ICP-MS-based ionomics method for discriminating the geographical origin of honey of Apis cerana Fabricius

Honey: Inorganic Composition as Possible Marker for Botanical and Geological Assignment

Honey can be classified based on its geographical or botanical origin according to Directive 2001/110/EC. Geographical origin is determined when the pollen collection occurs entirely in a specific location, allowing producers to label the honey with the name of the region. Verification typically involves melissopalynological analysis to match the pollen spectrum with regional vegetation. Botanical origin applies when honey predominantly comes from a single floral species, characterized by specific organoleptic, physico-chemical, and microscopic properties. However, defining "predominantly" and identifying distinct physico-chemical parameters remain ambiguous. This review evaluates the use of chemical analysis as a complement or alternative to melissopalynological methods for determining honey's origin. The focus is on inorganic composition, particularly metals and semimetals, as potential fingerprints to identify botanical or geographical provenance. Relevant studies were reviewed, with data reprocessed and analyzed using chemometric techniques, including Principal Component Analysis and Agglomerative Cluster Analysis. These methods reveal common traits distinguishing honeys by origin. Chemical analysis combined with chemometric processing enhances honey identification, prevents fraud, assesses environmental pollution in collection areas, and evaluates the impact of processing on the final product.

Metabolomics and ionomics reveal the quality differences among peach, acacia and karaya gums

Despite the diverse industrial applications and health benefits of plant gums, significant variations in quality among different types remain underexplored. This study investigates the differences in antioxidant activity, mineral elements, and metabolic profiles among peach, acacia, and karaya gums. Our findings reveal significant differences in total phenol content, with peach gum exhibiting the highest (20.41 μmol/g), followed by acacia gum (3.94 μmol/g) and karaya gum (1.24 μmol/g). Metabolomics and ionomics show that these gums were rich in a variety of small molecular metabolites, including amino acids, organic acids, flavonoids, and lipids, as well as numerous mineral elements. However, the concentrations of these compounds varied significantly across the different gum types. Specifically, peach gum contained higher levels of small-molecule organic acids (such as citric, quinic, and azelaic acids) and flavonoids. In contrast, acacia gum was characterized by a higher content of central amino acids (glutamic and aspartic acids), aromatic amino acids (tyrosine, phenylalanine and tryptophan) and alkaloids (trigonelline, spermidine and spermine). Karaya gum exhibited higher levels of lipids (including palmitic, linoleic, and tetradecanoic acids) and minerals (such as Ca, S, Mg and Fe). Notably, pesticide residues, including thiamethoxam, propiconazole, and difenoconazole, were detected in peach gum, indicating potential health risks. These findings provide valuable insights into the quality analysis of plant gums and the exploration of their functional components.

Comparison and chemometrics analysis of phenolic compounds and mineral elements in Artemisia Argyi Folium from different geographical origins

The quality of Artemisia Argyi Folium (AAF), a traditional Chinese food ingredient, is intrinsically linked to its geographical origin, which this study explores through phenolic compounds and mineral elements. The contents of 17 phenols and 18 minerals differed significantly between geographically distinct samples according to UHPLC and ICP-MS, respectively. Chemometrics indicated that a supervised model, orthogonal partial least squares discriminant analysis (OPLS-DA), outperformed unsupervised methods at classifying AAF samples by their origins. Phenols were more effective at distinguishing samples from seven provinces, while minerals were adept at differentiating samples from the Dabie Mountain region (three provinces) and those from four other provinces. Six phenols and 10 minerals were important variables for discrimination. Complex correlations were observed between the contents of various phenols and minerals in AAF, with minerals possibly affecting the accumulation of phenols. This study provides an approach for distinguishing geographically distinct AAF samples and determining their geographical origins.

Elemental Fingerprinting Combined with Machine Learning Techniques as a Powerful Tool for Geographical Discrimination of Honeys from Nearby Regions

Discrimination of honey based on geographical origin is a common fraudulent practice and is one of the most investigated topics in honey authentication. This research aims to discriminate honeys according to their geographical origin by combining elemental fingerprinting with machine-learning techniques. In particular, the main objective of this study is to distinguish the origin of unifloral and multifloral honeys produced in neighboring regions, such as Sardinia (Italy) and Spain. The elemental compositions of 247 honeys were determined using Inductively Coupled Plasma Mass Spectrometry (ICP-MS). The origins of honey were differentiated using Principal Component Analysis (PCA), Linear Discriminant Analysis (LDA), and Random Forest (RF). Compared to LDA, RF demonstrated greater stability and better classification performance. The best classification was based on geographical origin, achieving 90% accuracy using Na, Mg, Mn, Sr, Zn, Ce, Nd, Eu, and Tb as predictors.

