Characterization, Classification and Authentication of Spanish Blossom and Honeydew Honeys by non-targeted HPLC-UV and off-line SPE HPLC-UV Polyphenolic Fingerprinting Strategies.

Honey is a highly consumed natural product produced by bees which is susceptible to fraudulent practices, some of them regarding their botanical origin. Two HPLC-UV non-targeted finger-printing approaches were evaluated in this work to address honey characterization, classification, and authentication based on honey botanical variety. The first method used no sample treatment and a universal reversed-phase chromatographic separation. On the contrary, the second method was based on an off-line SPE preconcentration method, optimized for the isolation and extraction of polyphenolic compounds, and a reversed-phase chromatographic separation opti-mized for polyphenols as well.. For the off-line SPE method, the use of HLB (3 mL, 60 mg) car-tridges, and 6 mL of methanol as eluent, allowed to achieve acceptable recoveries for the selected polyphenols. The obtained HPLC-UV fingerprints were subjected to exploratory principal com-ponent analysis (PCA) and classificatory partial least squares-discriminant analysis (PLS-DA) to evaluate their viability as sample chemical descriptors for authentication purposes. Both HPLC-UV fingerprints resulted to be appropriate to discriminate between blossom-honeys and honeydew-honeys. However, superior performance was accomplished with off-line SPE HPLC-UV polyphenolic fingerprints, being able to differentiate among the different blos-som-honey samples under study (orange/lemon blossom, rosemary, thyme, eucalyptus, and heather). In general, this work demonstrated the feasibility of HPLC-UV fingerprints, especially those obtained after off-line SPE polyphenolic isolation and extraction, to be employed as honey chemical descriptors to address the characterization and classification of honey samples according to their botanical origin

Classificació i Autenticació de Mel mitjançant Perfils Cromatogràfics i Mètodes Quimiomètrics

Treballs Finals de Grau de Química, Facultat de Química, Universitat de Barcelona, Any: 2022, Tutors:. Oscar Núñez i Sònia SentellasNowadays, food industry is suffering a hard competitiveness that has led to a fraud increase in order to achieve greater economical profits. These irregular actions have raised the interest in the quality and authentication of commercialized food products, not only from the consumers and producers, but also from the scientific research. Honey can be found among the most vulnerable products to be adulterated and it is mostly affected by two fraud types: the mixing of monofloral honeys with multifloral honeys, which are considered of less quality, and the addition of cheaper sweeteners as syrups or industrial sugars. Even though these honeys are adulterated, they are labelled without all the information and they are sold as products of higher quality. This project aims to stablish a simple method to characterize honeys from different botanical and geographical origins and to classify them considering the results. A non-targeted method and a simple sample pre-treatment has been chosen to obtain chromatographic fingerprints, using high-performance liquid chromatography with an ultraviolet detector (HPLC-UV). The chromatographic profiles are evaluated as chemical descriptors by using different chemometric methods such as principal component analysis (PCA), partial least-squares discriminant analysis (PLS-DA) and, in some cases, hierarchical cluster analysis (HCA). The PCA results prove that the proposed method is robust and reproducible, and that separation of honey samples in different groups is possible. PLS-DA has shown a classification between different botanical origins of samples and this clustering has been supported by HCA, firstly between honeydew honeys and blossom honeys, and then between some floral varieties. On the other side, a good classification according to the region of production has not been accomplished, and it will be studied in further researc