400 MHz nuclear magnetic resonance (NMR) spectroscopy and multivariate data analysis techniques were used in the context of food surveillance to measure 328 honey samples with 1H and 13C NMR. Using principal component analysis (PCA), clusters of honeys from the same botanical origin were observed. The chemical shifts of the principal monosaccharides (glucose and fructose) were found to be mostly responsible for this differentiation. Furthermore, soft independent modeling of class analogy (SIMCA) and partial least squares discriminant analysis (PLS-DA) could be used to automatically classify spectra according to their botanical origin with 95–100% accuracy. Direct quantification of 13 compounds (carbohydrates, aldehydes, aliphatic and aromatic acids) was additionally possible using external calibration curves and applying TSP as internal standard. Hence, NMR spectroscopy combined with chemometrics is an efficient tool for simultaneous identification of botanical origin and quantification of selected constituents of honeys.
Honey is a natural, sweet, and syrupy fluid collected by bees from nectar of flowers [
Each honey is unique on the basis of chemistry, amount, and combination of the various components that give each honey a unique and individual organoleptic character. The control and characterization of quality and botanical origin of unifloral honeys are of great importance and interest in apiculture. Today the most important techniques to determine or certify the unifloral origin of honeys are the melissopalynological analysis and the evaluation of organoleptic characteristics [
Various novel, fast, and accurate chromatographic methods such as high-performance liquid chromatography (HPLC) [
Apart from these analytical methods, the application of multivariate data analysis and, in particular, principal component analysis (PCA) [
Nuclear magnetic resonance (NMR) spectroscopy has been also used to assess the botanical origin of honey and quantify some major compounds in it [
A total of 328 samples from different botanical origins were analyzed using NMR. The samples were randomly selected by governmental food inspectors from Baden-Württemberg, Germany, from honey bottling plants, supermarkets and directly by bee keepers. The following reference standards were used in proanalysis quality: hydroxymethylfurfural (HMF), fumaric acid, citric acid, malic acid, erlose, melibiose, xylitol, oxalic acid (anhydrous), D-glucuronic acid, DL-lactic acid (Sigma Aldrich, Steinheim, Germany); formic acid, phthalic acid, and glucose, L(+)-tartaric acid, fructose, D(+)-galactose, maltose, and saccharose, barbituric acid (Merck, Darmstadt, Germany); L(+)-rhamnose, arabinose, maltotriose, D(+)-turanose, D(+)-mannose, D(+)-xylose, D(+)-trehalosedihydrate, D(+)-melezitose monohydrate, D(+)-raffinosepentahydrate, malonic acid, pyruvic acid, and DL-proline (Fluka, Buchs, Switzerland); gluconic acid (calcium salt), and succinic acid (Carl Roth, Karlsruhe, Germany). The NMR buffer was prepared by dissolving 10.21 g of KH2PO4 and 9.75 mg of sodium azide in 50 mL of pure water and then by adjusting the pH to 4.5 with H3PO4 or KOH.
The water content was obtained for each honey before NMR measurement using the German reference refractometric method [
All NMR measurements were performed on a Bruker Avance 400 Ultrashield spectrometer (Bruker BioSpin, Rheinstetten, Germany) equipped with a 5 mm SEI probe with Z-gradient coils, using a Bruker Automatic Sample Changer (B-ACS 120). 1H NMR spectra were acquired at 300.0 K without sample rotation. 64 scans and 4 prior dummy scans of 65 k points were acquired with a spectral width of 19.9914 ppm, a receiver gain of 22.6, and an acquisition time of 4.096 s. Water suppression was achieved using the NOESY-presaturation pulse sequence (Bruker 1D noesygppr1d pulse sequence) with irradiation at the water frequency (1890.60 Hz) during the recycle and mixing time delays. 13C NMR spectra were acquired using a Bruker zgpg30 pulse sequence with 1024 scans and 4 prior dummy scans. The sweep width was 238.9 ppm, the time domain of the FID was 66 k, receiver gain of 2050, and an acquisition time of 1.38 s. The data were acquired automatically under the control of ICON-NMR (Bruker BioSpin, Rheinstetten, Germany), requiring about 91 min per sample (for both 1H and 13C NMR). All NMR spectra were phased, baseline-corrected, and calibrated by the TSP signal at 0.0 ppm.
Multivariate data analysis was performed using Unscrambler X version 10.0.1 (CAMO Software AS, Oslo, Norway) and Amix version 3.9.4 (Bruker BioSpin, Rheinstetten, Germany). First, to cope with small variations in pH or other sample conditions such as ionic strength or temperature, simple rectangular bucket tables were obtained from the complete sets of 1H and 13C NMR spectra. In both cases, scaling to total intensity was used. Further details on the bucketing process of NMR spectra for multivariate data analysis were previously described [
Figure
1H NMR spectra of
The PCA score plots generated using PC3-PC4 (1H NMR) and PC1-PC3 (13C NMR) to visualize the separation of the polyfloral honeys are shown in Figure
Scatter plot of the PCA scores of floral and honeydew honeys obtained from 1H NMR (9–0.25 ppm; no scaling (a)) and 13C NMR (200–0.25 ppm; scaling to unit variance (b)).
