, and protect cells by inhibition of enzymes or chelating trace metals against the injury of oxygen-centered free radicals. Therefore, flavonoids and their dietary sources have been of increasing interests of researchers during recent decades (Yao, Jiang, Singanusong, Datta, & Raymont, 2004). The main compositions of honey although are sugars and water, the minor chemical compositions actually determines its value or class, of which the difference in minor compounds of honey is strongly dependent on the floral/botanical origin or nectariferous plant (Yao et al., 2004). The unifloral honey is usually regarded as a more valuable class because it offers people to choose what flavour they prefer. In the market, the quality of honey and hence the prices are determined by their botanical origin, therefore increased price of some honey types stimulate the adulteration of honey of certain botanical origins. This is another effort that researchers have been trying to use marker compounds to control. Flavanoids and phenolic compounds have been used as marker compounds to fingerprint floral origin of honey since two decades ago, and hence various methods have been developed for the determination of flavonoids and botanical classification of honey using high-performance liquid chromatography (HPLC) (Yao et al., 2004), gas chromatography (Sanz, Gonzalez, de Lorenzo, Sanz, & Martinez-Castro, 2005) and capillary electrophoresis with

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different detectors (Aljadi & Kamaruddin, 2004). Owing to the presence of conjugated double and aromatic bonds in the structure, flavonoids exhibit the maximum absorbance in the vicinity of 280 nm and 360 nm in the ultra-violet (UV) region. Relative to the DAD and UV detection, the electrode coulometric detection (ECD) based on the measurements of the current resulting from oxidation/reduction reaction of the analyte at suitable electrode coulometric array detection provided spectacular selectivity and sensitivity for the analysis of flavonoids (Wittemer & Veit, 2003). The mass spectrometry (MS) equipped with electrospray ionization (ESI) or atmospheric pressure chemical ionization (APCI) source shows high sensitivity and has been employed for structural confirmation and quantitative analysis (e.g., molecular mass) of flavonoids (Edenharder, Keller, Platt, & Unger, 2001). Fingerprinting constructed by chromatographic techniques can be defined as a characteristic profile reflecting the chemical compositions for the evaluation and quality control of the analyzed sample, and may play important role for the discrimination or classification of them due to the complexity of natural substances (Tistaert, Dejaegher, & Heyden, 2011). Chemometric techniques have been widely accepted as the most powerful tools to characterize and classify samples for the analysis of geographical and botanical origins, of which Principal component analysis (PCA), partial least squares (PLS), Partial least squares-discrimination analysis (PLSDA) and soft independent modeling of class analogy (SIMCA) are commonly employed for modeling and analysis of complicated chemical or biological data that produce interpretable and reliable models capable of handling incomplete, noisy and collinear data structures (Di, Shuijuan, Xiaojing, Haiqing, & Yong, 2002; EuiCheol, Brian, Pegg, Dixon, & Eitenmiller, 2010). This study aims to explore a method of combination of various analytical methods listed above for honey floral authentication. Three aspects of attempts are made to achieve this goal, which include quantification of the flavonoids in rape and chaste honey and distinguishing their floral origins of honey, through comparison and analyses of the actual content ranges of specific flavonoids as floral marker compounds, their fingerprinting profiles and chemometric information. 2. Experimental 2.1. Materials Flavonoids including rutin (95.0%), myricetin (96.0%), quercetin (98.0%), kaempferol (97.0%), apigenin (95.0%), pinocembrine (95.0%), chrysin (97.0%), galangin (95.0%), morin (95.0%), ferulic acid (99.0%), naringenin (98.0%) and caffeic acid phenethyl ester (CAPE, 97.0%) were obtained from the Sigma–Aldrich (St. Louis, MO, U.S.A.). Acetonitrile, methanol and formic acid were the HPLC grade reagents (DIMA Technology Inc, Richmond, U.S.A.). Hydrochloric acid (37%) was of analytical reagent and purchased from Beijing chemical reagent company. The water used was purified using a Milli-Q system (Bedford, MA, USA). Solid-phase extraction (SPE) cartridges (Oasis HLB, 500 mg/6 mL) were purchased from Waters Corporation (Milford, MA, USA).

