Establishing authenticity of honey via comprehensive Romanian honey analysis

Application of Spatial Offset Raman Spectroscopy (SORS) and Machine Learning for Sugar Syrup Adulteration Detection in UK Honey

Honey authentication is a complex process which traditionally requires costly and time-consuming analytical techniques not readily available to the producers. This study aimed to develop non-invasive sensor methods coupled with a multivariate data analysis to detect the type and percentage of exogenous sugar adulteration in UK honeys. Through-container spatial offset Raman spectroscopy (SORS) was employed on 17 different types of natural honeys produced in the UK over a season. These samples were then spiked with rice and sugar beet syrups at the levels of 10%, 20%, 30%, and 50% w/w. The data acquired were used to construct prediction models for 14 types of honey with similar Raman fingerprints using different algorithms, namely PLS-DA, XGBoost, and Random Forest, with the aim to detect the level of adulteration per type of sugar syrup. The best-performing algorithm for classification was Random Forest, with only 1% of the pure honeys misclassified as adulterated and <3.5% of adulterated honey samples misclassified as pure. Random Forest was further employed to create a classification model which successfully classified samples according to the type of adulterant (rice or sugar beet) and the adulteration level. In addition, SORS spectra were collected from 27 samples of heather honey (24 Calluna vulgaris and 3 Erica cinerea) produced in the UK and corresponding subsamples spiked with high fructose sugar cane syrup, and an exploratory data analysis with PCA and a classification with Random Forest were performed, both showing clear separation between the pure and adulterated samples at medium (40%) and high (60%) adulteration levels and a 90% success at low adulteration levels (20%). The results of this study demonstrate the potential of SORS in combination with machine learning to be applied for the authentication of honey samples and the detection of exogenous sugars in the form of sugar syrups. A major advantage of the SORS technique is that it is a rapid, non-invasive method deployable in the field with potential application at all stages of the supply chain.

Comprehensive Elemental Profiling of Romanian Honey: Exploring Regional Variance, Honey Types, and Analyzed Metals for Sustainable Apicultural and Environmental Practices

We investigated the mineral concentrations of 61 honey samples from eight Romanian regions, employing advanced techniques to assess 30 chemical elements. Potassium emerged as the dominant element, showcasing significant variations across geographical locations. Essential minerals like calcium, magnesium, sodium, and manganese maintained consistent levels, while zinc, copper, and chromium were present in smaller proportions. Critically, lead and cadmium levels exceeded established safety limits in some samples, suggesting potential environmental contamination. Additionally, elevated levels of lithium, strontium, nickel, and aluminum were detected, hinting at possible atmospheric pollution. These findings highlight the importance of regional analysis, as mineral content varied significantly between locations. Furthermore, correlation analysis revealed interdependencies among elements, suggesting shared environmental influences. Advanced statistical techniques like hierarchical clustering and principal component analysis effectively captured the impact of geographical origin on honey composition. These insights contribute valuable information for future efforts in honey quality control, traceability systems, and regulatory measures. By providing valuable insights into environmental influences on honey composition, this study informs future research endeavors and paves the way for the development of robust regulatory measures to ensure honey safety for consumers.

Accuracy and stability enhanced honey authenticity visual tracing method via false positive-eradicating PCR assisted nucleic acid-capturing lateral flow strip

Honey authenticity guarantee is crucial for consumer health and fair-trading commerce. New visual false-positive-free molecular lateral flow strip (LFS), termed 5'-3' exonuclease activity -directed false positive-eradicating PCR assisted lateral flow strip (FPE-PCR-LFS) was developed. This FPE-PCR-LFS explored the availability of using a signal-probe as the mediator to integrate the efficient amplification module with visual LFS module. With the genomic DNA extracted from target honey, the designed signal probe would be hydrolyzed and exhausted by the 5'-3' exonuclease activity of Taq DNA polymerase in the amplification process. The hydrolyzed signal probe would not be recognized and capture on the T line with only C line of LFS, reflecting the authenticity of the tested honey. And as low as 0.5% authenticity can be accurately identified in commercial honey samples. Significantly, the false-positive-interference was successfully eradicated for the final visual results judgement, which would greatly widen the application of molecular PCR-LFS in various fields.