Scatter plot of the PCA scores of different types of unifloral honeys obtained from 1H NMR (9–0.25 ppm; Pareto scaling (a)) and 13C NMR (200–0.25 ppm; no scaling (b)).
Loadings plots allow to specify the variables (chemical shifts), which are responsible for the observed clustering for both data sets (1H and 13C NMR). Table
The most important variables (buckets) for the differentiation of different botanical origins of honey (chemical shifts are given in ppm).
Botanical origin of honey | 1H NMR | 13C NMR |
---|---|---|
Honeydew/floral | 1.17–1.20 |
101.49 |
| ||
Coniferous | 3.84–3.87 |
62.84 |
| ||
Rape | 4.64–4.66 |
75.81–75.88 |
Clover and rape | 7.65–7.95 | |
Sunflower | 3.25–3.27 | |
| ||
|
3.69 |
69.24–69.75 |
|
1.38–1.41 | |
| ||
Chestnut | 4.32–4.34 |
71.45–71.49 |
| ||
Mountain honey | 3.88–3.91 | 95.90 |
| ||
Orange tree | 8.13 |
62.75 |
| ||
Fruit tree flowers | 3.44–3.45 | —a |
aNo distinct cluster was obtained for fruit tree flowers with 13C NMR spectra.
Next, it is interesting to show the predictive power of the chemometric methods by classifying new samples. To do this, two data analysis methods (SIMCA and PLS-DA) were evaluated for predicting class membership of honey samples from the 1H NMR spectra. The independent test set for the floral/honeydew honey model (honeydew) consisted of 20 randomly selected objects (10 floral, 10 honeydew honeys). For the unifloral honey model, mountain (
Besides the classification of botanical origin of honey samples, it would be advantageous to establish a NMR method for the quantification of main constituents in the honey matrix. As first evaluation, if a quantitative approach is at all possible from the NMR spectra, we measured 34 commercially available compounds that may be present in honey. Then, the spectra of standards were compared to the spectra of honey samples. For most of the substances studied, direct quantification with integration is not possible due to extensive spectra overlap. As an example, the spectra of four carbohydrates are shown in Figure
NMR spectra of maltose, sucrose, D(+)-galactose, and D(+)-xylose standards in the mid-field region.
However, we were able to find 13 metabolites for which at least one resolved unambiguous resonance could be identified. Selected 1H NMR peaks (i.e., signals not overlapped or interfered by matrix) corresponding to each substance are shown in Table
NMR integration regions and investigated linear concentration ranges.
Compound | NMR range | Working range (mg/kg) |
---|---|---|
HMF | 9.43–9.47 ppm (singlet) | 20–600 |
Formic acid | 8.44–8.47 ppm (singlet) | 40–1400 |
Phthalic acid | 7.53–7.48 ppm (multiplet) | 30–900 |
Fumaric acid | 6.53–6.55 ppm (singlet) | 20–670 |
Pyruvic acid | 6.42–6.45 ppm (singlet) | 180–5000 |
L(+)-rhamnose | 5.13–5.09 ppm (doublet) | 160–2500 |
Glucose | 4.63–4.65 ppm (singlet) | 13–43000 |
Arabinose | 4.52–4.54 ppm (singlet) | 40–1200 |
L(+)-tartaric acid | 4.32–4.35 (singlet) | 90–2600 |
Fructose | 4.14–4.08 (doublet) | 11–46000 |
Malic acid | 2.73–2.70 (two singlets) | 30–1000 |
Citric acid | 2.69–2.68 (singlet) | 40–1300 |
Succinic acid | 2.50–2.52 (singlet) | 10–800 |
Results of the quantitative determination of substances by NMR (values are given in g/kg honey).
