Quantitative determination of carboxylic acids, amino acids, carbohydrates, ethanol and hydroxymethylfurfural in honey by 1H NMR

Accepted Manuscript Quantitative determination of carboxylic acids, amino acids, carbohydrates, ethanol and hydroxymethylfurfural in honey by 1H NMR Gloria del Campo, Juan Zuriarrain, Andoni Zuriarrain, Iñaki Berregi PII: DOI: Reference:

S0308-8146(15)30035-2 http://dx.doi.org/10.1016/j.foodchem.2015.10.036 FOCH 18231

To appear in:

Food Chemistry

Received Date: Revised Date: Accepted Date:

24 April 2015 24 September 2015 10 October 2015

Please cite this article as: del Campo, G., Zuriarrain, J., Zuriarrain, A., Berregi, I., Quantitative determination of carboxylic acids, amino acids, carbohydrates, ethanol and hydroxymethylfurfural in honey by 1H NMR, Food Chemistry (2015), doi: http://dx.doi.org/10.1016/j.foodchem.2015.10.036

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Quantitative determination of carboxylic acids, amino acids, carbohydrates, ethanol and hydroxymethylfurfural in honey by 1

H NMR

Gloria del Campo, Juan Zuriarrain, Andoni Zuriarrain, Iñaki Berregi* University of the Basque Country EHU/UPV, Faculty of Chemistry Manuel Lardizabal 3, 20018 Donostia-San Sebastián, Gipuzkoa, Spain

ABSTRACT A method using 1H NMR spectroscopy has been developed to quantify simultaneously thirteen analytes in honeys without previous separation or pre-concentration steps. The method has been successfully applied to determine carboxylic acids (acetic, formic, lactic, malic and succinic acids), amino acids (alanine, phenylalanine, proline and tyrosine), carbohydrates (α- and β-glucose and fructose), ethanol and hydroxymethylfurfural in eucalyptus,

heather,

lavender,

orange

blossom,

thyme

and

rosemary

honeys.

Quantification was performed by using the area of the signal of each analyte in the honey spectra, together with external standards. The regression analysis of the signal area against concentration plots, used for the calibration of each analyte, indicates a good linearity over the concentration ranges found in honeys, with correlation coefficients higher than 0.985 for the thirteen quantified analytes. The recovery studies give values over the 93.7–105.4% range with relative standard deviations lower than 7.4%. Good precision, with relative standard deviations over the range of 0.78–5.21% is obtained.

Keywords: 1H NMR; Honey; Metabolites; Quantitative analysis

*

Corresponding author. Tel.: +34 943 018210 Fax: +34 943 015270 E-mail address: [email protected]

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1. Introduction

Honey is a natural syrup which sensory properties (color, flavor and texture) are a complex function of physicochemical parameters, mainly determined by the botanic and geographic origins. In essence, it is a concentrated aqueous solution of glucose (31%) and fructose (39%), but other species account for, on average, 13% (w/w) of the sample. Honey contains free amino acids at a level of 1% (w/w), pollen being one of their sources. Proline, which might originate from bees, is the prevalent amino acid and makes up 50–85% of the amino acid fraction (White, 1975). Organic acids are present in honey at low concentrations (<0.5%) and they are related to color, flavor and physicalchemical properties of the honey, such as pH, acidity, and electrical conductivity. Organic acids, chelate metals and so forth can synergistically enhance the antioxidant action of phenolic compounds (Gheldof, Wang & Engeseth, 2002). Moreover, acetic acid and ethanol can be used as fermentation indicators and formic acid as an indicator for the treatment of Varroa infestation (Calderone, 2000). Traditionally, the analysis of minor organic compounds of the honey is carried out by means of chromatographic methods. The amino acid composition of honey has received considerable attention and has proven to be a good indicator when characterizing its geographic or botanical origin. In amino acids determination, mainly chromatographic techniques were applied. The results obtained using gas chromatography (GC) showed that differences between geographic origins (Gilbert, Shepherd, Wallwork & Harris, 1981) or botanical varieties existed (Pirini, Conte, Francioso & Lercker, 1992). This technique is limited by the necessity of derivatizing the compounds. Determination of amino acids in honey by high-performance liquid chromatography (HPLC) also shows a problem caused by the high sugar concentration and the presence of other chemical species which would poison HPLC columns (Gilbert et al., 1981; Pirini et al., 1992; Nozal, Bernal, Toribio, Diego & Ruiz, 2004; Hermosín, Chicón & Cabezudo, 2003). Analytical methods for determination of organic acids in honey have been reviewed (Mato, Huidobro, SimalLozano & Sancho, 2006a) and enzymatic, chromatographic and electrophoretic methods

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have been used for quantifying purposes. The aldehyde 5-hydroxymethylfurfural (HMF) is considered an important quality parameter for honey, because elevated concentrations provide an indication of overheating, storage in poor conditions or aging of the honey. The International Honey Commission recommends three methods for the determination of HMF: two spectrophotometric methods and a HPLC method. These three methods have been compared and the HPLC method seems to be the most appropriate because the presence of compounds which interfere with the UV methods is overcome (Zappalà, Fallico, Arena & Verzera, 2005). GC and HPLC procedures have usually been applied for analyzing carbohydrates in honey (Mateo, Bosch, Pastor & Jiménez, 1987; Swallow & Low, 1990; Nozal, Bernal, Toribio, Álamo, Diego & Tapia, 2005), where HPLC methods are the most widely used given that derivatization is not normally necessary. Quantitative methods in nuclear magnetic resonance (NMR) spectroscopy have been successfully used. Since NMR spectra contains the resonances of all components with concentrations higher than the detection threshold (around 5-10 µM), they can be advantageously utilized for quantitative analysis if certain technical and instrumental parameters are taken into account; furthermore, in order to obtain accurate quantitative results the signals could be free of overlapping and correctly integrated (Saito et al., 2004; Pauli, Jaki & Lankin, 2005; Pauli, Jaki & Lankin, 2007). As has been proved in various natural products, 1H NMR spectroscopy gives a good overall picture of all types of organic compounds in the sample. Most published methods use internal standards (Pauli et al., 2005), although there are applications where external standards have been employed (Burton, Quilliam & Walter, 2005; López-Rituerto, Cabredo, López, Avenoza, Busto & Peregrina, 2009). In recent years, the use of high-resolution NMR techniques in the study of the honey has attracted the interest of a number of groups and, as result, 1-dimensional and 2dimensional NMR experiments have been explored in order to characterize and to classify a great amount of honeys. High-resolution NMR spectroscopy coupled with multivariate data analysis has been applied to analyze metabolite profiles, to detect variations in the composition of honeys, to find biomarkers and to clearly explain the molecular structure

