Botanical and geographical origin of Turkish honeys by selected-ion flow-tube mass spectrometry and chemometrics

Analysis of aromatic amines in human urine using comprehensive multi-dimensional gas chromatography-mass spectrometry (GCxGC-MS)

Several aromatic amines (AA) are classified as human carcinogens, and tobacco smoke is one of the main sources of exposure. Once in the human body, they undergo different metabolic pathways which lead to either their excretion or ultimately to the formation of DNA and protein adducts. The aim of this study was to investigate AA in 68 urine samples (aged 29-79, 47% female), including 10 smokers (S), 28 past-smokers (PS) and 30 never-smokers (NS), and to study if there was a relation between the smoking status and the amount of the AA present. GCxGC-MS was used to analyze AA in complex urine samples due to its high peak capacity and the fact that it provides two sets of retention times and structural information, which facilitates the separation and identification of the target analytes. First, a qualitative comparison of an example set of a NS, PS and S sample was carried out, in which 38, 45 and 46 AA, respectively, could be tentatively identified. Afterwards, seven AA were successfully quantified in the samples. Of these, 4-ethylaniline (4EA, p = 0.015), 2,4,6-trimethylaniline (2,4,6TMA, p = 0.030), 2-naphthylamine (2NA, p = 0.014) and the sum of 2,4- and 2,6-dimethylaniline (DMA, p = 0.017) were found in significantly different (α = 0.05) concentrations for the S, 29 ± 14, 87 ± 49, 41 ± 26, and 105 ± 57 ng/L respectively, compared to the NS, 15 ± 6, 42 ± 30, 16 ± 6, and 48 ± 28 ng/L. And 2,4,6TMA (39 ± 26, p = 0.022), 2NA (18 ± 9, p = 0.025) and DMA (53 ± 46, p = 0.030), were also found at significantly higher concentrations in samples from S when compared to PS. However, some samples had AA concentrations outside the calibration curve and could not be taken into account, especially for 2-methylaniline (2MA). Therefore, all the samples were evaluated using a quantitative screening approach, by which the intensities of 4EA (p = 0.019), 2,4,6TMA (p = 0.048), 2NA (p = 0.016), DMA (p = 0.019) and 2MA (p = 0.006) in S were found to be significantly (α = 0.05) higher than in the NS, and 2MA (p = 0.019) and 4EA (p = 0.023) in S were found to be significantly higher than in the PS. An association between the smoking status and the amount of certain AA present could therefore be found. This information could be used to study the relation between the smoking status, the amount of AA present, and smoking related diseases like bladder cancer.

Exploring stingless bee honey from selected regions of Peninsular Malaysia through gas chromatography-mass spectrometry-based untargeted metabolomics

Volatile organic compounds in honey are known for their considerable impact on the organoleptic properties of honey, such as aroma, flavor, taste, and texture. The type and composition of volatile organic compounds are influenced by entomological, geographical, and botanical origins; thus, these compounds have the potential to be chemical markers. Sixty-two volatile compounds were identified using gas chromatography-mass spectrometry from 30 Heterotrigona itama (H. itama) honey samples from 3 different geographical origins. Hydrocarbons and benzene derivatives were the dominant classes of volatile organic compounds in the samples. Both clustering and discriminant analyses demonstrated a clear separation between samples from distant origins (Kedah and Perak), and the volcano plot supported it. The reliability and predictability of the partial least squares-discriminant analysis model from the discriminant analysis were validated using cross-validation (R2 : 0.93; Q2 : 0.83; accuracy: 0.97) and the permutation test (p < 0.001), and the output depicted that the model is legitimate. In combination with the variable importance of projection (VIP > 1.0) and the Kruskal-Wallis test (p < 0.01), 19 volatile organic compounds (encompassed aldehydes, benzene derivatives, esters, hydrocarbons, and terpenoids) were sorted and named potent chemical markers in classifying honey samples from three geographical origins. In brief, this study illustrated that volatile organic compounds of stingless honey originated from the same bee species, but different geographical origins could be applied as chemical markers.

Comprehensive study of volatile compounds of rare Leucosceptrum canum Smith honey: Aroma profiling and characteristic compound screening via GC-MS and GC-MS/MS

Monofloral honeys are highly valued for their unique flavors and potential health benefits. In this study, the aromatic attributes of rare Leucosceptrum canum Smith honey (LCH) were characterized by GC-MS coupled with GC-MS/MS. Based on their odor contribution rates (OCRs), linalool (74.22%), 3-methyl-1-butanol (18.19%), benzeneacetaldehyde (1.31%) and lilac aldehyde B (2.78%) were largely responsible for the unique and complex flavor of LCH - flowery, spicy, sweet, fruity and fresh. Compared to other tested honeys, linalool (0.18 mg/kg), which has known antibacterial properties, was higher in LCH. However, it was not the main antibacterial compound in LCH, suggesting as of now unknown antibacterial compounds. This study provides the first aromatic profile of LCH, which will be useful for the authentication of LCH and for uncovering the mechanisms behind its perceived health benefits.

