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 machinelearning 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 predictor

Bioactive Amines in Wines. The Assessment of Quality Descriptors by Flow Injection Analysis with Tandem Mass Spectrometry

Biogenic amines (BAs) occur in a wide variety of foodstuffs, mainly from the decomposition of proteins by the action of microorganisms. They are involved in several cellular functions but may become toxic when ingested in high amounts through the diet. In the case of oenological products, BAs are already present in low concentrations in must, and their levels rise dramatically during the fermentation processes. This paper proposes a rapid method for the determination of BAs in wines and related samples based on precolumn derivatization with dansyl chloride and further detection by flow injection analysis with tandem mass spectrometry. Some remarkable analytes such as putrescine, ethanolamine, histamine, and tyramine have been quantified in the samples. Concentrations obtained have shown interesting patterns, pointing out the role of BAs as quality descriptors. Furthermore, it has been found that the BA content also depends on the vinification practices, with malolactic fermentation being a significant step in the formation of BAs. From the point of view of health, concentrations found in the samples are, in general, below 10 mg L−1, so the consumption of these products does not represent any special concern. In conclusion, the proposed method results in a suitable approach for a fast screening of this family of bioactive compounds in wines to evaluate quality and health issues

Off-Line SPE LC-LRMS Polyphenolic Fingerprinting and Chemometrics to Classify and Authenticate Spanish Honey

The feasibility of non-targeted off-line SPE LC-LRMS polyphenolic fingerprints to address the classification and authentication of Spanish honey samples based on both botanical origin (blossom and honeydew honeys) and geographical production region was evaluated. With this aim, 136 honey samples belonging to different botanical varieties (multifloral and monofloral) obtained from different Spanish geographical regions with specific climatic conditions were analyzed. Polyphenolic compounds were extracted by off-line solid-phase extraction (SPE) using HLB (3 mL, 60 mg) cartridges. The obtained extracts were then analyzed by C18 reversed-phase LC coupled to low-resolution mass spectrometry in a hybrid quadrupole-linear ion trap mass analyzer and using electrospray in negative ionization mode. Principal component analysis (PCA) and partial least squares-discriminant analysis (PLS-DA) were employed to assess the pattern recognition capabilities of the obtained fingerprints to address honey classification and authentication. In general, a good sample discrimination was accomplished by PLS-DA, being able to differentiate both blossom-honey and honeydew-honey samples according to botanical varieties. Multiclass predictions by cross-validation for the set of blossom-honey samples showed sensitivity, specificity, and classification ratios higher than 60%, 85%, and 87%, respectively. Better results were obtained for the set of honeydew-honey samples, exhibiting 100% sensitivity, specificity, and classification ratio values. The proposed fingerprints also demonstrated that they were good honey chemical descriptors to deal with climatic and geographical issues. Characteristic polyphenols of each botanical variety were tentatively identified by LC-MS/MS in multiple-reaction monitoring mode to propose possible honey markers for future experiments (i.e., naringin for orange/lemon blossom honeys, syringic acid in thyme honeys, or galangin in rosemary honeys)