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of the most successful compounds that differentiate honeys (Lolli, Bertelli, Plessi, Sabatini & Restani, 2008; Consonni & Cagliani, 2008; Beretta, Caneva, Regazzoni, Bakhtyari & Facino, 2008; Donarski, Jones & Charlton, 2008; Donarski, Jones, Harrison, Driffield & Charlton, 2010). Quantitative analysis has also been attempted. Sandusky and Raftery (2005) used selective TOCSY NMR experiments to obtain relative concentrations for five amino acids and ethanol in honey. Its study pointed out the problems showed for this type of sample; one was caused by the very large difference in concentrations between the major components and minor components, and another due to the unfortunate effects of the sugars on the viscosity and NMR relaxation properties. At higher honey concentrations, significant reduction in sensitivity of some amino acids was observed, this being attributed to the higher viscosity of the honey samples and consequent increase in the relaxation rate during the TOCSY spin lock used. The objective of the present study was to develop an experimental procedure based on 1H NMR spectroscopy to quantified metabolites in honey which would allow for the best resolution of the signals, an accurate quantification and a reduced time of analysis. This method allows for the simultaneous quantification of carboxylic acids (acetic, formic, lactic, malic and succinic acids), amino acids (alanine, phenylalanine, proline and tyrosine), sugars (fructose and α- and β-glucose), ethanol and 5-hydroxymethylfurfural, in honey; in total, thirteen analytes.

2. Materials and methods

2.1. Reagents The chemical reagents, all of analytical grade, were purchased from Sigma-Aldrich, with the exception of 1,3,5-benzenetricarboxilic acid, formic acid and hydrochloric acid, which were purchased from Merck, and ethanol, which was purchased from Panreac. All solutions were prepared with double-distilled water (from this point on, “water”).

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2.2. Honey samples The honeys analyzed were of Spanish origin and they were purchased from local markets. The total sugar percentage of each honey was previously measured by refractometry as sucrose w/w percentage. Next, 20.0 g of honey were weighed and mixed with 15 mL of water. The pH of the resulting solution was adjusted to 1.0 by adding HCl 1.2 M from an automatic titrator, provided by a combined pH electrode. The total sugar percentage was then reduced to a final value of 40.0%, w/w, by dilution with acidified water at pH 1.0. The total sugar percentage of pure honeys is in average ~80%, w/w (Ball, 2007; Bogdanov, Jurendic, Sieber & Gallmann, 2008), but is different from one honey to another, so each honey needed a different dilution. Finally, the solution obtained was filtered through 0.45 µm nylon membrane (Cameo, Scharlab, Barcelona, Spain), and its

1

H NMR spectrum was recorded in triplicate by the method explained

further on. The concentration of the thirteen analytes was calculated from the 1H NMR spectra by using the absolute area of their respective signals and the calibration equations.

2.3. Standard solutions Nine different standard solutions containing the principal compounds of the honey were prepared to approach natural matrices as much as possible. The chosen concentrations ranged from the values experimentally found for three types of representative honeys: eucalyptus, rosemary and thyme (Table 1) (Hermosín et al., 2003; Nozal, Bernal, Diego, Gómez & Higes, 2003; Nozal et al., 2004; Zappalà et al., 2005; de la Fuente, Ruiz-Matute, Valencia-Barrera, Sanz & Martínez-Castro, 2011). These standard solutions contained 29 organic compounds, which signals appear in the 1

H NMR spectra, and potassium, added to adjust the ionic strength of the samples to a

constant value of 1.0 g per kg honey, minimum observed in honeys (Bogdanov et al. 2008). The pH was adjusted to 1.0 and the total sugar percentage to 40%, w/w, applying the procedure described for honey samples. The spectra of the standard solutions were recorded and the selected peak areas for each analyte were measured by electronic

5

integration of expanded regions around selected resonances. The integrals taken from the spectra were not subsequently normalized and the areas of the corresponding signals were calculated as absolute integrals. Calibration graphs were obtained by plotting the selected peak areas for each analyte against its concentration.

2.4. 1H NMR spectroscopy 600 µL of the sample (standard solution or pre-treated honey sample) were placed into a 5 mm outer diameter NMR tube, with 100 µL of a solution containing 70% (v/v) D2O, 10.0 g L–1 of sodium 3-trimethylsilyl-3,3,2,2-tetradeuteriopropionate (TSP) and 1.0 g L–1 of 1,3,5-benzenetricarboxilic acid (BTC). The final concentrations were 10% D2O, 1.43 g L–1 of TSP and 0.14 g L–1 of BTC. BTC was added as an internal standard which supplied a reference peak for the phenolic region (5.8–9.5 ppm) (Berregi, del Campo, Caracena & Miranda, 2007). One-dimensional spectra was recorded on a Bruker Avance500 MHz spectrometer (Karlsruhe, Germany). To obtain the spectra of the samples, 128 scans of 64 K data points were acquired at 30ºC using a spectral width of 8012 Hz (16 ppm), acquisition time of 4.0 s, recycle delay of 2.0 s and a 90º flip angle, requiring about 13 min per sample. Water suppression was achieved by using the one-dimensional nuclear Overhauser effect spectroscopy (1D NOESY) pulse sequence, incorporating presaturation during the relaxation delay and mixing time (150 ms) (McKay, 2011) and the pre-saturation power used was the minimum needed to effect complete suppression of the water peak. The receiver gain setting for a given pulse sequence was adjusted manually in preliminary experiments and it was held constant for all the spectra. The Free Induction Decay signals were processed before Fourier transformation using Bruker software, TOPSPIN 1.3. A broad-line of 1.0 Hz was applied to obtain a good sensitivity (i.e. signal/noise) and sufficient resolution (line width). The spectra were referenced to the TSP singlet peak at 0.0 ppm. To attain reliable results the phasing and the baseline correction over the entire spectral range are critical, so these processes were carried out manually, as we did in a previous work (del Campo, Berregi, Caracena & Santos, 2006).

