Honey is a natural viscous, aromatic and sweet substance from various types of secretions of different plants (Codex Alimentarius Commission, 2001; Trifković et al., 2017) and produced by the honey bees, such as the western honey bee (
Notwithstanding, the expectations for the honey are usually high, and the authenticity of honey can depend on (Bogdanov & Martin, 2002) honey production (geographical and botanical origins) and labelling (
Identifying its botanical and/or geographic origin of requires the history of the honey to be traced of from the source,
In this review, a detailed computer-based search of the methods and strategies used for the identification of honey was performed through some publications databases (
Melissopalynology, one of the most affirmed methods for honey identification, is based on the analysis of pollen grain naturally occurring in honey. A microscopic analysis identifies all the components of the pollen spectrum, which can be compared with pollen spectra already known (Anklam, 1998). This method unequivocally provides important information about the nectar source and evidence of the botanical origin of a honey sample (Von Der Ohe et al., 2004; Corvucci et al., 2015). In addition, this method is a valid choice for the geographical fingerprint of honeys in case of areas with site-specific melliferous flora (Anklam, 1998). Through the melissopalynological analysis, honey are classified as monofloral if the characteristic pollen (
However, some types of pollen (
Each physico-chemical trait has a different discriminatory value for each group of floral honeys. In general, the minimum/maximum values are used as diagnostic tools rather than the average values. For example, the honeys of
Honey is a natural antioxidant food with many beneficial properties (Aljadi & Kamaruddin, 2004; Chua et al., 2013), but the different geographical and botanical origin of honey leads to variable content of phenolic compounds and different antioxidant properties (Dżugan et al., 2018). Overall, the botanical origin of the honey most influences its antioxidant activity, while processing, handling and storage only slightly affect honey antioxidant activity (Beretta et al., 2005; Bertoncelj et al., 2007). In a study carried out by Gül and Pehlivan (2018) in Turkey,
Minerals in honey are divided (Solayman et al., 2016) into main elements (>50 mg/g), trace elements (<50 mg/g) and ultra-trace elements (<1 µg/g). Some trace minerals (
The results of this approach allowed a right-classification as high as 98% (Patrignani et al., 2015). Karabagias et al. (2018) observed that the content of B, Ca, Si, Fe, P, Mn, and Mg in honey samples from Greece, Egypt and Cyprus gave less reliable results (79.4% right-classification success) than by using moisture content, free acidity, electrical conductivity, total dissolved solids, salinity, colour and pH (91.2% of right-classification success). These differences were attributed to honeys’ botanical origin, similarities in soil and vegetation conditions of production areas, harvesting year, honey processing and extraction methods. Louppis et al. (2017) used a combined methodology of mineral profiling (Tab. 1) and the analysis of physico-chemical traits (pH, moisture content, electrical conductivity, free acidity, lactonic acidity, total acidity and ash content) to discriminate the botanical origin of 207 honey samples. The combined approach resulted in satisfactory discriminating performances reaching a 96.1% right botanical classification. Oroian et al. (2015) carried out a study (Tab. 1) in the Suceava, Botoșani and Vaslui regions of Romania and highlighted the mineral profiling as a good approach for the botanical classification of honey (80.8% of right-classified samples) but judged it as unsuitable for the geographical origin (only 21.2% of the samples were rightly classified on the basis of the area of production). Such poor performance was explained by the diffuse occurrence of Fe and Zn that biased subtle differences among the regions under study. Tab. 1 lists other experimental works aimed at studying the mineral profile of honey for botanical anf/or geographical classification.