Composition, functional properties and safety of honey: a review

Honey has been used not only as a food source but also for medicinal purposes. Recent studies have indicated that honey exhibits antioxidant, hepatoprotective, hypolipidemic, hypoglycemic and anti-obesity properties, as well as anticancer, anti-atherosclerotic, hypotensive, neuroprotective and immunomodulatory activities. These health benefits of honey could be attributed to its wide range of nutritional components, including polysaccharides and polyphenols, which have been proven to possess various beneficial properties. It is notable that the composition of honey can also be affected by nectar, season, geography and storage condition. Moreover, the safety of honey requires caution to avoid any potential safety incidents. Therefore, this review aims to provide recent research regarding the chemical composition, biological activities and safety of honey, which might be attributed to comprehensive utilization of honey. © 2023 Society of Chemical Industry.

A comprehensive review of the current trends and recent advancements on the authenticity of honey

The authenticity of honey currently poses challenges to food quality control, thus requiring continuous modernization and improvement of related analytical methodologies. This review provides a comprehensively overview of honey authenticity challenges and related analytical methods. Firstly, direct and indirect methods of honey adulteration were described in detail, commenting the existing challenges in current detection methods and market supervision approaches. As an important part, the integrated metabolomic workflow involving sample processing procedures, instrumental analysis techniques, and chemometric tools in honey authenticity studies were discussed, with a focus on their advantages, disadvantages, and scopes. Among them, various improved microscale extraction methods, combined with hyphenated instrumental analysis techniques and chemometric data processing tools, have broad application potential in honey authenticity research. The future of honey authenticity determination will involve the use of simplified and portable methods, which will enable on-site rapid detection and transfer detection technologies from the laboratory to the industry.

Enabling honey quality and authenticity with NMR and LC-IRMS based platform

Honey has been known for economically motivated adulteration around the world, because of its high demand and short supply. As consequence increasing honey production using the deliberate addition of sugar syrups while claiming a fictitious origin and diversifying it to increase its value. Generally, honey testing is supervised by a set of guidelines and quality parameters to ensure its quality and authenticity. As per the many regulatory bodies, current honey scams have been challenging to identify with conventional methods, so quality control labs require sophisticated technology. With these paradigm shifts, the aim of the present review is focused on the authenticity of honey through two important cutting-edge methods viz LC-IRMS and NMR. The LC-IRMS aids in the detection of added C₃ and C₄ sugars. Whereas NMR has provided a potent solution by allowing the classification of botanical varieties and geographical origin along with the quantification of a set of quality parameters in a single experiment.

Determination of Possible Adulteration and Quality Assessment in Commercial Honey

This study aims to predict several quality traits in commercial honey samples simultaneously and to reveal possible honey adulteration using a field-deployable portable infrared spectrometer without any sample preparation. A total of one hundred and forty-seven commercial honey samples were purchased from local and online markets in Turkey and the United States of America (USA), and their soluble solids (°Brix), pH, free acidity, moisture, water activity (aw), glucose, fructose, sucrose, and hydroxymethyl furfural (HMF) contents were determined using reference methods. The HMF (n = 11 samples) and sucrose (n = 21) concentrations were higher than the regulatory limits in some tested samples. The exceeding HMF content may imply temperature abuse during storage and prolonged storing. On the other hand, high sucrose content may indicate possible adulteration with commercial sweeteners. Therefore, soft independent modeling of class analogies (SIMCA) analysis was conducted to reveal this potential sweetener adulteration in the samples, and the SIMCA model was able to identify all the flagged samples. The suggested FT-IR technique may be helpful in regulatory bodies in determining honey authenticity issues as well as assessing the quality characteristics of honey samples in a shorter period and at a lower cost.