6

In all instances, the baseline was additionally corrected over the integrated regions. Integral values were entered into Microsoft Excel spreadsheets for further processing.

2.5. Method validation The proposed method was validated in terms of linearity, limits of detection and quantification, analytical recovery as well as precision and accuracy. The linearity was tested by regression analysis of the absolute integrals of the peaks of the analytes, with respect to their corresponding concentrations in the nine standard solutions. Recovery was tested through the standard addition procedure on two different types of honeys: eucalyptus and rosemary. The procedure used here was practically the same as that described in section 2.2. for honey samples. For each honey, 10.0 g were weighed and mixed with 5 mL of standard solution “9” and 10 mL of water, another 10.0 g with 10 mL of the standard and 5 mL of water, and another 10.0 g with 15 mL of the standard. From this point on, the method was identical as section 2.2. For each addition, one spectral acquisition was made with three repetitions of the data processing per spectrum, in total, 9 measurements. By using the selected peak areas for each analyte and the calibration graphs, the content of the thirteen analytes was calculated in the original and in the enriched honeys, as well as the recovery and its absolute (SD) and relative (RSD, %) standard deviation. Accuracy

and

precision

of

the

method

were

determined

through

repeatable

measurements, by analyzing three different preparations of the same standard solution, with three repetitions of the data processing per spectrum (in total, 9 measurements). By using the selected peak areas for each analyte and the calibration graphs, the content of the thirteen analytes was measured. The accuracy was determined as the bias for each analyte in the standard solution, which was calculated as measured content — nominal content x 100/nominal content. The precision was determined as the RSD of each analyte content.

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3. Results and discussion

3.1. 1H NMR spectra of honey samples The assignment of the signals identified in our honeys is reported in Table 2. All of these assignments were confirmed by recording the NMR spectra of the individual compounds and by comparison with chemical shift values in the literature. Moreover, we recorded the spectra of the nine standard solutions containing sugars, amino acids, acids and alcohols in proportions close to their natural concentrations in honey (Table 1). The detected peaks and the variation of their areas according to variation of their concentrations allowed the confirmation of previously assigned peaks. Fig. 1 shows the 1H NMR spectra of a sample of lavender honey, divided in three regions. The other honeys present qualitatively similar spectra. Regarding the low chemical shift region in Fig. 1A (0.8–3.1 ppm), the major signals correspond to amino acid proline according to its relatively high concentration in honey. Multiplets at 2.05 and 2.17 ppm were assigned to H2 of proline, whereas the multiplet at 2.43 ppm was assigned to H3. Other signals of the pyrrolidine moiety of proline are also clearly seen in the spectrum except H5 which overlapped with other signals in the central region (3.1– 6.0 ppm). The spectra of all honeys showed the triplet corresponding to the methyl group of ethanol which is well defined and free of overlapping. Also signals from lactic, acetic, succinic, malic and citric acids can be observed. Quantification of malic acid has to be carried out carefully. Its double doublet has been divided in two parts, one at 2.86 nm and the other at 2.92 nm, as given in table 2. Although both parts belong to the same signal, only that at 2.92 nm has been used to quantify malic acid, because the other overlaps with the signal of citric acid at 2.84 nm. This is the strategy we used in a previous work (del Campo et al., 2006). Signals from the amino acids isoleucine, leucine, valine, threonine, lysine, alanine and arginine were observed, but mainly depending on the type of honey they showed a low intensity or a partial overlapping and only proline, alanine, isoleucine, leucine and valine were tested for quantification. Two signals from unidentified compounds were observed in this region at 1.14 and 1.82 ppm, both signals

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also appear in the 1H NMR spectra of honeys showed by Consonni and Cagliani (2008) and Sandusky and Raftery (2005), although there is no information on the assignment of these signals. Fig. 1B shows the central region (3.1–6.0 ppm). The strongest signals in this region arise from the two main sugars, fructose and glucose, overlapped with signals of amino acids, gluconic and malic acids and other minor sugars (maltose, turanose and sucrose). Several isomers of glucose and fructose are present in both, aqueous solutions and honeys. In the standard NMR spectrum of a dilute solution of glucose, only the anomeric forms of β-glucopyranose (about 63.0% of glucose) and α-glucopyranose (about 37.0% of glucose) were observed, while for fructose, α and β-fructopyranose (about 2.4 and 69.8% of fructose, respectively) and α and β-fructofuranose (about 4.8 and 23.0% of fructose, respectively) were observed and quantified in artificial mixtures of mono-, diand trisaccharides (Mazzoni, Bradesi, Tomi & Casanova, 1997). For glucose quantification we selected two signals corresponding to

β-glucopyranose at 3.23 ppm and α-

glucopyranose at 5.21 ppm. For fructose determination, the signals at 4.00 ppm (H5 and H6 of β-fructopyranose + H4 of α-fructofuranose) and 4.10 ppm (H3 of α and H3 + H4 of β-fructofuranose) were integrated. The assignment and quantification of hydroxymethylfurfural, tyrosine, phenylalanine and formic acid were performed based on signals present in the high chemical shift region of the spectra, from 6.0 to 9.5 ppm (Fig. 1C). Moreover, the spectra of some honeys showed peaks corresponding to histidine. All signals were well defined and without overlapping, the only limitation for their quantification was the low signal/noise ratio for 5-hydroxymethylfurfural, and histidine in some honeys.

