The Importance of Testing the Quality and Authenticity of Food Products: The Example of Honey

Identification of botanical marker candidates for buckwheat honey using a non-targeted approach based on liquid chromatography coupled with high resolution mass spectrometry

Novel authentication tools are needed to determine unambiguously the botanical or geographical origin of food products such as honeys. In this study, a non-targeted workflow was developed to discover and identify authenticity markers for buckwheat honey based on a 'dilute-and-shoot' approach using liquid chromatography coupled with high resolution mass spectrometry (LC-HRMS). A PLS-DA model was built using data obtained for 147 honeys whose characteristics had been checked with an orthogonal method (pollen analysis). The resulting model was able to distinguish buckwheat honeys from 13 other types of honey with a sensitivity of 100 %. Thirteen molecular features were identified as candidate markers for buckwheat honey, including three confirmed threshold markers: 4-hydroxybenzaldehyde, 2-hydroxypyridine and hydroxyquinoline-3‑carbonitrile. 2-Hydroxypyridine and hydroxyquinoline-3‑carbonitrile were first time reported as botanical marker candidates for buckwheat honey, highlighting the capacity of this approach to discover and identify novel candidate markers of authenticity. The present analytical workflow can be used, alone or in combination with other analytical techniques, to strengthen honey authentication.

Robust DEEP heterogeneous ensemble and META-learning for honey authentication

Food fraud raises significant concerns to consumer health and economic integrity, with the adulteration of honey by sugary syrups representing one of the most prevalent forms of economically motivated adulteration. This study presents a novel framework that combines data from multiple analytical techniques with specialized deep learning models (convolutional neural networks), integrated via meta-learning, in order to differentiate between pure honey and samples adulterated with sugar cane molasses, glucose syrup, or caramel-flavored ice cream topping. Unlike traditional chemometric methods, this approach expands the input feature space, leading to enhanced predictive performance. The resulting deep heterogeneous ensemble learner exhibited considerable generalization capability, achieving an average classification accuracy of 98.53 % and a Matthews correlation coefficient of 0.9710. Furthermore, the ensemble demonstrated exceptional robustness, maintaining an accuracy of 73 %, even when 90 % of the input data were corrupted, underscoring its unparalleled capacity to generalize under both subtle and extreme data variability. This adaptable and scalable solution underscores the transformative potential of ensemble-meta-learning strategy for addressing complex challenges in analytical chemistry. The model, its constituents and other additional resources were made available in an open repository.

The Use of Fluorescence Spectrometry Combined with Statistical Tools to Determine the Botanical Origin of Honeys

At a time when the botanical origin of honey is being increasingly falsified, there is a need to find a quick, cheap and simple method of identifying its origin. Therefore, the aim of our work was to show that fluorescence spectrometry, together with statistical analysis, can be such a method. In total, 108 representative samples with 10 different botanic origins (9 unifloral and 1 multifloral), obtained in 2020-2022 from local apiaries, were analyzed. The fluorescence spectra of those samples were determined using a F-7000 Hitachi fluorescence spectrophotometer, Tokyo, Japan. It is shown that each honey variety produces a unique emission spectrum, which allows for the determination of its botanical origin. Taking into account the difficulties in analyzing these spectra, it was found that the most information regarding botanical differences and their identification is provided by synchronous cross-sections of these spectra obtained at Δλ = 100 nm. In addition, this analysis was supported by discriminant and canonical analysis, which allowed for the creation of mathematical models, allowing for the correct classification of each type of honey (except dandelion) with an accuracy of over 80%. The application of the method is universal (in accordance with the methodology described in this paper), but its use requires the creation of fluorescence spectral matrices (EEG) characteristic of a given geographical and botanical origin.

Multimodal Interference-Based Fiber Optic Sensors for Glucose and Moisture Content Detection in Honey

Fiber optic sensors (FOSs) have transformed industrial applications with their high sensitivity and precision, especially in real-time monitoring. This study presents a fiber optic sensor based on multimodal interference (MMI) applied to detect honey adulteration. The sensor is built using a non-core multimode fiber (NC-MMF) segment spliced between two standard single-mode fibers (SMFs). We focus on reporting the detection of two main adulterants in honey that modify its refractive index (RI): the presence of glucose and moisture content. Detailed testing was performed with two commercially approved honey brands, named A and B. The sensor successfully detected glucose concentrations from 1% to 5% and moisture content from 0% to 20% for both brands. For glucose detection, we obtained sensitivity values −0.55457 nm/% for brand A and −2.61257 nm/% for brand B. In terms of moisture content in honey, we observed a sensitivity around −0.3154 nm/% and −0.3394 nm/% for brands A and B, respectively. Additionally, temperature tests were performed, showing that the sensor works optimally up to 30 °C. The results were validated using a conventional refractometer, showing a close agreement with the data obtained and confirming the reliability and accuracy of the proposed sensor. Compared to other refractometers, the MMI sensor offers advantages such as real-time monitoring, ease of assembly, cost-effectiveness, and minimal maintenance. Furthermore, the sensor represents an alternative tool to guarantee the quality and authenticity of honey, overcoming the limitations of conventional measurement techniques.