A non-targeted metabolomic strategy for characterization of the botanical origin of honey samples using headspace gas chromatography-ion mobility spectrometry

In this work, characterization of the botanical origin of honey was carried out using headspace gas chromatography coupled to ion mobility spectrometry (HS-GC-IMS). The proposed methodology was applied for the analysis of 89 samples from ten different botanical origins. A total of 15 volatile compounds could be identified, namely, 3-methyl-1-butanol, heptanal, valeraldehyde, octanal, trans-2-hexenal, nonanal, hexanal, benzaldehyde, 2-heptanone, 2-butanone, 2-hexanone, 6-methyl-5-hepten-2-one, 2-pentanone, ethyl acetate and linalool. The analytical method was characterized in terms of limits of detection and quantification, and precision, in order to quantify the identified compounds. Compounds were quantified using the sum of the protonated monomer and proton-bound dimer and logarithmic regression (R² > 0.98), although the establishment of a concentration threshold that would allow creation of classification rules was not possible since there was variability within the group. Consequently, the establishment of chemometric models was necessary. A non-targeted strategy using 275 features is proposed. Orthogonal partial least squares-discriminant analysis (OPLS-DA) allowed the differentiation of five botanical origins: thousand flowers, rosemary, albaida, orange blossom, and "others" (rest of the investigated botanical origins, since a limited number of samples was available). A success validation rate of 100% allowed the classification of 14 honeys with unknown botanical origin.

Accurate and Rapid Genetic Tracing the Authenticity of Floral Originated Honey with the Molecular Lateral Flow Strip

Honey is a natural product and is heavily consumed for its well-known nutritional functions. Honeys with different floral origins possess distinctive flavors, tastes, functions and economic values. It is vital to establish an effective strategy for identifying the authenticity of honey. The intrinsic genetic materials of pollen were adopted as target analytes for the effective identification of honey with floral origins. With an optimized protocol for the rapid gene extraction from honey, target genetic templates were amplified on-site with a portable device. Conveniently, all on-site amplified functional products were easily judged by the designed lateral flow strip (LFS), which was defined as the molecular LFS in this research. Additionally, the entire on-site genetic authentication of honey was completed in less than 2 h by visual observation. Commercial honey products have been successfully identified with excellent accuracy. This low-cost, high-efficiency and easy-operational strategy will greatly benefit the quality guarantee of foods with specific functions and geographical markers.

LC-HRMS-Based Non-Targeted Metabolomics for the Assessment of Honey Adulteration with Sugar Syrups: A Preliminary Study

Honey is a natural product that is in great demand and has a relatively high price, thus making it one of the most common targets of economically motivated adulteration. Its adulteration can be obtained by adding cheaper honey or sugar syrups or by overfeeding honeybees with sugar syrups. Adulteration techniques are constantly evolving and advanced techniques and instruments are required for its detection. We used non-targeted metabolomics to underscore potential markers of honey adulteration with sugar syrups. The metabolomic profiles of unadulterated honeys and sugar beet, corn and wheat syrups were obtained using hydrophilic interaction liquid chromatography high-resolution mass spectrometry (LC-HRMS). The potential markers have been selected after data processing. Fortified honey (5%, 10% and 20%), honey obtained from overfeeding, and 58 commercial honeys were analyzed. One potential marker appeared with a specific signal for syrups and not for honey. This targeted analysis showed a linear trend in fortified honeys with a calculated limit of quantification around 5% of fortification.