3.2. Parameters affecting the accuracy, resolution and sensitivity of the 1H NMR spectra 3.2.1. Instrumental parameters. To obtain a high accuracy in quantitative analysis the elapsed time between the successive acquisitions of the spectra must be five times the longitudinal relaxation time (T1) of the longest among the signals of interest for a flip angle of 90º, therefore the length of the recycle delay is frequently selected from the

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relaxation properties of the individual nuclei in the sample. The recycle time of 2.0 s with an acquisition time of 4.0 s, applied in this work, was a compromise between a reduced analysis time (about 13 min per spectrum) and the attainment of spectra with good resolution and adequate sensitivity. These recycle and acquisition times only guaranteed the total relaxation of those protons which have a T1 value lower than 1.2 s, but the T1 values measured for a part of the metabolites were higher than this value (del Campo et al., 2006; Caligiani, Acquotti, Palla & Bocchi, 2007; del Campo, Berregi, Caracena & Zuriarrain, 2010) meaning that, for some metabolites, the magnetization is not at equilibrium during the spectra acquisition. However, the differences produced from variations in the recovery level of the magnetization of the same signals between different samples is generally small, since all the parameters for acquisition and processing of the spectra were identical for honey samples and for standard solutions throughout the analysis (Nord, Vaag & Duus, 2004). After dilution the honey samples contained 60% water and suppression of the strong solvent signal is necessary in order to obtain a high ratio signal to noise for the minor peaks. Three techniques were examined in order to suppress water signal on the 1H NMR spectrum: WATERGATE (water suppression by gradient-tailored excitation) (Nord et al., 2004), 1D NOESY and PURGE (pre-saturation utilizing relaxation gradients and echoes) (Liu, Mao, He, Huang, Nicholson & Lindon, 1998; Simpson & Brown, 2005). The spectra obtained showed that applying the WATERGATE technique, the water signal was fully suppressed but the intensity of the signals at 4.65 ppm (β-glucose C1) and 5.22 ppm (αglucose C1) decreased. The PURGE sequence produced a strong attenuation of the water signal while the intensity of the near signals was no affected; however the phasing and the baseline correction in the spectra were not satisfactorily carried out. The 1D NOESY sequence efficiently decreased the water signal without reducing the intensity of the remaining signals and moreover, the phasing and the baseline correction can be easily performed and so, this sequence was selected to suppress the water signal. 3.2.2. Chemical parameters. The influence of both, pH and dilution of the honey samples, on the peak areas was examined. The pH of the samples need to be controlled

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because the chemical shifts of some compounds, often containing acidic or basic groups, depend on its value. To know the effect of the pH variation on the chemical shifts of the honey spectra, three samples were diluted up to a total sugar percentage of 30%, w/w, and its pH was adjusted at 1.0, 2.0 and 3.0. The most important spectral changes were observed for the three multiplet signals corresponding to proline (pK1=1.952), since its protonation state changes with the pH changes between 1.0 and 2.0. The chemical shift corresponding to the signal from acetic acid (pK=4.757) was maintained at 1.09 ppm, with pHs 1.0–3.0. At pHs 3.0 and 2.0 the resonance from acetic acid is partially overlapped with the multiplet signal β’CH from proline, while at pH 1.0 the three multiplet signals from proline and the signal from acetic present a good resolution. Moreover, considering that the microbiologic stability of the honey samples is increased to a low pH, the samples for obtaining spectra were adjusted at pH 1.0. To reduce the viscosity of the honey and hence enabling its manipulation, the sample needs to be diluted. From preliminary

1

H NMR studies, it was observed that the

quantitative results depended on the total sugar percentage in honey due to the effect of this on the sample viscosity, which influences the relaxation time and therefore the peak areas of some compounds. In order to find the largest concentration of the sample that offers correct quantitative results,

1

H NMR spectra of a honey sample (total sugar

percentage of 72%, w/w) was acquired at different dilutions varying the total sugar percentage from 10 to 40%, w/w, where 40% was the highest total sugar percentage that permitted an adequate manipulation of the samples. The results obtained showed that, for the greater part of the signals, the peak areas increased with increasing total sugar percentage; consequently the minimum dilution (up to total sugar percentage of 40%, w/w) was selected for sample preparation.

3.3 Quantification Normalization of the area of the interest peaks by dividing their areas by the area of an internal standard is frequently performed for quantitative treatment of the spectral data. At first we used TSP as an internal standard for low chemical shift and central

11

regions and BTC for high chemical shift regions, as we did in previous works (Berregi et al., 2007; del Campo et al., 2006), and the ratios between analyte area and internal standard area for all spectra were calculated. However, the calibration graphs obtained by plotting these ratios against concentrations showed a poor lineal fit for the greater part of the analytes. Alternatively, we considered absolute areas, and the calibration curves were constructed by linear regression of the absolute integrals of the analytes with respect to their corresponding concentrations in the nine standard solutions. Linear responses were observed over the ranges found in the honeys for all the analytes, so, absolute areas were used. In Table 3, the δ ranges for integration and the calibration parameters are given together with the detection and quantification limits. As can be seen, the values obtained for determination coefficients were higher than 0.94, surpassing by 0.99 for the greater part of the metabolites, which indicates a good linear response within the concentration range studied. Calibration parameters were calculated with respect to standard solutions analyzed, considering mg (for minor components) or grams (for sugars) in 500 ml of solution, while ranges of linearity and limits of detection and quantification are referred to honey samples and expressed per kg of honey. The limits of detection (LOD) and quantification (LOQ) were calculated from the intercept plus three and ten times its standard deviation, respectively. As can be read in Mato’s review (2006a), the composition of organic acids in honey presents an important variability. The acid content in different Spanish honeys (SuárezLuque, Mato, Huidobro, Simal-Lozano & Sancho, 2002; Mato, Huidobro, Simal-Lozano & Sancho, 2006b) varied over the ranges (mg kg–1 honey): 12–759 (mean: 88) for succinic acid, 13–434 (mean: 110) for malic acid, 20–394 (mean: 99) for citric acid, <9.2–632 (mean: 209) for lactic acid, <34–336 (mean: 146) for acetic and 46–908 (mean: 266) for formic acid. As can be seen, by comparing the cited contents with the LOQs showed in Table 3, the proposed NMR method is adequate when quantifying acetic, formic, lactic, malic and succinic acids, but not for citric acid. The poor regression data and high LOQ for citric acid can be explained by the fact that the rather weak signal used for integration is near to high signals from sugars, these producing baseline artefacts.