Identifying Key Markers for Monofloral (Eucalyptus, Rosemary, and Orange Blossom) and Multifloral Honey Differentiation in the Spanish Market by UHPLC-Q-Orbitrap-High-Resolution Mass Spectrometry Fingerprinting and Chemometrics

Honey differentiation based on the botanical origin is crucial to guarantee product authenticity, especially considering the increasing number of fraud cases. This study assessed the metabolomic differences arising from various botanical origins in honey products sold in Spanish markets, focusing on two goals: (1) discrimination within monofloral samples (eucalyptus, rosemary, and orange blossom honey) and (2) differentiation between multifloral vs. monofloral honey samples. An omics strategy based on ultra-high-performance liquid chromatography coupled with quadrupole-Orbitrap-high-resolution mass spectrometry (UHPLC-Q-Orbitrap-HRMS) was applied for the reliable identification of specific honey markers selected by orthogonal partial least squares discriminant analysis (OPLS-DA) (R2Y = 0.929-0.981 and Q2 = 0.868-0.952), followed by the variable importance in projection (VIP) approach. Key amino acid, alkaloid, and trisaccharide markers were identified to distinguish between honey samples. Some Amadori compounds were highlighted as eucalyptus honey markers, suggesting their potential use for honey aging and botanical origin differentiation. L-phenylalanine and raffinose were markers of rosemary honey. Four markers (e.g., trigonelline, L-isoleucine, and N-(1-deoxy-1-fructosyl)isoleucine) were found in higher levels in multifloral samples, indicating a greater availability of amino acids, potentially increasing the Maillard reaction. This research is the first to address the botanical origin's impact on honey by identifying novel markers not previously described.

Assessing the Impact of Botanical Origins, Harvest Years, and Geographical Variability on the Physicochemical Quality of Serbian Honey

This study summarized the physicochemical analysis of 609 honey samples originating from the Republic of Serbia. Variations among honey samples from different botanical origins, regions of collections, and harvest years were exposed to descriptive statistics and correlation analysis that differentiated honey samples. Furthermore, most of the observed physicochemical parameters (glucose, fructose, sucrose content, 5-hydroxymethylfurfural (5-HMF) levels, acidity, and electrical conductivity) varied significantly among different types of honey, years, and regions. At the same time, no noticeable difference was found in diastase activity, moisture content, and insoluble matter. Based on the obtained results, 22 honey samples could be considered adulterated, due to the irregular content of sucrose, 5-HMF, acidity, and diastase activity. In addition, 64 honey samples were suspected to be adulterated. Adulterated and non-compliant samples present a relatively low percentage (14.1%) of the total number of investigated samples. Consequently, a considerable number of honey samples met the required standards for honey quality. Overall, these findings provide insights into compositional and quality differences among various types of honey, aiding in understanding their characteristics and potential applications.

Impact of Different Adulterants on Honey Quality Properties and Evaluating Different Analytical Approaches for Adulteration Detection

The study was carried out keeping in view the recently emerging concern of adulteration of natural honey on the honey markets. This study intended to investigate honey adulteration detection using physical and chemical composition to achieve a foreign component (a marker) that is present in the honey that confirms either the adulteration or authenticity of the honey. The technique was evaluated on honey samples that were 5-50% adulterated with various common adulterants in Ethiopia. Preliminary quick tests and characterization of physicochemical and antioxidant properties were tested as alternative analytical approaches for honey adulteration detection. Preliminary quick test methods were used to detect adulterated honey, but these methods were found specific to adulterant materials. The proline and pH levels decreased as molasses, sugar, and banana adulterants increased, while increased as melted candy and shebeb adulterants increased. Moisture content decreased as sugar, melted candy, and shebeb adulterants were increased, while decreased as molasses and banana adulterants increased. HMF content increased as molasses, melted candy, and shebeb adulterants were increased. The sugar compositions are key differential criteria to detect the adulteration of honey with sugar. Based on their physical characteristics, PCA demonstrated a considerable difference between samples of pure and contaminated honey. In conclusion, it was observed that honey adulteration was detected based on significant deviations of physicochemical and biochemical components from expected values in the concentration of naturally occurring components. This study successfully demonstrated a method to rapidly and accurately classify and authenticate honey. Accordingly, it is recommended that frequent training for stakeholders on adulteration detection methods should be carried out to avoid adulteration of honey from the markets.

Sensitive immunoenzyme assay for the detection of antibiotic flumequine in honey

Fluoroquinolone antibiotics are used to cure and protect bees and apiaries from infections. Consequently, they may contaminate honey and other products of beekeeping. In this study, a highly sensitive immunoenzyme assay (EIA) was for the first time developed for the determination of a fluoroquinolone flumequine (FLU) in honey. The EIA was carried out in an indirect competitive format with colorimetric detection. The analysis was characterized by a low limit of detection of 30 pg mL-1. The polyclonal antibodies used showed no cross-reactivity with 24 other (fluoro)quinolones; the assay was highly specific only toward FLU. Different coating FLU-protein conjugates were tested to achieve the most sensitive competitive immunodetection. A highly simplified and rapid (3-5 min) sample preparation was proposed based on the 100-300 times dilution of honey by a buffer. The developed EIA has been tested to detect FLU in honey of different origins, namely acacia, flower, buckwheat, chestnut, and linden honey. It has been demonstrated that 76.2-115.9% of FLU could be determined by the assay. The versatility, simplicity, and rapidity of the EIA enable us to propose this method as an effective tool to control the contamination of honey.

Reinforce Bee Product Quality Evaluation to Protect Human Health

The quality of bee products is directly related to the health of consumers [...].