Hydroxy Fatty Acids as Novel Markers for Authenticity Identification of the Honey Entomological Origin Based on the GC-MS Method

The authenticity of honey is generally a worldwide concern, and there is a pressing need to establish a suitable entomological method to identify the authenticity of Apis cerana cerana (A. cerana) and Apis mellifera ligustica (A. mellifera) honey. Hydroxy fatty acids as bee-derived components are known to widely exist in honey and other biosamples. Herein, we present an identification strategy for hydroxy fatty acids based on the relative quantification with reference to royal jelly and targeted quantification combined with multivariate statistical analysis to identify the honey entomological origin. Multivariate statistical analysis was used to further determine differential hydroxy fatty acids between A. cerana honey and A. mellifera honey. Results showed that 8-hydroxyoctanoic acid (96.20-253.34 versus 0-32.46 mg kg^-1) and 3,10-dihydroxydecanoic acid (1.96-6.56 versus 0-0.35 mg kg^-1) could be used as markers for accurate identification of the honey entomological origin, while the three fraud honey samples were recognized using this method. This study provides the novel marker hydroxy fatty acids to identify A. cerana honey and A. mellifera honey from the perspective of bee-derived component differences.

Identification of the botanical origins of honey based on nanoliter electrospray ionization mass spectrometry

The botanical origins of honey are important for the quality control and commercialization of honey. In this research, we established a nanoliter electrospray ionization mass spectrometry (Nano-ESI-MS) method to identify Castanopsis honey (CH), Eurya honey (EH), Dendropanax dentiger honey (DH), and Triadica cochinchinensis honey (TH). In total, 38 compounds were identified based on the collision-induced dissociation experiments by Nano-ESI-MS with 16 differential compounds and 7 quantified as potential differential markers. These four types of honey were distinguished from each other by their mass spectrometry data combined with multivariate analysis with three out of the 7 differential markers, i.e., phenethylamine, tricoumaroyl spermidine, and (+/-)-abscisic acid, identified as potential markers for CH, EH, and DH, respectively. Both the qualitative and quantitative results derived from Nano-ESI-MS were further verified by UPLC-Q/TOF-MS. Our studies provided the significant potential of the Nano-ESI-MS method in the identification of the botanical origins of different kinds of honey.

Production regions discrimination of Huangguanyin oolong tea by using the content of chemical components and rare earth elements

Oolong tea is one of the most popular tea beverages in China. Tea cultivars, processing technology and origin of production affect the quality and price of oolong teas. To investigate the differences in Huangguanyin oolong tea from different production regions, the chemical components, mineral elements and rare earth elements of Huangguanyin oolong tea produced in Yunxiao (YX) and Wuyishan (WY) were analyzed by using spectrophotometry methods, targeted metabolomics and inductive plasma coupled mass spectrometry (ICP-MS). The results of spectrophotometry methods revealed that there were significant differences in thearubigin, tea polyphenols and water extract between Huangguanyin oolong teas from different production regions. Targeted metabolomics identified a total of 31 chemical components in Huangguanyin oolong teas from the two production regions, of which 14 chemical components were significantly different and contributed to the regional differentiation of Huangguanyin oolong tea. Yunxiao Huangguanyin had relatively higher contents of (-)-Epigallocatechin-3-O-(3-O-methylgallate) (EGCG3″Me), ornithine (Orn) and histidine (His), while Wuyishan Huangguanyin had relatively higher contents of glutamic acid (Glu), γ-aminobutyric acid (GABA), β-aminobutyric acid (β-ABA) and other components. Moreover, ICP-MS identified a total of 15 mineral elements and 15 rare earth elements in Huangguanyin oolong tea from the two production regions, of which 15 elements were significantly different between YX and WY, and contributed to the regional differentiation of Huangguanyin oolong tea. K had a relatively higher content in Yunxiao Huangguanyin, while rare earth elements had relatively higher contents in Wuyishan Huangguanyin. The classification results by the production region showed that the discrimination rate of the support vector machine (SVM) model based on the 14 different chemical components reached 88.89%, while the SVM model based on the 15 elements reached 100%. Therefore, we used targeted metabolomics and ICP-MS techniques to screen and explore the chemical components, mineral elements and rare earth elements differences among two production regions, which indicated the feasibility of Huangguanyin oolong tea classification by production regions in the study. The results will provide some reference for the distinction between the two production regions of Huangguanyin oolong tea.