Comparative Metabolomics Study of Chaenomeles speciosa (Sweet) Nakai from Different Geographical Regions

Chaenomeles speciosa (Sweet) Nakai (C. speciosa) is not only a Chinese herbal medicine but also a functional food widely planted in China. Its fruits are used to treat many diseases or can be processed into food products. This study aims to find key metabolic components, distinguish the differences between geographical regions and find more medicinal and edible values of C. speciosa fruits. We used ultra-high-performance liquid chromatography–tandem mass spectrometry (UHPLC-MS/MS) and widely targeted metabolomics analysis to reveal key and differential metabolites. We identified 974 metabolites and screened 548 differential metabolites from 8 regions. We selected significantly high-content differential metabolites to visualize a regional biomarker map. Comparative analysis showed Yunnan had the highest content of total flavonoids, the highest amounts of compounds related to disease resistance and drug targets and the most significant difference from the other regions according to the Traditional Chinese Medicine Systems Pharmacology Database and Analysis Platform database, a unique platform for studying the systematic pharmacology of Chinese herbal medicine and capturing the relationship between drugs, targets and diseases. We used oral bioavailability (OB) ≥ 30% and drug likeness (DL) ≥ 0.18 as the selection criteria and found 101 key active metabolites, which suggests that C. speciosa fruits were rich in healthy metabolites. These results provide valuable information for the development of C. speciosa.

Detection of adulterations in a valuable Brazilian honey by using spectrofluorimetry and multiway classification

Spectrofluorimetry combined with multiway chemometric tools were applied to discriminate pure Aroeira honey samples from samples adulterated with corn syrup, sugar cane molasses and polyfloral honey. Excitation emission spectra were acquired for 232 honey samples by recording excitation from 250 to 500 nm and emission from 270 to 640 nm. Parallel factor analysis (PARAFAC), partial least squares discriminant analysis (PLS-DA), unfolded PLS-DA (UPLS-DA) and multilinear PLS-DA (NPLS-DA) methods were used to decompose the spectral data and build classification models. PLS-DA models presented poor classification rates, demonstrating the limitation of the traditional two-way methods for this dataset, and leading to the development of three-way classification models. Overall, UPLS-DA provided the best classification results with misclassification rates of 4% and 8% for the training and test sets, respectively. These results showed the potential of the proposed method for routine laboratory analysis as a simple, reliable, and affordable tool.

Determination of Floral Origin Markers of Latvian Honey by Using IRMS, UHPLC-HRMS, and 1H-NMR

The economic significance of honey production is crucial; therefore, modern and efficient methods of authentication are needed. During the last decade, various data processing methods and a combination of several instrumental methods have been increasingly used in food analysis. In this study, the chemical composition of monofloral buckwheat (Fagopyrum esculentum), clover (Trifolium repens), heather (Calluna vulgaris), linden (Tilia cordata), rapeseed (Brassica napus), willow (Salix cinerea), and polyfloral honey samples of Latvian origin were investigated using several instrumental analysis methods. The data from light stable isotope ratio mass spectrometry (IRMS), ultra-high performance liquid chromatography coupled with high-resolution mass spectrometry (UHPLC-HRMS), and nuclear magnetic resonance (NMR) analysis methods were used in combination with multivariate analysis to characterize honey samples originating from Latvia. Results were processed using the principal component analysis (PCA) to study the potential possibilities of evaluating the differences between honey of different floral origins. The results indicate the possibility of strong differentiation of heather and buckwheat honeys, and minor differentiation of linden honey from polyfloral honey types. The main indicators include depleted δ¹⁵N values for heather honey protein, elevated concentration levels of rutin for buckwheat honey, and qualitative presence of specific biomarkers within NMR for linden honey.

Physicochemical, antioxidant and FTIR-ATR spectroscopy evaluation of Kashmiri honeys as food quality traceability and Himalayan brand

Three types of honeys viz. Robinia pseudoacacia (RSA), Plectranthus rugosus (PR) and multifloral honey (MF) from Kashmir Himalayas of India were characterized on the basis of physicochemical, antioxidant and FTIR-ATR spectroscopic features. The physicochemical parameters indicated that ash content, electrical conductivity, redness (a*) and yellowness (b*) were higher in MF honey, moisture content, Hydroxymethylfurfural (HMF) and lightness (L*) in PR honey and Water activity (a_w) and proline content in RSA honey (P ≤ 0.05). Significantly higher values of total sugar, total reducing sugar and sucrose content was observed in MF honey (P ≤ 0.05). The total phenolic content (TPC), total flavonoids, ferric reducing antioxidant power and 1, 1-diphenyl-2-picrylhydrazy-IC₅₀ (DPPH- IC₅₀) free radical scavenging activity showed that MF honey had significantly higher antioxidant activity (P ≤ 0.05). ATR-FTIR analysis demonstrated that the absence of peaks at broad band around 1653.454-1637.656 cm^-1 in MF honey, 1185.989-1153.072 cm^-1 and 918.570-916.544 cm^-1 wavenumbers in PR honey thus clearly discriminating different types of honeys. The results revealed that physico-chemical, antioxidant and spectroscopic characteristics of honey types vary with geographic origin and their floral sources and MF honey have a high therapeutic potential.