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Analysis of Spanish honeys showed that the amino acid content of honeys from different botanical origins varied throughout a wide range (Nozal et al., 2004; Hermosín et al., 2003). For the greater part of the honeys, the contents of isoleucine, leucine and valine were lower than the respective LOQs in Table 3, therefore, in general, these amino acids cannot be quantified by the proposed NMR method. For proline quantification the best results can be expected by applying the regression equation for proline 1, owing to low LOQ attained with this equation (70 mg/kg honey), jointly with its high sensitivity and low intercept. The application of NMR spectroscopy to quantify fructose and glucose shows that its advantage with respect to other more sensitive techniques (HPLC or GC) lies in the possibility of identifying sugar isoforms, otherwise not detectable (Pauli et al., 2005). The sensitivity in quantifying a metabolite, measured as the slope of the calibration line, depends on the number of protons contributing to the signal. For total glucose two calibration equations, indicated in Table 3 as glucose 1 (signal from β-glucopyranose) and glucose 2 (signal from α-glucopyranose) were constructed, and the ratio between the slopes corresponding to both calibration equations was 1.727 which indicates relative proportions of 63.3% of β-glucopyranose and 36.7% of α-glucopyranose, in accord with literature data for aqueous solutions of glucose (Mazzoni et al., 1997; Horton & Walaszek, 1982). From these good results, new calibrations were calculated for β- and αglucose considering their relative proportions in the standard glucose solutions. As can be seen in Table 3, the slopes of the calibration lines obtained for both anomeric forms are not significantly different (p<0.01), as expected, since the calibration equations were obtained from the peaks at 3.25 ppm (β-glucose) and 5.22 ppm (α-glucose) and both peaks are assigned to an unique proton. The ratio of slopes corresponding to fructose 1 and fructose 2 is 2.779, which indicates relative proportions of 73.5% for fructose 1 and 26.5% for fructose 2. Considering that the areas for fructose 1 and 2 correspond each to the sum of two isomeric forms, the proportions obtained are in good agreement with literature data (Mazzoni et al., 1997; Horton et al., 1982), the differences being lower

13

that 1.5%. The limit of quantification obtained for sucrose is higher than the content found in the honeys (Ball, 2007), therefore it cannot be quantified. Codex Alimentarius (Alinorm 01/25 2000) and the European Union (EU Directive 110/2001) established the maximum hydroxymethylfurfural content permitted in honey as 40 mg kg–1, with the exceptions of 80 mg kg–1 for honey from countries of tropical temperatures, and 15 mg kg–1 for honey with a low enzymatic content. The three calibration equations obtained for hydroxymethylfurfural quantification show similar slopes, but the lower limit of quantification (14 mg kg–1 honey) is furnished from a singlet signal of hydroxymethylfurfural at 9.45 ppm (Table 3, HMF 3), corresponding to the aldehydic proton being lower than the lowest content permitted. The recovery study was carried out on two samples belonging to eucalyptus and rosemary honeys. In this study, all the calibration equations indicated in Table 3 were applied, but the better results for recovery of metabolites having more than one calibration equation, were obtained using equations from data for malic acid 1, proline 1, tyrosine 1, fructose 1 and HMF 3. The contents of β- and α-glucose in the enriched honey samples were calculated from the contents measured in the honey and the added glucose considering a ratio β-glucose/α-glucose equal to 1.727. Table 4 summarizes the recovery results for the compounds analyzed in both honey samples using the cited equations, as mean, standard deviation (SD) and relative standard deviation (RSD). Satisfactory results were obtained where recovery was close to 100% and RSDs lower than 7.3%. To evaluate the accuracy of the proposed method, this was applied to determine the 13 compounds in a standard solution containing the analytes in concentrations close to those found in honey and maintaining the remainder compounds in the mean concentrations indicated in Table 1, with the results showed in Table 5. The obtained RSDs ranged from 0.78% for hydroxymethylfurfural to 5.21% for malic acid. The biases varied from 0.58% for succinic acid to 4.97% for fructose. From these results, it appears that a correct evaluation of the concentrations was obtained and, finally, we applied the 1

H NMR method to honey samples.

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Nine honey samples of different botanical origins (eucalyptus, heather, lavender, orange blossom, thyme and rosemary) were analyzed applying the proposed

1

H NMR

method. Additionally, the ratios fructose/glucose were calculated. The results are summarized in Table 6. As can be seen in this table the composition of the honeys’ organic acids and amino-acids shows an important variability but the values found are within the range of values previously described by using enzymatic and chromatographic methods (Gilbert et al., 1981; Pirini et al., 1992; Liu et al., 1998; Simpson et al., 2005; Suárez-Luque et al., 2002). The fructose/glucose ratio is occasionally used to ascertain honey authenticity. In the samples analyzed this ratio varied from 1.17–1.36 and was quite similar to those proposed for natural honeys (Nozal et al., 2005; Cotte, Casabianca, Chardon, Lheritier & Grenier-Loustalot, 2004). To sum up, the present study provides a new method based on 1H NMR spectroscopy for the determination of 13 metabolites in honeys, including acids, amino acids, sugars, ethanol and hydroxymethylfurfural. In addition, the two anomeric forms (α− and βglucopyranose) of glucose can be quantified. The method offers advantages in terms of speed, simplicity of sample preparation, minimal amount of solvents, precision and accuracy, and has been successfully applied in eucalyptus, heather, lavender, orange blossom, thyme and rosemary honeys.