A review of conventional and rapid analytical techniques coupled with multivariate analysis for origin traceability of soybean

Soybean has developed a reputation as a superfood due to its nutrient profile, health benefits, and versatility. Since 1960, its demand has increased dramatically, going from a mere 17 MMT to almost 358 MMT in the production year 2021/22. These extremely high production rates have led to lower-than-expected product quality, adulteration, illegal trade, deforestation, and other concerns. This necessitates the development of an effective technology to confirm soybean's provenance. This is the first review that investigates current analytical techniques coupled with multivariate analysis for origin traceability of soybeans. The fundamentals of several analytical techniques are presented, assessed, compared, and discussed in terms of their operating specifics, advantages, and shortcomings. Additionally, significance of multivariate analysis in analyzing complex data has also been discussed.

Phenolic profile and in vitro anti-inflammatory activity of Mimosa scabrella Bentham honeydew honey in RAW 264.7 murine macrophages

The anti-inflammatory activity is mainly attributed to the phenolic compounds. Once the geographical location affects the phenolic content of honeys, a relationship between the collection spot and the anti-inflammatory effect of bracatinga (Mimosa scabrella Bentham) honeydew honeys was hypothesized. The inhibitory effect of 14 honey samples on NOx, TNF-α, IL-6, IL-12p70, MCP-1, INF-γ, and IL-10 in RAW 264.7 macrophages inflamed by LPS was evaluated. Fourteen phenolic compounds were identified, mainly syringic acid and rutin. Ten honeys inhibited nitrite production; at least six downregulated TNF-α, IL-12p70, MCP-1, and IFN-γ; only four honey samples inhibited IL-6; and one honey sample inhibited IL-10 levels, showing their variable effects on the inflammatory markers. Principal component analysis grouped samples according to the phenolic content and downregulation of specific inflammatory markers. The bracatinga honeydew honey effectiveness was associated with geographical location, as samples from areas with higher density and diversity of plants had a more significant anti-inflammatory effect. PRACTICAL APPLICATIONS: The present research study investigated the anti-inflammatory potential of bracatinga honeydew honey samples collected from regions with different vegetation coverages. Honey samples collected from locations presenting greater forest diversity and density inhibited inflammatory markers more efficiently. This study reinforces the role of the bracatinga honeydew honey in preventing inflammatory processes and the importance of preserving forests so that products with a greater diversity of compounds and consequently more active can be obtained.

Ultra-sensitive H2S sensor based on sunflower-like In-doped ZnO with enriched oxygen vacancies

In–ZnO with oxygen vacancies exhibits a higher sensing response and a shorter recovery time for H2S compared to ZnO.

Multi-Elemental Composition Data Handled by Chemometrics for the Discrimination of High-Value Italian Pecorino Cheeses

The multi-elemental composition of three typical Italian Pecorino cheeses, Protected Designation of Origin (PDO) Pecorino Romano (PR), PDO Pecorino Sardo (PS) and Pecorino di Farindola (PF), was determined by Inductively Coupled Plasma Optical Emission Spectrometry (ICP-OES). The ICP-OES method here developed allowed the accurate and precise determination of eight major elements (Ba, Ca, Fe, K, Mg, Na, P, and Zn). The ICP-OES data acquired from 17 PR, 20 PS, and 16 PF samples were processed by unsupervised (Principal Component Analysis, PCA) and supervised (Partial Least Square-Discriminant Analysis, PLS-DA) multivariate methods. PCA revealed a relatively high variability of the multi-elemental composition within the samples of a given variety, and a fairly good separation of the Pecorino cheeses according to the geographical origin. Concerning the supervised classification, PLS-DA has allowed obtaining excellent results, both in calibration (in cross-validation) and in validation (on the external test set). In fact, the model led to a cross-validated total accuracy of 93.3% and a predictive accuracy of 91.3%, corresponding to 2 (over 23) misclassified test samples, indicating the adequacy of the model in discriminating Pecorino cheese in accordance with its origin.