Honey is a frequent target of adulteration through inappropriate production practices and origin mislabelling. Current methods for the detection of adulterated honey are time and labor consuming, require highly skilled personnel, and lengthy sample preparation. Fluorescence spectroscopy overcomes such drawbacks, as it is fast and noncontact and requires minimal sample preparation. In this paper, the application of fluorescence spectroscopy coupled with statistical tools for the detection of adulterated honey is demonstrated. For this purpose, fluorescence excitation-emission matrices were measured for 99 samples of different types of natural honey and 15 adulterated honey samples (in 3 technical replicas for each sample). Statistical
Honey is a pure natural food produced by bees from the nectar of flowers. Its main components are different types of carbohydrates, water, and some minor constituents, such as pollen grains, proteins, amino acids, lipids, alkaloids, enzymes, and flavoring components. The composition and concentrations of minor constituents are unique characteristics of honey, and some of them can be used to differentiate honey samples by their geographical and botanical origins, as well as to define their quality and authenticity [
According to European Union standards [
As a relatively expensive food product, honey is a frequent target of adulteration through inappropriate production practices and its origin mislabelling [
Optical spectroscopy methods are less demanding in this respect, and many of them are successfully applied in food analyses [
Here, we aimed at exploring differences in fluorescence between natural honey and adulterated honey which arise due to dissimilar composition of fluorescence species that are present in them. For this purpose, we measured fluorescence excitation-emission matrices of 114 samples of natural and fake honey samples (in three technical replicas) aiming at developing the fluorescence-based technique for the fast and nondestructive detection of honey adulteration. Principal component and linear discriminant analysis of characteristic honey emission features were utilized to describe observed differences and to build and test the classification model.
In this research, a total of 114 samples, 99 natural honey samples (45 acacias, 11 lindens, 14 sunflowers, and 29 meadow mixes) and 15 fake honey samples, were obtained from the Association of the Beekeeping Organizations of Serbia (SPOS,
Room temperature fluorescence excitation-emission matrices (EEMs) were obtained by a Perkin Elmer Fluorescence Spectrophotometer LS45 in a front face measurement configuration. The instrument was equipped with a Xe lamp for excitations and a R928 PMT for the detection of emission radiation. EEMs were recorded over the 270–640 nm emission range (at 0.5 nm intervals) and 240–500 nm excitation range (with a 5 nm step). Emission intensities were automatically normalized to the excitation intensity by the instrument. Honey samples were liquefied at 40°C and pipetted into 3 mL quartz cuvettes before measurements. Contributions to measured signal intensity from the first and second order Rayleigh scattering were removed and replaced with interpolated values. The use of interpolated values to remove scattering contribution has been shown to provide better and more meaningful results when dealing with EEMs compared to the data deletion [
Data analyses comprised the calculation of spectral domain volumes below EEM intensity surfaces, testing of the statistical significance of differences observed between spectral characteristics of natural and fake honey samples by a
Spectral domain volumes (spectral domain being represented by maximal and minimal values of excitation and emission wavelengths,
Principal component analysis (PCA) is a method used for reducing data dimensionality and identifying differences between analysed samples as well as investigating and visualizing variations found in a data set [
Linear discriminant analysis (LDA) is a linear classification method with a goal to find one or more linear functions of the input variables which, then, can be used for the sample classification. Fisher’s algorithm [
Figure
Fluorescence excitation-emission matrices of (a) naturaland (b) fake honey and (c) their difference spectrum. The discussedspectral regions are marked with a full line
Intrinsic fluorophores and their specific microenvironments in honey produce a complex excitation-emission pattern which varies among samples. As presented in Table
Spectral regions showing characteristic fluorescence of honey fluorophores.
Spectral domain |
|
|
Fluorophore |
---|---|---|---|
1st | 240–265 | 370–495 | Phenolic compounds |
2nd | 280–320 | 390–470 | Phenolic compounds |
3rd | 260–285 | 320–370 | Aromatic amino acids |
4th | 310–360 | 370–470 | Phenolic compounds |
5th | 375–435 | 440–520 | Maillard reaction products |
Statistical analysis mean values, standard deviation: SD, and statistical significance of difference between means:
Spectral domain | Honey | Mean |
SD |
|
---|---|---|---|---|
1st | Natural | 1.38 × 105 | 3399 | 4.90 × 10−13 |
Fake | 6.13 × 104 | 8688 | ||
2nd | Natural | 9.42 × 104 | 2580 | 2.03 × 10−11 |
Fake | 4.14 × 104 | 6597 | ||
3rd | Natural | 5.70 × 104 | 1895 | 7.97 × 10−12 |
Fake | 1.73 × 104 | 4844 | ||
4th | Natural | 3.37 × 105 | 6844 | 5.44 × 10−13 |
Fake | 1.83 × 105 | 17494 | ||
5th | Natural | 3.08 × 105 | 5191 | 3.12 × 10−21 |
Fake | 1.40 × 105 | 13268 |
Differences in fluorescence of natural and fake honey samples are extremely significant in all spectral regions (
PCA was performed for the further study of differences between fluorescence responses of honey and fake honey samples; results are presented in Figure
Results of PCA of unfolded honey EEM spectra: (a) loadings of the first principal component, (b) loadings of the second principal component, (c) cumulative variance plot, and (d) the PCA score plot.
The quality of fluorescence-based discrimination between natural and fake honey samples was evaluated by a linear discriminant analysis (LDA) of 5 spectral domain volumes. LDA is a method used to find one or several linear functions (linear latent variables) of the data features that can be used for separation between two or more groups [
LDA classification and cross-validation errors.
Classification error (%) | Cross-validation error (%) | |
---|---|---|
Natural honey | 0 | 0 |
Fake honey | 0 | 0 |
To conclude, fluorescence excitation-emission spectroscopy can be effectively used for the nondestructive and fast detection of adulterated honey specimens. Differences in fluorescence of natural and adulterated honey samples are extremely significant in five spectral regions due to differences in concentrations and local environments of aromatic amino acids, phenolic compounds, furosine, and hydroxymethylfurfural, as is demonstrated by statistical testing and PCA. By quantifying fluorescence responses and subjecting them to the statistical classification technique, for example, LDA, it is possible to detect adulterated honey with 100% accuracy. Such accuracy suggests that fluorescence excitation-emission spectroscopy may be a promising method for the low-level adulteration of honey, which is the subject of our future work.
Data are available upon request to Lea Lenhardt Acković (
The authors declare that there are no conflicts of interest regarding the publication of this paper.
The authors acknowledge the financial support of the Ministry of Education, Science and Technological Development of the Republic of Serbia (Projects 45020 and 173049).