diate solutions were stored at 2–6 °C while the standard stock solutions at 20 °C. HPLC-DAD Conditions: The Agilent LC 1200 system (Agilent, Waldbronn, Germany) with a vacuum degasser (G1322A), a binary pump (G1312B), an auto sampler (G1367D), a column compartment (G1316B) and a diode array detector (G1315C) was used for the fingerprinting profile construction and chemometric data acquisition of honey samples. The chromatographic separation was carried out using a Zorbax SB-C18 column (100 mm  2.1 mm, 1.8 lm) (Agilent, Wilmington, DE). The complex flavonoid solutions were separated using binary gradient elution. The mobile phase consisted of (A) 0.1% formic acid, and (B) methanol; the gradient separation was performed as follows: 0–15 min, 10–25% (B); 15–20 min, 25–30% (B); 20–30 min, 30–35% (B); 30–35 min, 35–70% (B); 35–40 min, the isocratic composition was held for a further 5 min; 40–42 min, 70–10% (B); 42–50 min, 10% (B). The column temperature was maintained at 30 °C; flow rate was 0.2 mL min 1 and the injection volume was 5 lL. 2.3. Mass-spectrometric conditions An Agilent 6460 triple quadruple tandem MS coupled to electrospray ionization (ESI) interface and Agilent Jet Stream Ion Focusing (Agilent Technologies, Palo Alto, USA) was used for mass analysis and quantification of target analytes. The MS was operated in the negative ion mode. The tuning parameters were optimized for the target analytes: gas temperature 350 °C, drying gas flow 6 L min 1, nebulizer pressure 35 psi, Vcap voltage: 3500 V, sheath gas temperature: 350 °C, sheath gas flow: 9 L min 1, Nozzle voltage: 1000 V. The MS parameters applied for flavonoids with regard to the transitions from precursor to product ions were shown in Table 1. The system operation, data acquisition and analysis are controlled and processed by the MassHunter software. 2.4. Sample preparation A total of 187 raw honey samples were collected through beekeepers (accompanied by our researcher) to ensure the authenticity of botanical origin in different provinces of China between Table 1 The mass spectrometry parameters applied for 12 flavonoids. Compound name

Precursor ion (m/z)

Product ion (m/z)

Dwell time (ms)

Fragmentor (v)

CE (eV)

Apigenin

269

CAPE

283

Chrysin

253

Ferulic acid

193

Galangin

269

Kaempferol

285

Morin

301

2.2. Preparation of standard solutions

Myricetin

317

Individual standard stock solutions were prepared within the range 0.1–0.8 mg mL 1 by dissolving flavonoid standards in methanol. Mixed intermediate solutions were prepared by the 1:10 dilution of stock solutions with methanol. The standard working solutions were diluted directly to the required concentrations with pure water on the day of use, which was based on the sensitivity of detection and the linearity range of the study. The mixed interme-

Pinocembrine

255

Quercetin

301

Rutin

609

Naringenin

271

117 151 135 179 63 143 134 178 169 171 93 117 151 125 151 179 151 213 151 179 301 271 151 119

20 20 40 40 40 40 40 40 40 40 40 40 40 40 40 40 40 40 40 40 40 40 40 40

128 128 150 150 128 128 76 76 128 128 150 150 128 128 150 150 125 125 150 150 180 180 102 102

30 20 20 12 30 20 8 4 25 25 30 40 16 16 25 16 16 12 16 12 30 45 12 24

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March and September 2009. Of these samples, 98 were of chaste honey and 89 rape honey. Voucher specimens were deposited in Bee Research Institute and the honey samples were stored at 4 °C in darkness. Prior to analysis, honey samples were allowed to liquefy at room temperature for 1 h, and then inverted to mix. If honey was crystallized, it can be gently heated in a thermostatic bath at a temperature not exceeding 50 °C Portions of 10.0 g honey samples were weighted into 50 mL polypropylene centrifuge tube and mixed with 30 mL of pure water adjusted to pH 2 with HCl, until completely fluid by stirring in a magnetic stirrer for 15 min. The fluid samples were then centrifuged at 10,000 rpm for 10 min to remove the impurity particles. The clear supernatants were loaded onto the Oasis HLB cartridge column, which was previously activated with methanol (10 mL) followed by pure water (10 mL) acidified with HCl (pH 2). The cartridge was washed with 10 mL pure water and the flavonoids were eluted with 3 mL methanol. The extract was evaporated at 40 °C under a stream of nitrogen. The residue was reconstituted in 1.0 mL of 0.1% formic acid: acetonitrile (70:30). The samples were then mixed on a Vortexer for 2 min and filtered through filter membrane with 0.25 lm pore size. The final solutions were transferred to autosampler vials and stored at 4 °C until further analysis by HPLC–DAD–MS/MS. 2.5. Data analysis The identification and quantification of flavonoids was performed with MassHunter software released by Agilent Technologies. Fingerprinting profiles and similarity of honey samples were constructed and analyzed with ‘‘Similarity Evaluation System for Chromatographic Fingerprinting of Traditional Chinese Medicine (Version 2004A)’’, which is recommended by state food and drug administration (SFDA) of China and mainly used in the similarity analysis of chromatographic for windows. The multivariate analysis included PCA, PLS, PLS-DA and SIMCA were carried out using MATLAB 2009b (The Mathworks Inc., Natick, Mass., U.S.A.).