Studies in which analyses of the mineral profile of honey were carried out to evaluate its geographical (GO) and botanical origin (BO)
Reference | Type of honey | Number of samples | From | Analytical technique | Aim | Elements |
---|---|---|---|---|---|---|
Conti et al. (2007) | 69 | IT | F-AAS, GF-AAS | BO | Na, K, Ca, Mg, Fe, Cu, Mn | |
Vanhanen et al. (2011) | NZ | ICP-OES | GO | Al, As, B, Ca, Cd, Cr, Cu, Fe, K, Mg, Mn, Mo, Na, Ni, P, Pb, S, Zn | ||
Oroian et al. (2015) | 52 | RO | ICP-MS | GO and BO | As, Cd, Cr, Cu, Fe, Hg, Mn, Ni, Pb, Zn | |
Ahmed et al. (2016) | 16 | PK, DE, FR | AAS | GO | Ca, Cd, Co, Cr, Cu, Fe, K, Mg, Mn, Na, 0Ni, Pb, Zn | |
Louppis et al. (2017) | 207 | GR | ICP-AES | BO | Ag, Al, As, B, Ba, Be, Ca, Cd, Co, Cr, Cu, Fe, Hg, Mg, Mn, Mo, Ni, Pb, Sb, Se, Si, Ti, Tl, V and Zn | |
Bontempo et al. (2017) | 265 | IT | ICP-OES | GO and BO | Al, B, Ba, Ca, Cr, Cu, Fe, K, Mg, Mn, Na, Ni, Pb, Rb, Sr, Zn | |
Bilandžić et al. (2017) | Multifloral, |
26 | HR | ICP-MS | BO | Al, CA, Cu, Fe, K, Mg, Mn, Na, Zn, Ag, As, Ba, Be, Cd, Co, Cr, Mo, Ni, Se, Sb, V, U, Th |
Di Rosa et al. (2019) | 28 | IT | ICP-MS | BO | Na, K, Ca, Mg, Fe, Mn, Zn | |
Lanjwani & Channa (2019) | Multifloral | 15 | PK | F-AAS | GO | Fe, Zn, Co, Cu, Mn, Cr, Ni, Pb, Cd, Na, K, Ca, Mg |
Squadrone et al. (2020) | Multifloral | 75 | BALKANS, KZ, SOUTH AMERICA, TZ | ICP-MS | GO and BO | Ag, Al, As, Be, Bi, Cd, Co, Cr, Cu, Fe, Ga, In, Mn, Mo, Ni, Pb, Rb, Sb, Se, Sn, Tl, U, V, Zn |
F-AAS: Flame Atomic Absorption Spectroscopy; GF-AAS: Graphite Furnace Atomic Absorption Spectrometry; ICP-OES: inductively coupled plasma optical emission spectrometer; ICP-MS: Inductively coupled plasma mass spectrometry; AAS: Atomic Absorption Spectroscopy: ICP-AES Inductively Coupled Plasma Atomic Emission Spectroscopy
The ISO 3166-1 alpha-2 standard code was used to indicate the provenance of the samples
Spectroscopy techniques can be used with excellent results to determine the botanical and geographical origin of honey (Noviyanto & Abdulla, 2020). The advantage of using some spectroscopic techniques on honey samples is their low invasiveness and suitability to work with non-manipulated samples (Minaei et al., 2017). The VIS-NIR hyperspectral imaging is a promising approach in this field that involves both spectroscopy and digital imaging (Schaepman, 2007; Minaei et al., 2017). Based on NIR hyperspectral imaging data, Noviyanto and Abdulla (2020) built a classification model using fifty-eight samples of New Zeland honey, that gave an overall balanced accuracy of classification of the botanical origin (90%). Lastra-Mejías et al. (2018) experimented the fluorescence spectroscopy technique on honey (Tab. 2), a economical, easy and fast technique at the same time, which has proved its efficiency for the characterization of a group of forty-four samples of honey with nine different botanical origins and rice syrups (adulterant of honey), with a 95% prediction accuracy.