Acknowledgments We are grateful to SGIKER, Gipuzkoa Unit (UPV/EHU) for NMR facilities.

References

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del Campo, G., Berregi, I., Caracena, R. & Zuriarrain, J. (2010). Quantitative determination of caffeine, formic acid, trigonelline and 5-(hydroxymethyl)furfural in soluble coffees by 1H NMR spectrometry. Talanta, 81, 367–371. Donarski, J.A., Jones, S.A. & Charlton, A.J. (2008). Application of cryoprobe 1H nuclear magnetic resonance spectroscopy and multivariate analysis for the verification of Corsican honey. Journal of Agricultural and Food Chemistry, 56, 5451–5456. Donarski, J.A., Jones, S.A., Harrison, M., Driffield, M. & Charlton, A.J. (2010). Identification of botanical biomarkers found in Corsican honey. Food Chemistry, 118, 987–994. Gheldof, N., Wang, X-H. & Engeseth, N.J (2002). Identification and quantification of antioxidant components of honeys from various floral sources. Journal of Agricultural and Food Chemistry, 50, 5870–5877. Gilbert, J., Shepherd, M.J., Wallwork, M.A. & Harris, R.G. (1981). Determination of the geographical origin of honeys by multivariate analysis of gas chromatographic data on their free amino acid content. Journal of Apicultural Research, 20, 125–135. Hermosín, I., Chicón, R.M. & Cabezudo, M.D. (2003). Free amino acid composition and botanical origin of honey. Food Chemistry, 83, 263–268. Horton, D. & Walaszek, Z. (1982). Tautomeric equilibria of some sugars by partially relaxed,

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H NMR in Rioja red wine

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Pauli, G.F., Jaki, B.U. & Lankin, D.C. (2005). Quantitative 1H NMR: Development and potential of a method for natural products analysis. Journal of Natural Products, 68, 133–149. Pauli, G.F., Jaki, B.U. & Lankin, D.C. (2007). A routine experimental protocol for qHNMR illustrated with taxol. Journal of Natural Products, 70, 589–595. Pirini, A., Conte, L.S., Francioso, O. & Lercker, G. (1992). Capillary gas chromatographic determination of free amino acids in honey as a means of discrimination between different botanical sources. Journal of High Resolution Chromatography, 15, 165– 170. Saito, T., Nakaie, S., Kinoshita, M., Ihara, T., Kinugasa, S., Nomura, A. & Maeda, T. (2004). Practical guide for accurate quantitative solution state NMR analysis. Metrologia, 41, 213–218. Sandusky, P. & Raftery, D. (2005). Use of selective TOCSY NMR experiments for quantifying minor components in complex mixtures: application to the metabonomics of amino acids in honey. Analytical Chemistry, 77, 2455–2463. Simpson, A.J. & Brown, S.A. (2005). Purge NMR: Effective and easy solvent suppression. Journal of Magnetic Resonance, 175, 340–346. Suárez-Luque, S., Mato, I., Huidobro, J.F., Simal-Lozano, J. & Sancho, M.T. (2002). Rapid determination of minority organic acids in honey by high-performance liquid chromatography. Journal of Chromatography A, 955, 207–214. Swallow, K.W. & Low, N.H. (1990). Analysis and quantitation of the carbohydrates in honey using high-performance liquid chromatography. Journal of Agricultural and Food Chemistry, 38, 1828–1832. White, J.W. (1975). Composition of honey. In E. Crane (Ed.), Honey. A comprehensive survey (pp. 157-206). New York: Crane, Russak & Company. Zappalà, M., Fallico, B., Arena, E. & Verzera, A. (2005). Methods for the determination of HMF in honey: a comparison. Food Control, 16, 273–277.

19

20

Fig. 1. Representative 1H NMR spectrum of a lavender honey at pH 1.0 and diluted up to 40% total sugar percentage. (A) Low chemical shift region from 1.8 to 3.1 ppm, (B) central region from 3.1 to 6.0 ppm and (C) high chemical shift region from 6.0 to 9.5 ppm. The signals used for the determination of the thirteen analytes are shown. The ordinate scale of A and C regions is expanded by a factor of 250 with respect to that of B region. HMF: hydroxymethylfurfural