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arising from cinnamoyl structure. In order to obtain reliable fingerprinting profiles of flavonoids with maximum absorbance, characteristic chromatographic patterns were recorded at 270 nm and 360 nm as the detection wavelength, respectively. In this study, a total of 12 flavonoids with different structures were analysed for their content, fingerprinting profiles and chemometrics information under the optimized chromatographic conditions. 3.2. Mass spectra Optimization of the MS conditions was performed using the standard solutions of flavonoids for multiple reaction monitoring (MRM) analysis from the characteristic mass spectra by syringe pump infusion. The standard solutions (50 lg/mL) were infused into the MS separately to obtain precursor ions and product ions as specific transition and to optimize mass parameters such as fragmentor and collision energy. Flavonoids can be analyzed in both positive and negative ion modes according to the previous literatures (Cuyckens & Claeys, 2004; Lee, Kim, Liu, Oh, & Lee, 2005; Madhusudanan, Kusum, Harrison, & Kulshreshtha, 1985). In the full scan mass spectra, the protonated and deprotonated molecular ions [M + H]+ and [M H] of 12 flavonoids were detected to compare their abundances. The highest sensitivity is obtained using ESI in the positive ion mode with an eluent system of acetonitrile with 0.1% formic acid, with higher the response of flavonoids except myricetin (Fig. 1). However, there are several issues affecting the using of positive ionisation mode: (1) some of the targeted analytes were easily interfered with other compounds in the total ion chromatogram; (2) [M + Na]+ or [M + K]+ are formed in the mode; (3) biological matrix or glass container containing sodium and potassium metal adducts; (4) multi-molecular complexes such as [2 M + H]+ due to the too high concentration of target analytes; (5) no response was found for myricetin. Therefore, the negative ESI mode was selected to analyze the flavonoids for the comparatively stable and better selectivity and sensitivity for MS analyses. 3.3. Sample clean-up procedure

3. Results and discussion 3.1. Chromatographic conditions Acetonitrile and methanol as the organic mobile phase modifiers are widely used in LC–MS analysis and are hence tested to improve the separation of flavonoids. Very polar analytes require low concentrations of organic to elute from the column, while non-polar compounds require higher concentrations. Acetonitrile provides higher sensitivity and sharper peak shapes than methanol. However, methanol presents better resolution among chromatographic peaks of flavonoids. Increasing the aqueous phase concentration or decreasing the acetonitrile content from 70% to 10% can significantly increase the retention and resolution of the flavonoids. Different types of volatile acids including formic acid (0.05–0.5%, v/v) and acetic acid (0.05–0.5%, v/v) as modifier in the mobile phases are employed to minimize peak tailing and enhance the ion efficiency in the ESI positive mode. The proportion of modifier in the mobile phase varying from 0.05 to 0.5% (v/v) slightly increases the retention of the flavonoids. Further trials found that the sharpened peak shapes and high responses of flavonids can be achieved when the concentration of formic acid was in the range of 0.1% on MS analysis. As a result, the combination of methanol and 0.1% formic acid was confirmed as the optimum mobile phase to identify and quantify the flavonoids in honey samples in the system. Two UV absorbance bands of flavonoids were observed at 260– 280 nm originating from benzene ring system and at 350–370 nm

It is difficult to isolate a complete profile of flavonoids from honey matrix since it contains a variety of flavonoids with different physical and chemical properties. In order to obtain as much flavonoids as possible from the honey samples, acidifying water was

Fig. 1. The TIC of 12 flavonoids under ESI positive and negative ion mode. Compounds name and retention time(min) are as follows: Ferulic acid(18.1), naringenin(19.3), Rutin(26.6), Myricetin(28.3), Morin(30.6), Quercetin(33.8), Kampferol(34.1), Chrysin(38.5), Pinocembrine(39.8), Apigenin(44.1), Galangin(46.4) and Cape(46.8).

