Detecting adulteration of stingless bee honey using untargeted 1H NMR metabolomics with chemometrics

Exploring Bahia's coffee diversity: NMR spectroscopy and chemometrics unveil regional variations

Coffee, a globally popular beverage, has Brazil as a leading producer, with Bahia being the fourth largest producer in the country. Within Bahia, the Chapada Diamantina and Vitória da Conquista Plateau micro-regions are renowned for their high-quality gourmet coffees. This study utilized NMR spectroscopy combined with chemometric analysis to compare the chemical compositions of coffee from these regions. The analysis identified significant differences: Vitória da Conquista Plateau coffees had higher intensities for formic acid, fumaric acid, N-methylpyridinium, and methyl nicotinate, whereas Chapada Diamantina coffees showed higher intensities for acrylamide, caffeic acid, and trigonelline. These findings highlight regional variations in organoleptic properties. Classification methods k-NN, SIMCA, and PLS-DA effectively distinguished coffees from these micro-regions, demonstrating the potential of NMR and chemometrics for regional coffee differentiation.

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.

Advances in High-Resolution Mass Spectrometry-Based Metabolomics: Applications in Food Analysis and Biomarker Discovery

Consumer concerns regarding food nutrition and quality are becoming increasingly prevalent. High-resolution mass spectrometry (HRMS)-based metabolomics stands as a cutting-edge and widely embraced technique in the realm of food component analysis and detection. It boasts the capability to identify character metabolites at exceedingly low abundances, which remain undetectable by conventional platforms. It can also enable real-time monitoring of the flux of targeted compounds in metabolic synthesis and decomposition. With the emergence of artificial intelligence and machine learning, it has become more convenient to process the vast data sets of metabolomics and identify biomarkers. The review summarizes the latest applications of HRMS-based metabolomics platforms in traditional foods, novel foods, and pharmaceutical-food homologous matrices. It compares the suitability of HRMS to nuclear magnetic resonance (NMR) in metabolomics across three dimensions and discusses the principles and application scenarios of various mass spectrometry technologies.

A review of the emerging technologies and systems to mitigate food fraud in supply chains

Food fraud has serious consequences including reputational damage to businesses, health and safety risks and lack of consumer confidence. New technologies targeted at ensuring food authenticity has emerged and however, the penetration and diffusion of sophisticated analytical technologies are faced with challenges in the industry. This review is focused on investigating the emerging technologies and strategies for mitigating food fraud and exploring the key barriers to their application. The review discusses three key areas of focus for food fraud mitigation that include systematic approaches, analytical techniques and package-level anti-counterfeiting technologies. A notable gap exists in converting laboratory based sophisticated technologies and tools in high-paced, live industrial applications. New frontiers such as handheld laser-induced breakdown spectroscopy (LIBS) and smart-phone spectroscopy have emerged for rapid food authentication. Multifunctional devices with hyphenating sensing mechanisms together with deep learning strategies to compare food fingerprints can be a great leap forward in the industry. Combination of different technologies such as spectroscopy and separation techniques will also be superior where quantification of adulterants are preferred. With the advancement of automation these technologies will be able to be deployed as in-line scanning devices in industrial settings to detect food fraud across multiple points in food supply chains.

Rapid and sensitive approaches for detecting food fraud: A review on prospects and challenges

Precise and reliable analytical techniques are required to guarantee food quality in light of the expanding concerns regarding food safety and quality. Because traditional procedures are expensive and time-consuming, quick food control techniques are required to ensure product quality. Various analytical techniques are used to identify and detect food fraud, including spectroscopy, chromatography, DNA barcoding, and inotrope ratio mass spectrometry (IRMS). Due to its quick findings, simplicity of use, high throughput, affordability, and non-destructive evaluations of numerous food matrices, NI spectroscopy and hyperspectral imaging are financially preferred in the food business. The applicability of this technology has increased with the development of chemometric techniques and near-infrared spectroscopy-based instruments. The current research also discusses the use of several multivariate analytical techniques in identifying food fraud, such as principal component analysis, partial least squares, cluster analysis, multivariate curve resolutions, and artificial intelligence.

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.

Rapid evaluation of Ziziphi Spinosae Semen and its adulterants based on the combination of FT-NIR and multivariate algorithms

Ziziphi Spinosae Semen (ZSS) is a valued seed renowned for its sedative and sleep-enhancing properties. However, the price increase has been accompanied by adulteration. In this study, chromaticity analysis and Fourier transform near-infrared (FT-NIR) combined with multivariate algorithms were employed to identify the adulteration and quantitatively predict the adulteration ratio. The findings suggested that the utilization of chromaticity extractor was insufficient for identification of adulteration ratio. The raw spectrum of ZMS and HAS adulterants extracted by FT-NIR was processed by SNV + CARS and 1d + SG + ICO respectively, the average accuracy of machine learning classification model was improved from 77.06 % to 97.58 %. Furthermore, the R2 values of the calibration and prediction set of the two quantitative prediction regression models of adulteration ratio are greater than 0.99, demonstrating excellent linearity and predictive accuracy. Overall, this study demonstrated that FT-NIR combined with multivariate algorithms provided a significant approach to addressing the growing issue of ZSS adulteration.