21

Table 1 Composition of the nine standard solutions used for calibration. 1 a

2

3

4

5

6

7

8

9

Acetic acid

250

50

350

150

450

10

100

30

140

Citric acida

500

1000

250

750

50

750

175

100

425

Formic acida

100

50

200

20

150

5

10

90

85

Lactic acid

20

150

50

250

350

50

10

5

95

Malic acida

450

600

150

300

20

10

75

800

360

Succinic acida

20

40

60

80

100

50

10

200

30

Alaninea

10

25

40

70

55

100

75

30

45

Argininea

10

10

10

10

10

10

10

10

10

Asparaginea

10

10

10

10

10

10

10

10

10

a

a

Aspartic acid

20

20

20

20

20

20

20

20

20

Phenylalaninea

20

400

100

700

1000

50

1400

30

200

γ-aminobutiric acida

30

20

40

50

10

30

10

5

25

10

10

10

10

10

10

10

10

10

10

30

20

40

50

20

50

75

60

4

8

12

20

16

30

25

10

14 14

Glutamine

a

Histidinea Isoleucinea Leucinea

4

20

16

12

8

30

35

10

Lysinea

10

30

20

50

40

60

30

70

5

Prolinea

100

1400

600

1200

1000

400

800

50

200 10

Serinea Tyrosinea Threoninea Valinea

10

10

10

10

10

10

10

10

100

200

20

400

300

10

50

40

30

10

10

10

10

10

10

10

10

10

4

22

15

40

30

6

10

8

12

Fructoseb

460

300

380

340

420

660

550

260

405

Glucoseb

300

330

360

495

415

385

335 20

240

270

b

2

12

8

10

3

4

15

5

Maltoseb

4

4

4

4

4

4

4

4

4

10

16

6

14

4

2

18

8

12

Sucrose

Gluconic acidb Ethanola

20

30

40

80

150

60

70

100

50

Hydroxymethylfurfurala

30

80

10

100

120

20

50

5

40

1000

1000

1000

1000

1000

1000

1000

1000

1000

Ka a

mg kg–1; bg kg–1; Shaded rows: Contents corresponding to mean values find in different honeys.

22

Table 2 Characteristics of 1H NMR observable signals in honey samples at pH 1.0. Multiplicitya

δ (ppm)

Isoleucine (δCH3)

t

0.93

Leucine (δCH3)

d

0.97

Leucine (δ’CH3)

d

0.95

Valine (CH3)

d

0.99

Isoleucine (βCH3)

d

1.01

Valine (γCH3)

d

1.04

Unidentified compound

d

1.14

Ethanol (CH3)

t

1.18

Threonine (γCH3)

d

1.32

Lactic acid (C3H3)

d

1.42

Alanine (βCH3)

d

1.54

Unidentified compound

d

1.82

γ-aminobutiric acid (βCH2)

q

1.95

Proline (γCH2)

m

2.05

Acetic acid (C2H3)

s

2.09

Proline (β’CH)

m

2.17

Proline (βCH)

m

2.43

γ-aminobutiric acid (αCH2)

t

2.38

Succinic acid (α−βCH2)

s

2.68

Citric acid (CH2)

d

2.84

Assignment

Malic acid (β-CH2)

dd

2.86

Malic acid (β-CH2)

dd

2.92

Citric acid (Half CH2)

d

3.04

β-Glucose (H2)

dd

3.25

β-Glucose (H4)

dd

3.40

α-Glucose (H4)

dd

3.42

Fructoseb

m

4.00

c

Fructose

m

4.10

Malic acid (αCH)

dd

4.20

d

4.65

d

5.22

β-Glucose (H1)

Water suppression

4.90

α-Glucose (H1)

Turanose (H1)

d

5.28

Maltose (H1)

d

5.38

Sucrose (H1)

d

5.42

Hydroxymethylfurfural (H4)

d

6.67

Tyrosine (H3, H5)

d

6.90

d

7.20

Tyrosine (H2, H6) Phenylalanine (H2, H6)

dd

7.32

t

7.38

Phenylalanine (H3, H5)

td

7.43

Hydroxymethylfurfural (H3)

d

7.54

Formic acid (CH)

s

8.22

Histidine (ArH)

s

8.70

BTC (internal standard)

s

8.81

Hydroxymethylfurfural (H1)

s

9.45

Phenylalanine (H4)

a

s, singlet; d, doublet; t, triplet; m, multiplet; dd, doublet-doublet; td, triplet-doublet; bPeaks corresponding to β-fructopyranose (H5+H6) + α-fructofuranose (H4); cPeaks corresponding to α-fructofuranose (H3) + βfructofuranose (H3+H4).

23

Table 3 Integration ranges (∆δ), calibration parameters and limits of detection (LOD) and quantification (LOQ) for compounds in honey. Compound Acetic acid