Bioactive Molecules for Discriminating Robinia and Helianthus Honey: High-Performance Liquid Chromatography-Electron Spray Ionization-Mass Spectrometry Polyphenolic Profile and Physicochemical Determinations

Bioactive molecules from the class of polyphenols are secondary metabolites from plants. They are present in honey from nectar and pollen of flowers from where honeybees collect the “raw material” to produce honey. Robinia pseudoacacia and Helianthus annuus are important sources of nectar for production of two monofloral honeys with specific characteristics and important biological activity. A high-performance liquid chromatography–electro spray ionization–mass spectrometry (HPLC–ESI–MS) separation method was used to determine polyphenolic profile from the two types of Romanian unifloral honeys. Robinia and Helianthus honey showed a common flavonoid profile, where pinobanksin (1.61 and 1.94 mg/kg), pinocembrin (0.97 and 1.78 mg/kg) and chrysin (0.96 and 1.08 mg/kg) were identified in both honey types; a characteristic flavonoid profile in which acacetin (1.20 mg/kg), specific only for Robinia honey, was shown; and quercetin (1.85 mg/kg), luteolin (21.03 mg/kg), kaempferol (0.96 mg/kg) and galangin (1.89 mg/kg), specific for Helianthus honey, were shown. In addition, different phenolic acids were found in Robinia and Helianthus honey, while abscisic acid was found only in Robinia honey. Abscisic acid was correlated with geographical location; the samples collected from the south part of Romania had higher amounts, due to climatic conditions. Acacetin was proposed as a biochemical marker for Romanian Robinia honey and quercetin for Helianthus honey.

A Rheological Analysis of Biomaterial Behaviour as a Tool to Detect the Dilution of Heather Honey

Heather honey is a valuable and rheologically special type of honey. Its above-average selling price may motivate its intentional violation with a mixture of honey from another botanical origin, the price of which is lower on the market. This work deals with the rheological properties of such devalued heather honey in order to determine the changes in the individual rheological parameters depending on the degree of dilution of the heather honey. For this purpose, a differently diluted heather honey sample series was created and the following rheological parameters were determined: hysteresis area, n-value, yield stress (τ₀), parameter B (Weltman model), parameter ϕ, or parameter C (model describing the logarithmic dependence of the complex viscosity on the angular frequency). Part of the work was research into whether the set parameters can be used as comparative parameters. It was found that the hysteresis area does not appear to be a suitable relative comparison parameter due to the high variability. The parameters that appear to be suitable are the relative parameters n-value and the parameter ϕ, which showed the greatest stability. The change in the determined rheological parameters is, depending on the degree of dilution, non-linear with a step change between the samples containing 40% (w/w) and 60% (w/w) of a heather honey.

