Detection of Adulteration of Honey: Comparison
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Nowadays, botanical and geographical originality of honey is a major concern among authorities in order to ensure its quality and authenticity, by imposing specific standards that allow honey to be competitive in the market. Traditionally, identification of botanical and geographical origin of honey is performed by melisopalinological analysis. This analysis is a time and cost-consuming process which cannot ensure reliable characterization of the honey floral source since it strongly depends on the capability of the analyzer.

  • honey authentication
  • SPME
  • GC-MS
  • IR
  • Raman
  • chemometrics
  • botanical and geographical origin
  • adulteration

1. Introduction

The composition of honey depends on collection season, climate conditions, proximity to the forest, characteristics of soil which determine melliferous flora, method of storage, processing, and practices of beekeeping, and even interactions between chemical compounds and enzymes [3,8,9][1][2][3]. However, the aroma and taste of honey, owed to the volatile compounds, are dependent mainly on the botanical and floral origin of honey [9,10,11,12][3][4][5][6].

Nowadays, botanical and geographical originality of honey is a major concern among authorities in order to ensure its quality and authenticity, by imposing specific standards that allow honey to be competitive in the market [3,16][1][7]. Traditionally, identification of botanical and geographical origin of honey is performed by melisopalinological analysis [18,19][8][9]. This analysis is a time and cost-consuming process which cannot ensure reliable characterization of the honey floral source since it strongly depends on the capability of the analyzer [20,21][10][11]. Therefore, it is important to complement this analysis with other, more analytical techniques (physicochemical, organoleptic, chromatographic etc.), or replace it with them. During the past decades, several studies focused on gas chromatography (GC) in order to determine the volatile profile of honey [3,9,11,20,22,23,24,25,26][1][3][5][10][12][13][14][15][16]. The characteristic chemical fingerprint generated by volatile compounds is of major importance regarding consumers’ choice since it provides information about the botanical and geographical origin of honey [27][17].

Another main topic concerning the beekeeping sector, the honey industry, and researchers is the adulteration of honey. According to European Union regulations, the addition or removal of any kind of honey substance is illegal [49][18]. Honey adulteration is achieved by adding lower quality honey and artificial adulterants [50][19]. Honey’s health benefits, and its unique flavor and aroma make it more expensive in comparison to other sweeteners. Therefore, in an attempt to reduce production costs and simultaneously increase profit, honey is a product usually subjected to adulteration [9,15,51][3][20][21]. Starch and inverted syrup fed to bees, addition of sugars such as high fructose, glucose, and saccharose syrups, and low-quality honey added to high-priced honey are considered the most common ways of honey adulteration [15,52][20][22]. Honey adulteration can occur in any step of production or processing. It is also difficult to detect due to the fact that the adulterated honey is similar to the pure one [53][23]. Moreover, the classical methods that certify honey quality, such as physicochemical analyses, are incapable of detecting adulteration accurately. Thus, it is essential to develop and adopt a new process for honey quality control. For the aforementioned reasons, many analytical techniques have been applied, characterized by high effectiveness, accuracy, and sensitivity for the detection of honey adulteration [9][3].

The aim of this work was to present a review (period of 2007–2020) of SPME-GC-MS and spectroscopic techniques in combination with chemometrics for honey authentication. In addition, spectroscopic techniques (IR, Raman) combined with chemometric analysis for the investigation of honey adulteration are briefly discussed.

2. Honey Volatile Compounds Analysis Using SPME-GC-MS

Volatile compounds of honey are related to the floral origin and could be used as biomarkers. SPME followed by GC-MS for determining the volatile profile of honey are used as a tool for the botanical characterization of several different types of honeys [8,11,14,21,27,37,85,86,87,88][2][5][24][11][17][25][26][27][28][29].

Croatian honey samples of Paliurus honey were dominated by nonanal, four isomers of lilac aldehyde, decanal, methyl nonanoate, hexanoic, and 2-ethylhexanoic acids [27][17].

During the past decade, it has been noted that SPME-GC-MS fingerprinting of honey volatiles combined with chemometrics can be considered as non-time and of high potential combination also for routine analyses of honey for their botanical characterization.

Several studies of honey volatile composition that used SPME-GC-MS with chemometrics suggested that their combined usage in order to determine geographical origin of honey is a robust and reliable method of a high predictive ratio.

3. Authentication of Honey Using IR Spectroscopy

Infrared-based spectroscopy can be used for the detection of different adulterants in honey at different ranges of absorption. Chemometrics has been used as an essential tool for chemical fingerprinting of honey ( Table 21 ).