Studies in which spectroscopic techniques are used for the analysis of honey in order to verify its correct botanical (BO) and geographical (GO) origin and any fraudulent additions
Reference | Type of honey | Number of samples | From | Technique | Statistic appr. | Aim of the work | Results |
---|---|---|---|---|---|---|---|
Schellenberg et al. (2010) | Honeydew, unfloral | 516 | IT, PT, DE, ES, FR, GR, IS, IE, PL, GB, AT, DK. | IRMS | GO | 70% of correct classified samples | |
Zheng et al. (2016) | Unfloral, multifloral | 75 | CN | 1H NMR | PCA, OPLS-DA | GO and BO | Discrimination of sample variations by analysing saccharides and all types of amino acids and organic carboxylic acids |
Minaei et al. (2017) | 8 |
52 | PL | VIS-NIR (range 400–1000 nm) | PCA | BO | Three machine learning algorithms (radial basis function, support vector machine and random forest of which the best achieved 94% accuracy |
Lastra-Mejías et al. (2018) | Unfloral, multifloral | 10 | ES, MX. | Fluorescence spectroscopy | PLS-2 | BO | Simple approach, 95% accuracy |
Chen et al. (2019) | Longan, non-longan | 163 | TH, TW | IRMS | LSD | Anti-fraud | 7% adulterated samples, Differences between Taiwan and Thailand longan honeys |
Suhandy & Yulia (2021) | Durian, multifloral | 400 | ID | UV-VIS spectrometer | PCA | BO | 100% correct discrimination |
Stefas et al. (2021) | Unfloral, multifloral | 45 | LIBS | LDA | Anti-fraud | 2 predictive models with classification accuracy greater than 90% |
IRMS: isotope ratio mass spectrometry; 1H NMR: Proton nuclear magnetic resonance; VIS-NIR: visibly-near-infrared spectroscopy; UV-VIS: visibly-ultraviolet spectroscopy; LIBS: Laser Induced Breakdown Spectroscopy
The ISO 3166-1 alpha-2 standard code was used to indicate the provenance of the samples
The rapid evaporative ionization mass spectrometry analysis (REIMS) is a form of ambient mass spectrometry, which was initially used for
Another way to obtain reliable information on the origins of honey samples is measuring their stable isotopic abundance with the Isotope Ratio Mass Spectroscopy (IRMS). The first application of this techniques was for the identification of honey adulteration by exogenous sugar sources (Zhou et al., 2018; Magdas et al., 2021). Monocotyledonous (some C4 plants) and dicotyledonous (C3) plants have distinctive 13C to 12C carbon isotope ratios that are the result of different photosynthetic cycles. Since most melliferous plants are C3 plants, and both cane and corn, the two main sources of industrial sugar syrups, are C4 plants, authentic honeys are expected to have the characteristic properties of C3 plants rather than those of C4 plants (Souza-Kruliski et al., 2010; Chesson et al., 2013; Soares et al., 2017). Some limitations of this technique are that not all adulterations are easy to identify. In fact, sometimes honey is adulterated using C3 sugars derived from plants such as sugar beet, and in other cases the adulterants in honey are difficult to detect due to the development of new more sophisticated practices (Soares et al., 2017; Tosun, 2013; Wu et al., 2017; Zhou et al., 2018).
Notwithstanding, some authors have proven that IRMS can be applied to the investigation of the honey’s geographical and botanical origins. In fact, some isotopic ratios, especially those of carbon (δ13C) and nitrogen (δ15N), are highly influenced by the botanical origins of honey (Dinca et al., 2015; Bontempo et al., 2017) but also by the length of the growing season of the plants, the sunny days and the mean temperature (Schellenberg et al., 2010). These factors inevitably lead to difference between the isotopic ratios found in a temperate climate zone and the Mediterranean one, hence allowing for geographical discrimination. Bontempo et al. (2017) reported that the botanical origin of honey affects isotopic characteristics by using the IRMS in synergy with the Inductively Coupled Plasma Optical Emission Spectroscopy (ICP-OES), for the mineral profiling, analysing 265 samples (112 multifloral, 60 acacia, 37 chestnut, 18 citrus, 15 rhododendron, 13 eucalyptus and 10 honeydew). Berriel et al. (2019) studied carbon isotopic signature (δ13C) in the protein fraction of forty-seven honey samples from pastures and forty-two from eucalyptus. A logistic regression model was thus developed through which 90% of the samples were correctly assigned, using as variables the δ13C data of protein fraction and the isotopic index of honey. She et al. (2019) evaluated the geographical origin of seventy-one Chinese acacia honey from six different regions by analyzing both the chemico-physical parameters of oligosaccharides using gas chromatography-mass spectrometry, polyphenols with HPLC-MS and carbon isotope using Liquid Chromatography Isotope Ratio Mass Spectrometry. The combination of the thirty-one parameters analysed made it possible to obtain a correct classification rate of 94.12%. Dong et al. (2017) analysed forty-five commercial honey samples of fifteen different botanical origins from seven Chinese regions with the use of IRMS together with element analyzer (EA) and liquid chromatography (LC). Based on the analysis of these parameters, more than 30% of the analyzed samples were considered pure.