Sample | HMF | Formic acid | Phthalic acid | Fumaric acid | Pyruvic acid | L(+)-rhamnose | Glucose | Arabinose | L(+)-tartaric acid | Fructose | Malic acid | Citric acid | Succinic acid |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|
Manuka honey | 0.03 | 0.15 | n.d. | n.d. | n.d. | n.d. | 218 | 0.41 | 0.31 | 284 | n.d. | 0.09 | 0.03 |
Flower honey | n.d.a | 0.05 | n.d. | n.d. | n.d. | n.d. | 350 | 0.18 | 0.31 | 357 | 0.2 | n.d. | 0.01 |
Sunflower honey | 0.05 | 0.06 | n.d. | 0.03 | n.d. | n.d. | 348 | 0.26 | 0.38 | 379 | 0.4 | 0.49 | 0.01 |
Honeydew honey | n.d. | 0.08 | n.d. | 0.02 | n.d. | n.d. | 219 | 0.41 | 0.58 | 314 | n.d. | 0.25 | 0.17 |
Chestnut honey | 0.06 | 0.71 | n.d. | 0.05 | n.d. | n.d. | 215 | 0.56 | 0.64 | 350 | n.d. | n.d. | 0.08 |
Flower honey | n.d. | 0.15 | n.d. | 0.04 | n.d. | n.d. | 271 | 0.49 | 0.59 | 312 | 0.9 | n.d. | 0.1 |
|
0.06 | 0.04 | n.d. | n.d. | n.d. | 0.23 | 245 | 0.29 | 0.48 | 381 | n.d. | 0.17 | 0.01 |
Orange honey | 0.09 | 0.06 | n.d. | n.d. | n.d. | n.d. | 281 | 0.47 | 0.61 | 390 | 0.25 | 0.26 | 0.02 |
Flower honey | n.d. | 0.07 | n.d. | n.d. | n.d. | 2.6 | 337 | 0.26 | 0.35 | 358 | —b | n.d. | 0.02 |
Flower honey (mountain) | 0.02 | 0.04 | n.d. | n.d. | n.d. | n.d. | 318 | 0.17 | 0.38 | 366 | n.d. | 0.1 | n.d. |
Rape honey | 0.01 | 0.07 | n.d. | n.d. | n.d. | n.d. | 349 | 0.27 | 0.39 | 379 | n.d. | n.d. | 0.03 |
Honey from fruit trees | n.d. | 0.12 | n.d. | 0.03 | n.d. | n.d. | 296 | 0.88 | 0.44 | 338 | n.d. | n.d. | 0.04 |
Flower honey | 0.06 | 0.08 | n.d. | 0.03 | n.d. | n.d. | 297 | 0.24 | 0.46 | 366 | n.d. | n.d. | 0.03 |
Flower honey | 0.06 | 0.09 | n.d. | 0.03 | n.d. | n.d. | 316 | 0.32 | 0.47 | 382 | n.d. | n.d. | 0.07 |
Chestnut honey | n.d. | 0.8 | n.d. | 0.04 | n.d. | n.d. | 241 | 0.65 | 0.74 | 395 | n.d. | 0.19 | 0.18 |
Honeydew honey | 0.03 | 0.11 | n.d. | 0.04 | n.d. | n.d. | 259 | 0.52 | 0.66 | 352 | n.d. | 0.28 | 0.28 |
Flower honey | 0.03 | 0.11 | n.d. | n.d. | n.d. | n.d. | 260 | 0.34 | 0.54 | 406 | n.d. | n.d. | 0.03 |
Eucalyptus honey | 0.06 | 0.1 | n.d. | n.d. | n.d. | n.d. | 302 | 1.72 | 0.45 | 346 | 0.28 | n.d. | 0.06 |
Flower honey | n.d. | 0.63 | n.d. | n.d. | 1.45 | n.d. | 215 | 0.65 | 0.59 | 380 | n.d. | n.d. | 0.05 |
Flower honey with jelly Royal | 0.03 | 0.05 | n.d. | n.d. | n.d. | n.d. | 315 | 0.4 | 0.46 | 368 | —b | 0.17 | 0.02 |
aNot detectable (0.03 g/kg (phthalic acid and malic acid), 0.02 g/kg (HMF and fumaric acid), 0.18 g/kg (pyruvic acid), 0.16 g/kg (L(+)-rhamnose), 0.04 (citric acid), and 0.01 g/kg (succinic acid)).
bOverlapped signal, direct quantification is not possible.
400 MHz 1H NMR spectra of fructose (a) and formic acid (b) in standard solutions and honey samples.
NMR spectroscopy has already been used in honey analysis to determine its botanical and geographical origin. In the paper of Lolli et al., 71 Italian honey samples (
With regard to quantification, NMR was only used for determining several saccharides with 13C NMR [
In conclusion, it should be noted that honey is a very complex matrix endowed with very specific physicochemical properties. This complexity makes the analysis of honey difficult in terms of its different properties. Often the determination of botanical origin is complicated because of the incomplete correlation between analytical parameters: sensory properties and botanical identity.
Our investigation has shown that 1H NMR spectra of honeys in combination with appropriate multivariate statistics can provide qualitative information about the botanical origin and represent a good basis for the identification of marker compounds for the specific honey types. Quantitative information about a number of major components is also available from the same spectra without need for chromatographic separation. In combination with multivariate data analysis, NMR spectroscopy possesses the speed, simplicity, and low cost per analysis required for a screening technique.
The authors declare that there is no conflict of interests.
The authors are grateful to Margit Böhm, Bernd Siebler, Jürgen Geisser, Antje Theiner, Beate Wagner, Karin Wolff, and Klaus Klusch for their excellent technical assistance. The views expressed in this paper do not necessarily reflect those of the Ministry of Rural Affairs and Consumer Protection.