Evaluation of honey authenticity in Lebanon by analysis of carbon stable isotope ratio using elemental analyzer and liquid chromatography coupled to isotope ratio mass spectrometry

Honey is one of the most valuable sweeteners consumed by humans all over the world. Consequently, it is often a target for adulteration through the addition of different sugar syrups during or after honey production, resulting in a reduction in its nutritive value. For the first time, this study analyzes honey samples of various botanical species collected from different Lebanese regions using element analyzer (EA) and liquid chromatography (LC) coupled with isotope ratio mass spectrometry (IRMS). The δ13 C of bulk honey, its protein fraction, and the main individual sugars (glucose, fructose, disaccharides, and trisaccharide) were determined, in order to characterize and evaluate the authenticity of honey consumed in Lebanon. The results showed that the δ13 C values for bulk honey and its protein range from -26.5‰ to -24.5‰ and from -26.4‰ to -24.7‰, respectively, for authentic samples. δ13 C values for samples adulterated with sugar syrups range from -11.2‰ to -25.1‰ for bulk honey and from -26.6‰ to -23.7‰ for its proteins, with a difference between bulk and protein values between -1 and -8.7‰. Using LC-C-IRMS techniques, the δ13 C of individual sugars provides additional information on the presence of undeclared sugars. We found that all authentic samples had Δδ13 Cf-g and Δδ13 C max values within the naturally occurring range of ±1‰ and ±2.1‰, respectively, while the adulterated samples fall outside the Δδ13 C ranges. The oligosaccharide peak was detected in most adulterated samples.

Discrimination of Tunisian Honey by Mineral and Trace Element Chemometrics Profiling

The concentrations of 19 chemical elements have been determined in 36 honey samples of different botanical (wildflower, eucalyptus, eucalyptus red flowers, prickly pears, lemon blossom, thyme, almond, rosemary and jujube) honeys from the three geographical areas of Tunisia (Sidi Bouzid, Nabeul and Sfax) using inductively coupled plasma mass spectrometry (ICP-MS). The aim of this work was to use the multielement analysis together with chemometric tools to verify the botanical and the geographical origin of honeys. The correlation on the basis of mineral element content between the honey samples and their botanical and/or geographical origins was in some measure achieved. The data collected on the samples were also used to evaluate the nutritional quality and the potential health risks associated with elements via consumption of the Tunisian honey. According to the results obtained, the intake of essential elements was small, and the potential health risks associated with toxic or potentially toxic elements via consumption of this food were overall insignificant.

Honey authenticity: analytical techniques, state of the art and challenges

Honey is a high-value, globally consumed, food product featuring a high market price strictly related to its origin. Moreover, honey origin has to be clearly stated on the label, and quality schemes are prescribed based on its geographical and botanical origin. Therefore, to enhance food quality, it is of utmost importance to develop analytical methods able to accurately and precisely discriminate honey origin. In this study, an all-time scientometric evaluation of the field is provided for the first time using a structured keyword on the Scopus database. The bibliometric analysis pinpoints that the botanical origin discrimination was the most studied authenticity issue, and chromatographic methods were the most frequently used for its assessment. Based on these results, we comprehensively reviewed analytical techniques that have been used in honey authenticity studies. Analytical breakthroughs and bottlenecks on methodologies to assess honey quality parameters using separation, bioanalytical, spectroscopic, elemental and isotopic techniques are presented. Emphasis is given to authenticity markers, and the necessity to apply chemometric tools to reveal them. Altogether, honey authenticity is an ever-growing field, and more advances are expected that will further secure honey quality.

Detection of acacia honey adulteration with high fructose corn syrup through determination of targeted α‑Dicarbonyl compound using ion mobility-mass spectrometry coupled with UHPLC-MS/MS

High-value acacia honey is often adulterated with inexpensive high fructose corn syrup (HFCS), due to their similar color and sugar composition. α‑Dicarbonyl compounds formed by Maillard reaction or caramelization during heat treatment or storage, differ between HFCS and honey due to differences in starting materials and processing methods. In this study, we compared α-dicarbonyl compounds in acacia honey and HFCS by Ion Mobility-Mass Spectrometry and multivariate statistical analysis. Through α-dicarbonyl compound derivatization with o-phenylenediamine, we screened a marker with 189.1023 m/z and 139.3 Å² Collision Cross-Section that can distinguish HFCS from acacia honey. Nuclear magnetic resonance spectra identified this marker compound as 3,4-dideoxypentosulose. We then used chromatography-coupled tandem mass spectrometry to quantitate 3,4-dideoxypentosulose in market samples of honey and HFCS and found that 3,4-dideoxypentosulose was negligible (<0.098 mg/kg) in honey, but prevalent in HFCS (≧1.174 mg/kg), indicating 3,4-dideoxypentosulose can serve as an alternative indicator of HFCS adulteration of acacia honey.