In the last decade, studies have been carried out on the omics sciences, with particular regard to the metabolomic and genomic approaches. Metabolomic methods concern the analysis of metabolites, targeted and untargeted, quantitatively or qualitatively (Cevallos-Cevallos et al., 2009; Patti et al., 2012; Cubero-Leon et al., 2014; Cajka et al., 2016; Koulis et al., 2021), and their usefulness has been proven in food traceability and against frauds. The analysis of the amino acid (AA) profile of honey is one of the techniques, and in fact the variation of the amino acid profile could be used to determine a honey’s geographical and botanical source (Anklam, 1998). Hermosín et al. (2003) analysed the amino acids occuring in forty-eight Spanish honey samples of different botanical origins (lavender, eucalyptus, orange blossom, rosemary, thyme, heather, holm oak, forest, oak, multifloral), and the results showed that some unifloral honeys were effectively differentiable, and lavender had the highest tyrosine concentration. Cotte et al. (2004) analysed by HPLC the AA profiles of 280 samples of French honeys from seven different botanical origins (acacia, chestnut, rape, lavender, fir, linden, and sunflower), but the obtained data discriminated only the lavender samples for the selected amino acids histidine, threonine, phenylalanine, proline, and their total content. Janiszewska et al. (2012) investigated the composition of free amino acids, with HPLC, in eighteen Polish honeys of buckwheat, raspberry, acacia, heather, goldenrod and honeydew. Although they identified the honeys using the amino acid profile alone, the main differences were noted in the high amount of proline in the heather honey (387.88 mg/kg) and of aspartic acid in the raspberry and buckwheat honey samples (22.41 mg/kg and 30.07 mg/kg, respectively).
The Nuclear Magnetic Resonance Spectroscopy (NMR) and its metabolomic applications allows for the identification, even simultaneous, of chemical compounds occurring in food (Siddiqui et al., 2017; Consonni & Cagliani, 2019). Schievano et al. (2019) applied NMR with honey organic extracts to distinguish the geographical differences from 234 samples of acacia honey from Italy, eastern Europe and Hungary, which resulted in 100% accurate classification rate. Donarski et al. (2008) used NMR and multivariate data analysis to characterise 182 Corsican and non-Corsican honeys: strawberry, chestnut, autumn scrubland, honeydew scrubland, spring scrubland, summer scrubland, orange clementine and multifloral. In particular, they studied four biomarkers specific for authentic Corsican honey and were able to identify trigonelline (C7H7NO2) as a selective biomarker for non-Corsican honeys. Trigonelline, which it may be indicative for dry and saline habitats. The overall classification rates for the discrimination of Corsican and non-Corsican honeys were 75.8%, 94.5%, 96.2%, respectively by using Partial Least Squares-Linear Discriminant Analysis (PLS-LDA), two-stage Gaussian Process, and Partial Least Squares Gaussian Process (PLS-GP). Zhou et al. (2014) tested a method of liquid chromatography-diode array detection-tandem mass spectrometry (HPLC-DAD-MS/MS) for tracing the botanical origin of honeys. Kaempferol (C15H10O6), morin (C15H10O7) and ferulic acid (C10H10O4) were used as floral markers to distinguish chaste and rapeseed honey. A total of 187 honey samples were identified and separated using the chromatographic fingerprint in use with the similarity analysis (Similarity Evaluation System for Chromatographic Fingerprinting of Traditional Chinese Medicine, Version 2004A). DNA markers from pollens have also been used in recent years to identify accurately plant nuclear material found in honey.