Microbial Community Structure among Honey Samples of Different Pollen Origin

Honey's antibacterial activity has been recently linked to the inhibitory effects of honey microbiota against a range of foodborne and human pathogens. In the current study, the microbial community structure of honey samples exerting pronounced antimicrobial activity was examined. The honey samples were obtained from different geographical locations in Greece and had diverse pollen origin (fir, cotton, fir-oak, and Arbutus unedo honeys). Identification of honey microbiota was performed by high-throughput amplicon sequencing analysis, detecting 335 distinct taxa in the analyzed samples. Regarding ecological indices, the fir and cotton honeys possessed greater diversity than the fir-oak and Arbutus unedo ones. Lactobacillus kunkeei (basionym of Apilactobacillus kun-keei) was the predominant taxon in the fir honey examined. Lactobacillus spp. appeared to be favored in honey from fir-originated pollen and nectar since lactobacilli were more pronounced in fir compared to fir-oak honey. Pseudomonas, Streptococcus, Lysobacter and Meiothermus were the predominant taxa in cotton honey, whereas Lonsdalea, the causing agent of acute oak decline, and Zymobacter, an osmotolerant facultative anaerobic fermenter, were the dominant taxa in fir-oak honey. Moreover, methylotrophic bacteria represented 1.3-3% of the total relative abundance, independently of the geographical and pollen origin, indicating that methylotrophy plays an important role in honeybee ecology and functionality. A total of 14 taxa were identified in all examined honey samples, including bacilli/anoxybacilli, paracocci, lysobacters, pseudomonads, and sphingomonads. It is concluded that microbial constituents of the honey samples examined were native gut microbiota of melliferous bees and microbiota of their flowering plants, including both beneficial bacteria, such as potential probiotic strains, and animal and plant pathogens, e.g., Staphylococcus spp. and Lonsdalea spp. Further experimentation will elucidate aspects of potential application of microbial bioindicators in identifying the authenticity of honey and honeybee-derived products.

Authentication of extra virgin Argan oil by selected-ion flow-tube mass-spectrometry fingerprinting and chemometrics

Recognized for its nutritional and therapeutic use, extra-virgin Argan Oil (EVAO) is frequently adulterated. Selected-Ion Flow-Tube Mass Spectrometry (SIFT-MS) spectra were applied to quantify adulterants (i.e., Argan oil of lower quality (LQAO), olive oil (OO), and sunflower oil (SO)) in EVAO. Four data sets, i.e., using H₃O⁺, NO⁺, O₂⁺ reagent ions, and the combined data were considered. Soft independent modelling of class analogy (SIMCA), and partial least squares discriminant analysis (PLS-DA) were assessed to distinguish adulterated- from pure EVAO. The effectiveness of SIFT-MS associated with PLS and support vector machine (SVM) regression to quantify trace adulterants in EVAO was evaluated. Variable Importance in Projection (VIP), and interval-PLS (iPLS) were also investigated to extract useful features. Different models were built to predict the EVAO authenticity and the degree of adulteration. High accuracy was achieved. SIFT-MS spectra handled with the appropriate chemometric tools were found suitable for the quality evaluation of EVAO.

Geographical Origin Discrimination of Monofloral Honeys by Direct Analysis in Real Time Ionization-High Resolution Mass Spectrometry (DART-HRMS)

An untargeted method using direct analysis in real time and high resolution mass spectrometry (DART-HRMS) combined to multivariate statistical analysis was developed for the discrimination of two monofloral (chestnut and acacia) honeys for their geographical origins-i.e., Italy and Portugal for chestnut honey and Italy and China for acacia honey. Principal Component Analysis, used as an unsupervised approach, showed samples of clusterization for chestnut honey samples, while overlapping regions were observed for acacia honeys. Three supervised statistical approaches, such as Principal Components-Linear Discriminant Analysis, Partial Least Squares-Discriminant Analysis and k-nearest neighbors, were tested on the dataset gathered and relevant performances were compared. All tested statistical approaches provided comparable prediction abilities in cross-validation and external validation with mean values falling between 89.2-98.4% for chestnut and between 85.8-95.0% for acacia honey. The results obtained herein indicate the feasibility of the DART-HRMS approach in combination with chemometrics for the rapid authentication of honey's geographical origin.