Table 1. Application of vibrational spectroscopic techniques coupled with chemometrics in detection of honey adulteration.
Type of SpectroscopyChemometrics MethodsType of AdulterantsReferences
ATR-FTIRPCA, SIMCA, PLSFructose syrup, glucose syrup, sucrose syrup, corn syrup, cane sugar[97]
ATR-FTIRPCA, DA, PLSCommercial sugars of aren (Arenga pinnata), coconut, cane sugar[98]
ATR-FTIR and RamanPCASucrose, reducing sugars[59]
MIR and RamanPLS, Data fusionHigh fructose corn syrup, maltose syrup[57]
NIRDPLSHigh fructose corn syrup[91]
NIRCARS, PLS- LDAHigh fructose corn syrup[93]
NIRPLS-DAGlucose syrup, fructose syrup, cheap imported honey[65]
NIRPCA, PLSCorn syrup, sucrose syrup, high fructose corn syrup, beet syrup, rice syrup[94]
NIR and MIRPCA, PLS, DARice syrup, corn syrup[80]
NIR and ATR-FIIRSVM, Data fusionType 1: rice and beet syrup, type 2: high fructose corn syrup, corn syrup, maltose syrup, sucrose syrup[51]
RamanPCA, PLS, artificial neural network ANNGlucose, fructose, sucrose, maltose[58]
RamanAdaptive iteratively reweighted penalized least squares airPLS, PLS, DAHigh fructose corn syrup, maltose syrup[64]
RamanSIMCAMolasses, date molasses, grape molasses, high fructose corn syrup, corn syrup (dark and light), sucrose, inverted sugar[63]
NIRHCA, PCA, LDA, PLSHigh fructose corn syrup[55]
NIRHCA, LDA, PLSInverted sugar, rice syrup, brown cane sugar, fructose syrup[50]

Attenuated total reflectance (ATR)-FTIR spectroscopy coupled with chemometrics was used in a study on stingless bee ( Heterotrigona itama ) honey from Malaysia for its capacity to detect adulteration by five adulterants including fructose, glucose, sucrose, corn syrup, and cane sugar. Applying PCA, all the adulterants were discriminated at the spectral region 1180–750 cm −1 . Especially, the absorption peaks at 1054, 876, and 779 cm −1 were attributed to the increasing percentages of fructose. The characteristic peaks at 1022, 991, and 898 cm −1 were assigned to the presence of glucose, and at 991 and 921 cm −1 to the presence of sucrose. PLSR analysis was also able to quantity honey adulteration in all five cases [97][30]. In another study of honey adulteration with sugar, FTIR spectrometer with an ATR device was applied to honeys produced in different places of Ecuador combined with PCA. This combination showed to be ideal for the quality control of honey [59][31]. The ATR-FTIR technique has been also used alongside chemometrics for the estimation of the adulteration with commercial sugars of aren ( Arenga pinnata ), coconut, and cane sugar of Indonesian honeys. PCA and PLS analyses were applied for differentiation and quantification of the samples, respectively. It was proved that this combination is suitable for the detection of adulteration and measurement of the added sugar at Indonesian honeys [98][32].

Application of vibrational spectroscopic techniques coupled with chemometrics in detection of honey adulteration.

The combination of IR with chemometrics provide satisfactory discrimination and rapid first-line classification of honey based on the botanical and geographical origin.

4. Authentication of Honey Using Raman Spectroscopy

Raman spectroscopy can be successfully used to detect adulteration of honey ( Table 21 ).

Raman technique coupled with multivariate analysis was applied at honeys to identify and quantify sugars (glucose, fructose, maltose, and sucrose contents) and further to characterize them as adulterants. The characteristic spectral bands that correlated to sugars of honey were 314, 341, 415, 530, 617, 744, 776, 790, 838, 856, 911, 933, 1028, and 1106 cm −1 . PCA, partial least squares (PLS), and artificial neural network (ANN) were used to extract differentiation from the spectroscopic data which successfully led to the discrimination of sugar contents in honey [58][33]. Moreover, Raman technique was used by Salvador et al. [59][31] to detect the sugar content and the type of adulteration in commercial honeys of Ecuador. The main observed bands of honeys from Pichincha and Loja provinces were 326, 338, 419, 516, 630, 707, 817, 862, 918, 1062, and 1126 cm −1 . These bands were assigned to the presence of sugar (glucose, fructose, and sucrose) at honey samples. The bands of pure honey at 817 and 862 cm −1 , in the case of adulteration with sucrose, were overlapped with strong absorptions at 822 and 834 cm −1 . Principal component analysis was applied and confirmed the applicability of Raman technique for the detection of adulteration of honey with glucose, fructose, and sucrose.

In another study, Raman spectroscopy was also used to detect adulteration of honey with high fructose corn syrup and/or maltose syrup. The characteristic bands corresponding to authentic and adulterated honeys were observed: 351, 425, 517, 592, 629, 705, 778, 824, 865, 915, 981, 1065, 1127, 1264, 1373, and 1461 cm −1 ( Figure 51 ). The spectra data were subjected to adaptive iteratively reweighted penalized least squares (airPLS). Using PLS-LDA, classification of honeys was achieved in both cases of adulterants and in mixtures of them [64][34]. Chemometrics with Raman spectroscopy were successfully employed for the quantification of HFGCS (high fructose syrup) in adulterated honey, as well. At the band of 2791 cm −1 , the absorption was increased by increasing the HFGS concentration, while at 1130 cm −1 , the absorption was reduced due to the decrease in protein and amino acid content in the adulterated honeys. Three data fusion strategies were used and showed high predictability in the adulteration of honey, while the best results were obtained by the high-level data fusion process [57][35].

Foods 10 01671 g005 550
Figure 1. Raman spectra of a randomly selected authentic honey sample and the same honey sample adulterated with high fructose corn syrup (40%, w/w) and maltose syrup (40%, w/w). Reprinted with permission from ref. [64]. Copyright 2012 Copyright Elsevier Inc.

Raman technique is capable of on-site testing of honey samples to authenticate and verify their label information based on its origin.

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