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used to dissolve honey samples and denature the proteins. The impurities and denatured proteins were precipitated and removed from honey samples by centrifugation. The supernatant of honey samples was loaded onto the SPE column to remove the polar substances. Moreover, different SPE cartridges based on C18 bonded silica were examined and compared to achieve the satisfactory recovery under different washing and elution solvent. SPE cartridges such as Oasis cartridge were tested, as well as different combinations of methanol and water. Pure water showed best washing quality while increasing the methanol content resulted in reduced recovery of flavonoids and the decrease in the number of chromatographic peaks throughout the fingerprinting. For the elution of the flavonoids, methanol with different volume was tested. As a result, the Oasis cartridge and methanol with better recoveries for the MS detection and more chromatographic peaks for the DAD analysis were thus selected for further chromatogram isolation clean-up in this study. 3.4. Method validation The method was validated for linearity, limit of detection (LOD), limit of quantification (LOQ), recovery, matrix effect, inter-/intraday precision and stability according to the principles of the FDA industry guidance (Guidance for Industry-Bioanalytical Method Validation, 2001). 3.4.1. Calibration curves Linear regression analysis of 12 flavonoids was performed by analyzing six different concentrations (0.008–88 lg/mL) of the

standard solutions in five batches (Table 2). The standard curves were obtained by plotting peak area (y) versus nominal concentration (x (lg mL 1)) of each compound and were fitted to the linear regression y = ax + b. The calibration curves were calculated by using the weighted least squares linear regression (weighting factor = 1/x2) which could weak the variance at higher concentrations throughout the curve range because the data at the high end of the calibration curve tend to dominate the calculation of the linear regression and result in excessive error at the bottom of the curve (Almeida, Castel-Branco, & Falcão, 2002). The standard concentrations were back-calculated from constructed calibrations curves for flavonoids. All the calibration curves showed good linearity (R2 P 0.9981). 3.4.2. Limits of detection and limits of quantification The LODs and LOQs were calculated with the MRM chromatograms of 12 flavonoids from honey sample extracts using the quantification transition. The LOD is determined by spiking control samples (QC) with standard solutions of 12 flavonoids at a series of low and high concentration levels. The LOQ is determined as the lowest concentration that can be detected with acceptable recovery and precision (recovery > 75%, relative standard deviation within at least 15%, n = 5). The LODs and LOQs were 0.1– 1.0 lg 100 g 1 and 0.3–3.0 lg 100 g 1 for 12 flavonoids, respectively. Compared with reported method in honey samples or other matrix (Gheldof et al., 2002; Martos, Ferreres, & Tomás-Barberán, 2000), this method presents much better sensitivity with more detailed values about LOD and LOQ for the flavonoids analysed. The higher sensitivity may result from the appropriate fragmentor, col-

Table 2 Validation parameters of 12 flavonoids (n = 5). Linearity rangea (lg mL 1)

LOD (lg 100 g

Apigenin

0.024–24

0.3

0.8

CAPE

0.008–8.0

0.1

0.3

Chrysin

0.044–88

0.5

1.5

Ferulic acid

0.058–58

0.6

2.0

Galangin

0.080–80

1.0

3.0

Kaempferol

0.024–24

0.3

0.8

Morin

0.011–11

0.2

0.5

Myricetin

0.044-44

0.5

1.5

Pinocembrine

0.066-33

0.8

2.4

Quercetin

0.005-2.5

0.1

0.3

Rutin

0.044-22

0.5

1.5

Naringenin

0.020-20

0.2

0.6

Analytes

R2P0.9981

1

)

LOQ (lg 100 g

1

)

Spiked concentration (lg 100 g-1)

Recovery (% ± SD)

Matrix effect (% ± SD)

Intra-day precision (RSD, %)

Inter-day precision (RSD, %)

0.6 6 60 0.3 3 30 1 10 100 2 20 200 2.5 25 250 0.6 6 60 0.5 5 50 1 10 100 2 20 200 0.2 2 20 1 10 100 0.6 6 60

80.6 ± 4.7 85.7 ± 4.3 82.2 ± 3.1 76.6 ± 5.9 80.6 ± 3.2 86.1 ± 3.5 82.4 ± 4.5 86.3 ± 5.3 88.9 ± 3.0 85.3 ± 5.2 87.1 ± 4.9 92.9 ± 3.3 87.8 ± 6.0 86.0 ± 4.2 89.7 ± 3.8 90.1 ± 7.2 86.4 ± 5.3 95.3 ± 2.7 77.5 ± 4.9 80.2 ± 5.1 89.3 ± 2.7 81.1 ± 4.4 81.9 ± 3.1 89.2 ± 1.9 81.9 ± 6.2 84.6 ± 5.8 89.9 ± 3.0 76.6 ± 3.5 83.2 ± 4.9 87.1 ± 3.1 90.2 ± 6.5 82.5 ± 4.3 89.9 ± 2.8 74.9 ± 4.4 83.8 ± 3.1 95.3 ± 3.8