Detection of barley malt syrup as an adulterant in honey by 1H NMR profile

Currently, Barley Malt Syrup (BMS) is one of the forms of growing adulteration in honey. However, there have been no reports regarding its identification by NMR. In this aspect, we proposed a 1H NMR profiling method to discriminate between authentic and honey adulterated with BMS. The authenticated honey samples were artificially adulterated with varying percentages of BMS. It was found that a marker peak primarily falling around the 5.40 ppm region exhibited discrimination between pure and adulterated samples. Furthermore, NMR data of the samples were analyzed using statistical models. The findings demonstrate that NMR sugar profiles region, when combined with PCA analysis, can effectively detect varying degrees of adulteration. Despite qualitative nature of the outcomes, spiking studies have revealed that approach can reliably identify sugar addition at levels as low as 5-10%. Overall, NMR-based approach proves to be effective in detecting BMS as an adulterant in honey.

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.

Ultraviolet-visible spectroscopy combined with machine learning as a rapid detection method to the predict adulteration of honey

Honey is often adulterated with inexpensive and artificial sweeteners. To overcome the time-consuming honey adulteration tests, which require precision, chemicals, and sample preparation, it is needful to develop trustworthy analytical methods to assure its authenticity. In the present study, the potential of ultraviolet-visible spectroscopy (UV-Vis) in predicting the sucrose content was evaluated by using Support Vector Regression (SVR) and Partial Least Square Regression (PLSR). To predict the sucrose content based on diagnostic wavelengths, a Point Spectro Transfer Function (PSTF) was evaluated using Multiple Linear Regression (MLR). For this purpose, the spectra of authentic (n = 12), commercial (n = 12), and adulterated (n = 16) honey samples were recorded. Four distinguished wavelengths from correlation analysis between sucrose content and spectra absorption were 216, 280, 316, and 603 nm. The SVR performed better calibration model than the PLSR estimations (RMSE = 0.97, and R2 = 0.98). The predictive models result revealed that both models had high accuracy for the sucrose content estimation. This study proved that UV-Vis spectroscopy provides an economical alternative for the rapid quantification of adulterated honey samples with sucrose.

1H NMR-based metabolomics combined with chemometrics to detect edible oil adulteration in huajiao (Zanthoxylum bungeanum Maxim.)

Huajiao is a highly valued spice that is susceptible to fraudulent adulteration, particularly the addition of edible oils to increase weight and improve color. Nuclear magnetic resonance (¹H NMR) and chemometrics were used to analyze 120 huajiao samples adulterated with different types and levels of edible oils. Using untargeted data and partial least squares-discriminant analysis (PLS-DA), the discrimination rate between types of adulteration reached 100% accuracy, and the R² value of the prediction set for the level of adulteration using the targeted analysis dataset combined with PLS-regression methods reached 0.99. Triacylglycerols, major components of edible oils, were identified as a marker of adulteration through the variable importance in projection of the PLS-regression. A quantitative method based on the sn-3 triacylglycerol signal was developed that can achieve a detection limit of 0.11%. Testing of 28 market samples showed adulteration with various edible oils, with adulteration rates ranging from 0.96% to 4.41%.

NMR-Based Characterization of Citrus Tacle Juice and Low-Level NMR and UV-Vis Data Fusion for Monitoring Its Fractions from Membrane-Based Operations

Tacle is a citrus variety which recently gained further interest due to its antioxidant and biological properties. This study suggests using Nuclear Magnetic Resonance (NMR) imaging to characterize Tacle juice's metabolic composition as it is intimately linked to its quality. First, polar and apolar solvent systems were used to identify a significant fraction of the Tacle metabolome. Furthermore, the antioxidant capacity and the total content of flavonoids, polyphenols and β-carotene in the juice were investigated with UV-Visible spectroscopy. Tacle juice was clarified and fractionated by ultrafiltration (UF) and nanofiltration (NF) membranes in order to recover and purify its bioactive principles. Finally, the second part of this work sheds light on the spectrophotometric assays and 1H-NMR spectra of fractions coming from membrane operations coupled with a multivariate data analysis technique, PCA, to explore the impact of UF and NF processes on the metabolic profile of the juice.

NMR-Based Approaches in the Study of Foods

In this review, the three different NMR-based approaches usually used to study foodstuffs are described, reporting specific examples. The first approach starts with the food of interest that can be investigated using different complementary NMR methodologies to obtain a comprehensive picture of food composition and structure; another approach starts with the specific problem related to a given food (frauds, safety, traceability, geographical and botanical origin, farming methods, food processing, maturation and ageing, etc.) that can be addressed by choosing the most suitable NMR methodology; finally, it is possible to start from a single NMR methodology, developing a broad range of applications to tackle common food-related challenges and different aspects related to foods.