∆δ, ppm 2.07–2.12

Slope 8.62

Sa 0.27

Intercept 17.2

Sb 12.0

R2 0.993

LOD a

15

a

LOQ 49a

Citric acid

3.00–3.04

0.85

0.07

11.1

8.13

0.977

100

334a

Formic acid

8.20–8.26

2.83

0.08

2.86

1.56

0.995

5.8a

19a

a

17a

Lactic acid

1.38–1.43

5.38

0.09

0.08

2.66

0.998

5.2

Malic acid 1

2.89–2.95

2.59

0.02

5.91

2.72

0.999

11a

37a

0.952

a

96a

Malic acid 2

4.25–4.34

25.6

2.18

148

246

29

a

7.4a

Succinic acid

2.64–2.69

5.24

0.07

3.52

1.11

0.999

2.2

Alanine

1.53–1.57

6.22

0.18

–2.74

1.91

0.994

3.2a

11a

a

11a 14a

Isoleucine

0.93–0.98

6.73

0.65

0.83

2.17

0.972

3.4

Leucine

0.98–1.04

5.49

0.67

–1.39

2.20

0.944

4.2a a

39a 70a

Phenylalanine

7.28–7.49

5.67

0.07

–4.13

6.30

0.999

12

Proline 1

2.00–2.07

3.95

0.06

–18.8

7.85

0.998

21a

Proline 2

2.11–2.21

2.08

0.18

–40.5

24.8

0.951

a

417a

a

125

Proline 3

2.39–2.48

2.15

0.15

–55.7

20.9

0.967

102

340a

Tyrosine 1

6.87–6.92

2.02

0.02

0.04

0.99

0.999

5.1a

17a

a

20a

Tyrosine 2

7.16–7.22

1.97

0.03

–2.43

1.14

0.998

6.1

Valine

1.04–1.08

4.41

0.49

–0.82

2.22

0.965

5.3a

18

Fructose 1

3.96–4.03

1259

46

264

1072

0.996

10b

30b

Fructose 2

4.07–4.12

453

24

178

918

0.992

21b

71b

0.987

b

98b

b

Glucose 1

c

Glucose 2

c

5.18–5.25

403

23

357

1485

0.978

39

129b

β-Glucosed

3.18–3.29

1102

72

256

2520

0.987

24b

80b

1888

0.987

b

18

59b

97

0.982

2.4b a

11a

e

3.18–3.29

696

30

312

α-Glucose

5.18–5.25

1126

93

–492

Sucrose

5.40–5.44

418

22

57

Ethanol

1.15–1.20

10.3

0.3

HMF 1

6.65–6.71

1.20

0.04

HMF 2 HMF 3

7.52–7.58 9.42–9.47

1.19 1.17

0.04 0.02

a

1952

29

8.1b

–5.90

3.20

0.995

3.3

1.56

0.69

0.993

6.0a

20a

0.992

8.0

a

28a

4.1

a

14a

1.76 1.26

0.94 0.46

0.998

mg kg–1 honey; bg kg–1 honey; cparameters calculated in relation to total glucose content; dparameters calculated in relation to β-glucose content; eparameters calculated in relation to α-glucose content; Sa: standard error for slope; Sb: standard error for intercept; N=9 in all equations; HMF: hydroxymethylfurfural

24

Table 4 Recovery results. Eucalyptus honey

Rosemary honey

Recovery (N=9) Compound

Content

Mean (%)±SD

RSD (%)

Recovery (N=9) Content

Mean (%)±SD

RSD (%)

a

Acetic acid

a

41.6

101.7±4.0

3.9

18.9

98.1±4.7

4.8

Formic acid

20.6a

103.0±3.9

3.8

35.4a

96.7±4.7

4.9

Lactic acid

86.0a

98.2±2.9

3.0

14.0a

101.4±0.9

0.9

a

5.2

Malic acid

a

119

102.5±7.5

7.3

131

105.4±5.5

Succinic acid

30.1a

104.3±5.7

5.4

50.5a

103.8±4.9

4.7

Alanine

21.4a

94.8±2.8

3.0

34.5a

99.9±2.5

2.5

Phenylalanine

106a

99.2±5.1

5.1

402a

99.9±2.1

2.1

Proline

715

100.8±6.9

6.8

253a

98.6±5.4

5.5

Tyrosine

22.9a

97.6±5.9

6.0

80.6a

103.5±2.2

2.1

b

b

a

Fructose

398

105.2±6.5

6.9

379

104.0±5.3

5.4

β-Glucose

199b

99.2±1.2

1.2

187b

100.6±1.3

1.3

α-Glucose

126b

101.3±1.2

1.2

121b

99.0±1.5

1.5

a

Ethanol

12.3

93.7±2.0

2.1

37.2

100.3±3.2

3.2

HMFc

32.8a

102.3±5.1

5.0

22.1a

101.1±4.6

4.6

a

mg kg–1 honey; bg kg–1 honey; cHMF: hydroxymethylfurfural

25

Table 5 Precision (RSD %) and accuracy (bias %) determined in a standard solution. Compound Acetic acid Formic acid

Concentration Nominal Measured 60.00a 57.89a 40.00a

39.83a

a

a

RSDc, %

Biasd, %

4.76

3.51

2.40

0.42

Lactic acid

35.50

35.42

0.88

0.23

Malic acid

100.0a

102.9a

5.21

2.90

Succinic acid

12.00a

11.93a

0.95

0.58

Alanine

a

20.00

19.59a

3.12

2.06

Phenylalanine

350.0a

338.5a

2.98

3.28

a

a

Proline

250.0

246.8

3.50

1.30

Tyrosine

100.0a

98.90a

2.76

1.10

Fructose

b

350.0

367.4

b

3.67

4.97

β-Glucose

190.0b

191.4b

2.24

0.74

α-Glucose

b

120.0

118.8

b

2.15

1.00

Ethanol

28.00a

27.38a

1.45

2.20

a

a

0.78

0.70

e

HMF

30.00

30.21

a

mg/kg honey; bg/kg honey; cMean of nine measurements, obtained from three different preparations and three repetitions of the data processing per spectrum; dCalculated as measured concentration– nominal concentrationx100/nominal concentration; eHMF: hydroxymethylfurfural

26

Table 6 Results obtained applying the proposed 1H NMR method in eucalyptus (EU), heather (HE), lavender (LA), orange blossom (OB), thyme (TH) and rosemary (RO) honeysa. Compound

EU1

EU2

HE1

HE2

LA1

LA2

OB

TH

RO

Acetic acidb

71.8

60.4

78.9

73.4

59.8

56.3

16.8



Formic acidb

54.7

51.6

150.6

103.0

42.8

48.3

27.7

38.9

27.5

Lactic acidb

34.4

21.5

43.0

51.2

46.6

27.2


51.7

20.1

Malic acidb

182

218

46.3

91.3

168

47.1

55.9

49.2

241

Succinic acid

62.9

69.7

44.8

41.8

68.5

19.8

7.1

16.5

41.3

Alanineb

36.0

37.6

37.2

45.2

46.8

37.1

18.7

22.8

16.6

72

131

281

379

671

499

42.1

398

152

398

419

399

469

534

608

234

313

314

Tyrosine

38.3

53.3

58.1

106

242

191

25.9

90.2

64.8

Fructosec

387

372

390

379

371

408

346

366

357

β-Glucosec

200

181

185

170

184

189

168

176

183

c

α-Glucose

128

114

117

109

115

120

106

112

122

Ethanolb

22.6

19.3

50.3

28.1

11.5

25.5



47.0

HMF

15.3

38.6

70.7

27.8


35.5

60.5


32.4

Fructose/Glucose

1.18

1.26

1.29

1.36

1.24

1.32

1.26

1.27

1.17

b

Phenylalanine

b

Prolineb b

e,b

a

Mean of three measurements; bmg kg–1 honey; cg kg–1 honey; dLOQ, limit of quantification;

e

HMF: hydroxymethylfurfural

27

Determination of 13 metabolites in honey was performed by 1H NMR spectroscopy.

The absolute areas of the signals were used for quantification.

No previous separation or pre-concentration steps were required.

The two anomeric forms of glucose, α and β, could be quantified.

28