90.3 ± 4.2 91.4 ± 4.7 99.2 ± 2.7 96.8 ± 5.9 99.7 ± 3.2 100.1 ± 2.4 91.9 ± 2.7 95.7 ± 2.5 99.6 ± 1.9 98.3 ± 4.0 101.4 ± 5.1 97.2 ± 2.9 97.4 ± 4.8 96.4 ± 3.9 99.6 ± 1.9 95.4 ± 6.2 94.8 ± 4.8 103.3 ± 2.2 96.4 ± 5.0 92.3 ± 5.8 99.6 ± 2.0 98.0 ± 3.4 99.1 ± 4.2 99.2 ± 1.9 99.1 ± 8.3 96.2 ± 4.5 99.6 ± 2.8 97.9 ± 3.2 93.7 ± 3.0 97.9 ± 2.5 99.4 ± 7.6 98.4 ± 4.9 101.9 ± 3.7 89.3 ± 3.5 92.3 ± 2.9 97.8 ± 3.3

5.7 4.2 2.9 5.3 3.9 2.7 3.7 3.9 2.5 2.2 2.7 2.9 4.4 5.9 2.7 6.9 3.9 2.8 7.9 3.9 5.1 10.5 7.3 4.9 9.7 5.2 3.4 5.8 4.4 2.7 3.8 2.2 3.4 11.4 9.4 5.2

7.9 6.3 5.9 8.4 4.6 3.7 8.9 8.1 7.2 9.3 7.8 7.7 11.8 7.9 8.4 12.9 9.5 6.6 10.7 9.4 7.1 12.2 9.0 7.6 10.6 8.9 8.1 8.6 7.8 5.9 7.2 6.1 8.3 13.1 10.4 9.2

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lision energy and dwell time in the MS conditions, of which, the fragmenting provides transmission of the precursor ion into the MS; the collision energy can maximize the signal intensity for the product ions; and the dwell time can enhance the counting of the ions detected at each point in the scan mode and hence the accuracy, precision and sensitivity in analyzing complex analytes simultaneously. 3.4.3. Recovery, matrix effect and precision Method recovery presents the amount of analytes spiked in the matrix that can be recovered and quantified. The recoveries through the method were performed by spiking control samples (QC) with known amounts of standard solutions at three concentration levels for 12 flavonoids. In parallel, corresponding QC samples (no spiked flavonoids honey samples) were performed to distinguish the native amount of flavonoids in honey samples from the one spiked. The amount of endogenous analytes in QC samples was expressed as ‘‘D’’ for the sake of the convenient calculation. The recovery is given as the MRM response of honey samples that was spiked with a fixed concentration of standard solution of 12 flavonoids before extraction (B) relative to the response of the honey samples firstly subjected to the extraction procedure and then spiked with the same amount of 12 flavonoids (A); thus, the recovery is equal to [(B D/A D)  100]. Matrix effects are assessed by comparing the MS/MS responses of known concentrations of flavonoids standard solution in pure water (C) and response of the analytes spiked into a matrix sample that has been carried through the sample preparation process in advance with the same concentration (A). For matrix effect (ME, [((A D)/C)  100]), a negative value (100%) indicates an ionization enhancement effect. These procedures were repeated for different honey samples. The recoveries of the 12 flavonoids were between 74.9 and 98.3% and matrix effects ranged from 89.3 to 103.3% (Table 2). The repeatability (intra-day precision) and intermediate precision (inter-day precision) were evaluated according to the relative standard deviation of the analysis. The precision of the analytical method was determined in honey samples at spiked concentrations of flavonoids in quintuplicate on 3 successive days. The intra-day precisions of assay ranged from 2.2% to 11.4%, and interday precisions were less than 13.1% (Table 2). For the intra- and inter-day precisions, the results were suitable for three concentration levels (low, medium and high) and considered as adequate for other concentrations within a linear range, showing satisfactory performance during method validation. 3.4.4. Stability The stability studies evaluate the stability of flavonoids both in methanol as standard solution and mobile phase solution as the reconstituted solution 3.4.4.1. Standard solutions stability. The solutions were analyzed every 2 weeks and were compared with freshly prepared standards (10–80 lg mL 1) at storage temperatures of 4(±2.0) and 18(±2.0) °C in triplicate. The standard solutions of flavonoids were found to be stable within 8 months when refrigerated at 18 °C, Storage at 4 °C of flavonoids lead to a initial loss of about 5.1–7.4%, respectively over 4 months. 3.4.4.2. Processed samples stability. The samples reconstituted with mobile phase were found to be unstable for over 36 h at room temperature without photophobic condition. About 27.6–60.5% of them degraded at 2–8 °C for 2 weeks. The most likely reason is that flavonoids were easy to degrade under the acid condition containing 0.1% formic acid.