However, the low amounts of pollen DNA in honey and substances interfering with PCR amplification (
Studies in which genomic techniques have been adopted for the analysis of honey, with the respective results obtained
Reference | Type of honey | Provenance | Sequencing | Number of samples | Advantages | Problems |
---|---|---|---|---|---|---|
Bruni et al. (2015) | Multifloral | IT | RbcL and trnH-psbA plastid | 4 | high performances, possibility of replacing mellissopalinological analysis in the future | Need improving plant database |
Soares et al. (2015) | Unifloral/multifloral | PT | - | 4 | Solidity, sensitivity, specificity and less margin of human subjectivity in the analysis of the results | - |
Laha et al. (2017) | Multifloral | IN | NGS | 20 | Need improving plant database | |
Prosser & Hebert (2017) | Mixed | CA/MX | Ion Torrent | 7 | Successful in 5/7 of the samples | Risk of being acted upon by small fraudulent additions of pollen |
Utzeri et al. (2018) | Unifloral/multifloral/honeydrew | MIXED | TrnL-UAA plastid | 9 | Low cost in prospective and more efficacy | - |
Saravanan et al. (2019) | - | IN | 3730XL automated DNA Sequencer | 29 | Quicker, easier than mellissopalinological analysis | Need improving plant barcode database |
RbcL and trnH-psbA: plastid regions as barcode markers; NGS: Next generation sequencing; TrnL-UAA plastid region; 3730XL: DNA ANALYZER (THERMO FISHER)
The ISO 3166-1 alpha-2 standard code was used to indicate the provenance of the samples
This method is also compatible with other methods (
Agricultural traceability requires a large volume of data that must be collected across the supply chain. Early tracking and tracing systems used workers to record information in the field and then manually transferred it to forms or a computer system with the risk of information loss or errors. In recent decades, rapid development in automated processes, as well as in communication technologies, has given rise to the so-called Internet of Things (IoT) paradigm. This rapid evolution of IoT and sensor technology favours the data collection procedure by offering such fast and reliable methods as barcodes, QR codes, RFID and wireless sensor networks (WSN) (Lin et al., 2018; Demestichas et al., 2020). Among them, blockchain technology offers the possibility to create a smarter and more secure supply chain. Traceability information on product origin and possible allergens or added substances, which must be stored in a safe and immutable way, can be established and shared through a collaborative blockchain network among farmers, producers and distributors.
While scientific research on blockchain and traceability in agricultural supply chains is growing rapidly, the same is not reflected in the availability of large-scale commercial applications (Caro et al., 2018). One of the most famous relevant applications is the IBM Food Trust, that was tested with Walmart, a collaboration to trace the origin of Chinese mango and pork products (Kamath, 2018). As a further example, identifying the origin of the mango could take seven days without the Food Trust system, while it takes about 2.2 s with the Food Trust (Demestichas et al., 2020; Kamath, 2018). Also, AgriOpenData aims to trace in a transparent, safe and public manner, the entire agri-food chain and the processing of agricultural products, allowing them to certify their quality (Demestichas et al., 2020). Blockchains increase the transparency of the food supply chain as well as consumer confidence but carry a huge energy and financial cost. It is mandatory for companies to invest much money and time to train all the personnel involved and also to obtain the required equipment, so a cost-benefit analysis is essential (Galvez et al., 2018; Behnke & Janssen, 2020). Concerning honey and bee products, Rünzel et al. (2021) have hypothesized that the blockchain could be used for an open traceability system that includes predictive and verification models on honey yield, an intelligent distribution system based on blockchain technology, the verification of the pollen signature with machine learning algorithms and an information portal accessible to the final customer, all designed for sustainable development and be built and used all over the world. Srivastava & Dashora (2022) explained that the use of this technology is organized for the traceability of honey starting from production, processing, packaging, transport and consumption. Oracle works with IoT technology via the World Hive Network for the fraudulent honey market, and the technology helps to define which beehives were involved in the production of a single honey lot. The information is collected by the Oracle Blockchain platform (Mary Hall, n.d.).
To date, the melissopalynological analysis remains the method more commonly used for investigating the geographical and botanical origin of honey, (Persano Oddo & Bogdanov, 2004; Von Der Ohe et al., 2004). Melissopalynological analysis has also been standardized and is performed according to standard protocols (
This is why a series of methods are used in combination with one another, and the solutions with the most reliable results combine data deriving from different measurements (multicomponent analysis), especially with current statistical data evaluation techniques (Anklam, 1998; Schiassi et al., 2021). The new frontier concerns those methodologies that aim to reduce analysis times, experience and staff, automate processes and have more accurate results (Consonni & Cagliani, 2019). Genomics as well as the others “omics” sciences seem to have interesting perspectives, but preliminary digitization and cataloguing of the plants are necessary. Currently, these methodologies appear to be limited and dependent on other verification methods including melissopalynology, losing the advantages of speed, accuracy, simplicity and cost. In the future, in terms of economic, time and cost savings, analysis will be needed that deals with the control of the adulteration of honey, its origin, authenticity and typicity. Finally, the standardization of the main analysis methods would lead to data homogeneity, more effective product traceability and simplicity in data interpretation.