Sugar Profiling of Honeys for Authentication and Detection of Adulterants Using High-Performance Thin Layer Chromatography

Honey adulteration, where a range of sugar syrups is used to increase bulk volume, is a common problem that has significant negative impacts on the honey industry, both economically and from a consumer confidence perspective. This paper investigates High-Performance Thin Layer Chromatography (HPTLC) for the authentication and detection of sugar adulterants in honey. The sugar composition of various Australian honeys (Manuka, Jarrah, Marri, Karri, Peppermint and White Gum) was first determined to illustrate the variance depending on the floral origin. Two of the honeys (Manuka and Jarrah) were then artificially adulterated with six different sugar syrups (rice, corn, golden, treacle, glucose and maple syrup). The findings demonstrate that HPTLC sugar profiles, in combination with organic extract profiles, can easily detect the sugar adulterants. As major sugars found in honey, the quantification of fructose and glucose, and their concentration ratio can be used to authenticate the honeys. Quantifications of sucrose and maltose can be used to identify the type of syrup adulterant, in particular when used in combination with HPTLC fingerprinting of the organic honey extracts.

Vibrational Spectroscopy Coupled to a Multivariate Analysis Tiered Approach for Argentinean Honey Provenance Confirmation

In the present work, the provenance discrimination of Argentinian honeys was used as case study to compare the capabilities of three spectroscopic techniques as fast screening platforms for honey authentication purposes. Multifloral honeys were collected among three main honey-producing regions of Argentina over four harvesting seasons. Each sample was fingerprinted by FT-MIR, NIR and FT-Raman spectroscopy. The spectroscopic platforms were compared on the basis of the classification performance achieved under a supervised chemometric approach. Furthermore, low- mid- and high-level data fusion were attempted in order to enhance the classification results. Finally, the best-performing solution underwent to SIMCA modelling with the purpose of reproducing a food authentication scenario. All the developed classification models underwent to a "year-by-year" validation strategy, enabling a sound assessment of their long-term robustness and excluding any issue of model overfitting. Excellent classification scores were achieved by all the technologies and nearly perfect classification was provided by FT-MIR. All the data fusion strategies provided satisfying outcomes, with the mid- and high-level approaches outperforming the low-level data fusion. However, no significant advantage over the FT-MIR alone was obtained. SIMCA modelling of FT-MIR data produced highly sensitive and specific models and an overall prediction ability improvement was achieved when more harvesting seasons were used for the model calibration (86.7% sensitivity and 91.1% specificity). The results obtained in the present work suggested the major potential of FT-MIR for fingerprinting-based honey authentication and demonstrated that accuracy levels that may be commercially useful can be reached. On the other hand, the combination of multiple vibrational spectroscopic fingerprints represents a choice that should be carefully evaluated from a cost/benefit standpoint within the industrial context.

Geographical origin and botanical type honey authentication through elemental metabolomics via chemometrics

The trace and rare earth elements content of 93 honeys of different botanical type and origin have been studied through ICP-MS. Discriminant Analysis (DA) was successful for botanical type and geographical origin classification while Cluster Analysis (CA) was successful only for botanical type. Through Probabilistic Neural Network (PNN) analysis, 85.3% were correctly classified by the network according to their geographical origin and 73.3% according to their organic characterization. A Partial Least Squares (PLS) model was constructed, giving a prediction accuracy of more than 95%. Information obtained using Rare Earths (Y, La, Ce, Pr, Nd, Sm, Eu, Gd, Tb, Dy, Ho, Er, Tm, Yb, Lu) and trace elements (Li, Mg, Mn, Ni, Co, Cu, Sr, Ba, Pb) via chemometric evaluation facilitated classification of honey samples.