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3.4.5. Quantification of flavonoids in real honey samples The developed LC–MS/MS method was subsequently applied for the analysis of 12 flavonoids in honey samples. The calibration curves were used for the quantitative determination of these compounds. The contents of ferulic acid varied from 2.0 to 3.6 lg 100 g 1 in rape honey samples and 6.0–7.1 lg 100 g 1 in chaste honey samples. It was found that the level of ferulic acid in chaste honey samples was significantly higher than those in extracts of rape honey samples. The ferulic acid concentrations were compared with each other for chaste honey and chaste honey samples using Student’s t-test for 2-sample values and the level of significance was obtained. When probability value was less than 0.05 (chosen as the minimum level of significance), the null hypothesis was rejected. In the light of the analysis results, it may be concluded that the different analytical methods used afford significantly different results. OriginPro 8.5 program was employed for the statistical treatment of data. Morin (mean: 0.26 lg 100 g 1; RSD: 5.82%) and kaempferol (mean: 0.54 lg 100 g 1; RSD: 6.25%) were found in rape honey samples; while the chaste honey samples contained no 2 kinds of flavonoids. Moreover, other flavonoids studied were not found in rape honey and chaste honey samples. 3.5. Botanical classification of honey 3.5.1. Marker identification of chaste honey and rape honey In recent years, many studies have been carried out on the discrimination of honey samples collected from different floral origins by using reliable and specific marker compounds (Martos et al., 2000; Vilma & Petras, 2010). LC–MS/MS became a powerful qualitative and quantitative technique and was used to the analysis of trace components in biological matrix due to the enhanced selectivity and sensitivity of mass detection. In this work, attempts were made to identify the flavonoids as marker compounds to source the floral origin of chaste honey and rape honey. According to the developed HPLC–MS/MS method and pretreatment method, 98 of chaste honey samples and 89 of rape honey samples were analyzed to identify and quantify the flavonoids. The presence and absence or significant difference of flavonoids content was used to find the marker to authenticate the floral origin of chaste honey and rape honey. The results showed that there are no kaempferol and morin in chaste honey; however, the contents of kaempferol and morin exceeded 0.3 lg 100 g 1 in rape honey. Moreover, the content of ferulic acid was over 4.5 lg 100 g 1 in chaste honey but was below 3.0 lg 100 g 1 in rape honey. So, kaempferol, morin and ferulic acid as markers could be used to authenticate chaste honey and rape sample. No other flavonoids frequently reported by researchers were identified in those samples. 3.5.2. Fingerprinting analysis of chinese chaste honey and rape honey Chromatographic fingerprinting analysis was performed to check for the floral origin of honey samples. Chromatography has the advantage of separating the complicated multi-components and then visually presenting the chemical patterns of honey samples in the form of a chromatogram. In this study, a total of 187 raw honey samples were analyzed with the above extraction method and HPLC–DAD conditions. All the chromatograms were generated under the 270 nm and 360 nm wavelength, respectively. Typical chromatograms of chaste honey (Fig. 2A) and rape honey (Fig. 2C) were shown in fingerprinting profile for legible observation and comparison. Fingerprinting feature area 1 (FFA1) in Fig. 2A was sketched for identifying the chaste honey, but rape honey in Fig. 2C was deficient in FFA1 at 270 nm. Fingerprinting feature area 2 (FFA2) in Fig. 2C was outlined for identifying the rape honey, but FFA2 was not found in Fig. 2A of chaste honey at 270 nm. The obvious fingerprinting fea-

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Fig. 2. Typical chromatograms of chaste honey and rape honey at 270 nm and 360 nm ((A) chaste honey at 270 nm; (B) chaste honey at 360 nm; (C) rape honey at 270 nm; (D) rape honey at 360 nm).

ture area 3 (FFA3) was drawn in the Fig. 2B of chaste honey, but none in Fig. 2D of rape honey at 360 nm. Therefore, FFA1 and FFA2 at 270 nm and FFA3 at 360 nm could be used for the discrimination of chaste honey and rape honey. Correlation optimization warping (COW) was used to test on the honey samples to align chromatography peaks for adjusting the peak shape and eliminating retention time drift from run to run and various values were tried for user-specified number of sections (N) and slack parameter (s). In order to obtain the best warping, the slack is kept constant at 3 and 5, while the value for N is varied from 10 to 50 in steps of 10. In the final, the optimum values of N and s were found to be 40 and 5 respectively. The results showed the low variations for peak height, width at base and asymmetry factor after alignment. At the same time, the alignment of peak shape and retention time improve the mean correlation coefficients of honey samples from the same floral origin and score scatter plot in the principal component analysis. These descriptions have been added into the revised manuscript. Background subtraction is usually used in the analysis of mass spectrometry because subtracted spectra usually provide a more accurate representation of the mass spectrum of pure compounds available. Background subtraction should be performed uniformly on all samples analyzed contemporaneously. Every integrated peak