Impact of genus (Geotrigona, Melipona, Scaptotrigona) in the targeted 1H-NMR organic profile, and authenticity test by interphase emulsion of honey processed in cerumen pots by stingless bees in Ecuador

The biodiversity of Ecuadorian stingless bees is almost 200 species. Traditional pot-honey harvest in Ecuador is mostly done from nests of the three genera selected here Geotrigona Moure, 1943, Melipona Illiger, 1806, and Scaptotrigona Moure, 1942. The 20 pot-honey samples collected from cerumen pots and three ethnic honeys "abeja de tierra", "bermejo", and "cushillomishki" were analyzed for qualitative and quantitative targeted 1H-NMR honey profiling, and for the Honey Authenticity Test by Interphase Emulsion (HATIE). Extensive data of targeted organic compounds (41 parameters) were identified, quantified, and described. The three honey types were compared by ANOVA. Amino acids, ethanol, hydroxymethylfurfural, aliphatic organic acids, sugars, and markers of botanical origin. The number of phases observed with the HATIE were one in Scaptotrigona and three in Geotrigona and Melipona honeys. Acetic acid (19.60 ± 1.45 g/kg) and lactic acid (24.30 ± 1.65 g/kg) were particularly high in Geotrigona honey (in contrast to 1.3 g/kg acetic acid and 1.6 g/kg lactic acid in Melipona and Scaptotrigona), and with the lowest fructose + glucose (18.39 ± 1.68) g/100g honey compared to Melipona (52.87 ± 1.75) and Scaptotrigona (52.17 ± 0.60). Three local honeys were tested using PCA (Principal Component Analysis), two were assigned with a correct declared bee origin, but "bermejo" was not a Melipona and grouped with the Scaptotrigona cluster. However after HCA (Hierarchical Cluster Analysis) the three honeys were positioned in the Melipona-Scaptotrigona cluster. This research supports targeted 1H-NMR-based profiling of pot-honey metabolomics approach for multi-parameter visualization of organic compounds, as well as descriptive and pertained multivariate statistics (HCA and PCA) to discriminate the stingless bee genus in a set of Geotrigona, Melipona and Scaptotrigona honey types. The NMR characterization of Ecuadorian honey produced by stingless bees emphasizes the need for regulatory norms. A final note on stingless bee markers in pot-honey metabolites which should be screened for those that may extract phylogenetic signals from nutritional traits of honey. Scaptotrigona vitorum honey revealed biosurfactant activity in the HATIE, originating a fingerprint Honey Biosurfactant Test (HBT) for the genus in this set of pot-honeys.

Classification of Polish Natural Bee Honeys Based on Their Chemical Composition

The targeted quantitative NMR (qNMR) approach is a powerful analytical tool, which can be applied to classify and/or determine the authenticity of honey samples. In our study, this technique was used to determine the chemical profiles of different types of Polish honey samples, featured by variable contents of main sugars, free amino acids, and 5-(hydroxymethyl)furfural. One-way analysis of variance (ANOVA) was performed on concentrations of selected compounds to determine significant differences in their levels between all types of honey. For pattern recognition, principal component analysis (PCA) was conducted and good separations between all honey samples were obtained. The results of present studies allow the differentiation of honey samples based on the content of sucrose, glucose, and fructose, as well as amino acids such as tyrosine, phenylalanine, proline, and alanine. Our results indicated that the combination of qNMR with chemometric analysis may serve as a supplementary tool in specifying honeys.

Methodology for Single Bee and Bee Brain 1H-NMR Metabolomics

The feasibility of metabolomic 1H NMR spectroscopy is demonstrated for its potential to help unravel the complex factors that are impacting honeybee health and behavior. Targeted and non-targeted 1H NMR metabolic profiles of liquid and tissue samples of organisms could provide information on the pathology of infections and on environmentally induced stresses. This work reports on establishing extraction methods for NMR metabolic characterization of Apis mellifera, the European honeybee, describes the currently assignable aqueous metabolome, and gives examples of diverse samples (brain, head, body, whole bee) and biologically meaningful metabolic variation (drone, forager, day old, deformed wing virus). Both high-field (600 MHz) and low-field (80 MHz) methods are applicable, and 1H NMR can observe a useful subset of the metabolome of single bees using accessible NMR instrumentation (600 MHz, inverse room temperature probe) in order to avoid pooling several bees. Metabolite levels and changes can be measured by NMR in the bee brain, where dysregulation of metabolic processes has been implicated in colony collapse. For a targeted study, the ability to recover 10-hydroxy-2-decenoic acid in mandibular glands is shown, as well as markers of interest in the bee brain such as GABA (4-aminobutyrate), proline, and arginine. The findings here support the growing use of 1H NMR more broadly in bees, native pollinators, and insects.