is background subtracted by subtracting successively the mass spectra at peak start and peak end. In this study, the background subtraction has been performed automatically by the Agilent Masshunter software as the inherent function for the analysis of qualification and quantification. The smoothing algorithm can be applied to improve the signal to noise ratio and remove small variations of data. In this study, the smoothing operation with seven data points was performed by setting the software parameters in the chromatogram presented in the data analysis software of Agilent. The wide use of analytical instrumentation and their inherent programming flexibility make signal smoothing function especially easy and simple nowadays. The chromatographic fingerprinting developed in this study was evaluated by their similarity resulting from the calculation on the correlative coefficients between entire chromatographic profiles, as well as to generate the mean chromatogram as a representative standard fingerprinting chromatogram. The mean chromatograms of chaste honey and rape honey samples presented their distinctive characters to some extent. The results showed that the mean correlation coefficients of chaste honey and rape honey samples were above 0.974 for all samples. The entire chromatograms of samples from the same floral origin were highly consistent with each other. With regards to the samples from different

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3.5.3. Multivariate analysis of chinese chaste honey and rape honey To explore the hidden information in the large amounts of data generated by using HPLC–DAD analysis and achieve the subtle differences between different kinds of samples have become the new focus of traceability research in recent years. Chemometric analysis is considered a very important feature of modern analytical approaches for the characterization of complex matrices by extracting information from multivariate chemical data using tools of statistics and mathematics (Luis, Rosa, & Károly, 2007). In this study, 4 chemometrics methods such as PCA, PLS, PLS-DA and SIMCA were performed by analyzing 98 chaste honeys and 89 rape honeys. Each sample was composed for as many variables as data points recorded during the laboratory running time. The range of retention time from 4 to 30 min turned out to be the most informative region based on the developed chromatographic fingerprints. The aligned and row profile scaled data set comprising 3900 variables and 187 samples constituted the starting point for the pattern recognition analysis. Fig. 3. The scatter plot for the first two principal components estimated with PCA.

origins, the correlation coefficient between mean chromatograms was nearly 0.481. It was obvious that different floral origins resulted in the low similarity between chaste honey and rape honey. So, it is effective to use the fingerprinting analysis to discriminate the different origins of the samples based on the above merits.

3.5.3.1. Unsupervised classification by principal component analysis (PCA). PCA was performed over the data matrix (187  3900, 187 samples and 3900 variables) using autoscaling. Fig. 3 shows the scatter plot for the first two principal components estimated with PCA, through which a visualization of the data structure in a reduced dimension is obtained. A natural separation of honey samples with two floral origins occurred in a 2-dimensional plot of

Fig. 4. Multivariate selection and analysis of PCA and PLS-DA ((A) consecutive PCs; (B) number of factors; (C) training set; (D) test set).

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the sample scores. The first principal component accounts for 43.87% of the total variance, while the second accounts for 20.96%, leading to a cumulative proportion of 64.83% of the total variance, which were considered significant. In fact, the number of PCs is equal to either the smallest number of samples or the number of variables from a mathematical point of view. In our situation there are 187 samples and 3900 variables. The smaller of those two numbers is 187 and, therefore, 187 PCs can be computed. According to PCA principles, PCk is more important than PC (k + 1) and less important than PC (k 1) for all consecutive PCs (Massart & Heyden, 2005). Thus, PC1 is more important than PC2 and further PC3 in this study. As presented in Fig. 4A, the cumulative variance contribution rate of the former 6 factors reached 83.74%, shows that these factors have already contained the main information on the samples. As of different floral origin, the clustering of chaste honey and rape honey were separately presented on the basis of noticeable boundary in the PC1-PC2 grouping. Therefore, the first two principal components through PCA were performed to distinguish the two kinds of honey in a simplified way by avoiding complex systematic errors generated from grouping analyses. 3.5.3.2. Partial least squares (PLS) and partial least squares-discrimination analysis (PLS-DA). PLS-DA was performed in order to sharpen the separation between groups of observations, by hopefully rotating PCA components such that a maximum separation among classes is obtained. As shown in Fig. 4B, the optimal number of factors (n = 10) was achieved due to the stabilizing curve of RootMean-Square Error of Cross-Validation (RMSECV). A 4-fold crossvalidation was performed to assess the classification error of the PLS-DA model. The original sample is randomly partitioned into 4 subsamples: 3 subsamples are used as training set, and 1 subsample is retained as the test set for testing the model. In this study, 75% of the samples were used as the training data set and the remaining 25% as the test data set. The resulting model was applied to the test data set to compute the predicted Y values (Westerhuis et al., 2008). The elements of y are usually coded as 1 for chaste honey and 2 for rape honey that provide information about the belonging of objects to two defined classes. Considering the difficulty to calibrate and predict the honey samples, it was necessary to discriminate the results between the initial values 1 or 2. We considered that all the values that are less than 1.49 conduce to a chaste honey sample and the ones that are greater than 1.51 to rape honey sample. So, the honey samples are classified into two groups by PLS-DA with a threshold of 1.5. Applying this threshold, all predicted values within a class are frequently less than 0.5 (or negative), when two classes are defined. The root mean square error (RMSE) is calculated for each interval and can tell us about the fit of the model developed to the data set used. Fig. 4C and D showed that the RMS of y-observed and y-predicted was 0.1333, which can be considered a well-constructed model. RMSECV was very close to root mean square error of prediction (RMSEP) (RMSECV = 0.1463 for training test, RMSEP = 0.1929 for test set), which means the loss in the accuracy was very small when the calibration models were applied to new samples. This also proved the first two components of PLS model were sufficient to clearly separate the 187 honey samples into two distinguished groups. 3.5.3.3. Soft independent modeling of class analogy (SIMCA). SIMCA as a class-modeling technique using PCA in the training set can effectively handle a multitude of classes with within-class variability and classifies unknown objects to other classes via cross validation. Predictive ability was evaluated by comparing the predicted values to the measured values of the test set and expressed as statistical measures including the coefficient of determination (R2), (RMSEP) and discrimination accuracy (Luis et al., 2007). The model

showed high R2 values of 0.8002 and 0.8088, and low RMSEP of 0.2339 and 0.1890 for calibration set and predictive set, respectively. The discrimination accuracy was obtained for calibration set (94.53%) and predictive set (96.43%), which suggest highly predictive and effective models to classify the honey samples in this study. 4. Conclusion In this study, the combination of LC DAD MS/MS and chemometric analyses was proposed in order to achieve flavonoid markers, fingerprinting profiles and classification models for identifying the floral origin of chaste honey and rape honey. HPLC MS/MS coupled to the pretreatment of honey samples was performed to authenticate chaste honey and rape honey samples via the identification and quantification of their flavanoids, kaempferol, morin and ferulic acid, as marker compounds to distinguish their floral origins. The HPLC fingerprinting of chaste honey and rape honey were successfully established at different wavelengths for the first time. A total of 187 honey samples were identified and distinguished by the chromatographic fingerprinting in combination with similarity analysis. The chromatograms of samples from the same floral origin presented high correlation and different floral origins resulted in the low correlation between the two honeys. Furthermore, four multivariate methodologies including PCA, PLS, PLS-DA and SIMICA were employed by analyzing the collected data in the form of two-dimensional matrices, which showed a satisfactory separation of the two types of honeys. The PLS analysis with low RMSE was performed to separate the 187 honey samples. PLS-DA and SIMCA were then applied to construct the training test and test set for the classification of chaste and rape honeys. They could be expected to produce sufficiently satisfactory and acceptable merits. Conclusively, the application of HPLC DAD MS/MS to honey marker compounds analyses for the botanical authentication classification seems to be a very promising approach. Further studies are required to focus on the finding of other markers which has been showed in the fingerprinting, but not elucidated in this study. Moreover it could be interesting to extend the study to broader floral origins of honey samples and include the studies of their geographical origins. Acknowledgements This project was financially supported by the special fund (NYCYTX-43) from Apicultural Industry Technology System Construction of Modern Agriculture, China and National Natural Science Foundation of China (Grant No. 31201859). References Aljadi, A. M., & Kamaruddin, M. Y. (2004). Evaluation of the phenolic contents and antioxidant capacities of two Malaysian floral honeys. Food Chemistry, 85, 513–518. Almeida, A. M., Castel-Branco, M. M., & Falcão, A. C. (2002). Linear regression for calibration lines revisited: Weighting schemes for bioanalytical methods. Journal of Chromatography B Analytical Technologies in the Biomedical and Life Sciences, 774, 215–222. Cuyckens, F., & Claeys, M. (2004). Mass spectrometry in the structural analysis of flavonoids. Journal of Mass Spectrometry, 39, 11–15. Di, W., Shuijuan, F., Xiaojing, C., Haiqing, Y., & Yong, H. (2002). Three-way principal component analysis applied to food analysis: an example. Analytica Chimica Acta, 462, 133–148. Edenharder, R., Keller, G., Platt, K. L., & Unger, K. K. (2001). Isolation and characterization of structurally novel antimutagenic flavonoids from spinach (Spinaciaoleracea). Journal of Agricultural and Food Chemistry, 49, 2767–2773. Elke, A. (1998). A review of the analytical methods to determine the geographical and botanical origin of honey. Food Chemistry, 63, 549–562.

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