Honey is one of the most renowned natural foods. Its composition is extremely variable, depending on its botanical and geographical origins, and the abundant presence of functional compounds has contributed to the increased worldwide interest is this foodstuff. In particular, great attention has been paid by the scientific community towards classes of compounds like phenolic compounds, due to their capability to act as markers of unifloral honey origin. In this contribution the most recent progress in the assessment of new analytical procedures aimed at the definition of the qualitative and quantitative profile of phenolic compounds of honey have been highlighted. A special emphasis has been placed on the innovative aspects concerning the extraction procedures, along with the most recent strategies proposed for the analysis of phenolic compounds. Moreover, the centrality of validation procedures has been claimed and extensively discussed in order to ensure the fitness-for-purpose of the proposed analytical methods. In addition, the exploitation of the phenolic profile as a tool for the classification of the botanical and geographical origin has been described, pointing out the usefulness of chemometrics in the interpretation of data sets originating from the analysis of polyphenols. Finally, recent results in concerning the evaluation of the antioxidant properties of unifloral honeys and the development of new analytical approaches aimed at measuring this parameter have been reviewed.

Honey is a natural product known to modulate several biological activities including cancer. The aim of the present study was to examine the phytochemical content and the antioxidant activity of Strawberry tree (Arbutus unedo) honey (STH) and its cytotoxic properties against human colon adenocarcinoma (HCT-116) and metastatic (LoVo) cell lines in comparison with Manuka (Leptospermum scoparium) honey (MH). Several unifloral STH and MH were analyzed for their phenolic, flavonoid, amino acid and protein contents, as well as their radical scavenging activities. STH from the Berchidda area showed the highest amount of phenolic, flavonoid, amino acid and protein content, and antioxidant capacity compared to MH. Both STH and MH induced cytotoxicity and cell death in a dose- and time-dependent manner in HCT-116 and LoVo cells, with less toxicity on non-cancer cells. Compared to MH, STH showed more effect at lower concentrations on HCT-116 and LoVo cells. In addition, both honeys increased intracellular reactive oxygen species (ROS) generation. In HCT-116 cells, STH and MH induced similar ROS production but in LoVo cells STH induced a higher percentage of ROS compared to MH. Our results indicate that STH and MH can induce cell growth inhibition and ROS generation in colon adenocarcinoma and metastatic cells, which could be due to the presence of phytochemicals with antioxidant properties. These preliminary results are interesting and suggest a potential chemopreventive action which could be useful for further studies in order to develop chemopreventive agents for colon cancer.

Fraudulent practices concerning honey are growing fast and involve misrepresentation of origin and adulteration. Simple and feasible methods for honey authentication are needed to ascertain honey compliance and quality. Working on a robust dataset and simultaneously investigating honey traceability and adulterant detection, this study proposed a portable FTNIR fingerprinting approach combined with chemometrics. Multifloral and unifloral honey samples (n = 244) from Spain and Sardinia (Italy) were discriminated by botanical and geographical origin. Qualitative and quantitative methods were developed using linear discriminant analysis (LDA) and partial least squares (PLS) regression to detect adulterated honey with two syrups, consisting of glucose, fructose, and maltose. Botanical and geographical origins were predicted with 90% and 95% accuracy, respectively. LDA models discriminated pure and adulterated honey samples with an accuracy of over 92%, whereas PLS allows for the accurate quantification of over 10% of adulterants in unifloral and 20% in multifloral honey.

Iron, one of the most common metals in the environment, plays a fundamental role in many biological as well as biogeochemical processes, which determine its availability in different oxidation states. Its relevance in environmental and industrial chemistry, human physiology, and many other fields has made it necessary to develop and optimize analysis techniques for accurate determination. Spectrophotometric methods are the most frequently applied in the analytical determination of iron in real samples. Taking advantage of the fact that desferrioxamine B, a trihydroxamic acid used since the 1970s in chelation therapy for iron overload treatment, forms a single stable 1:1 complex with iron in whichever oxidation state it can be found, a smart spectrophotometric method for the analytical determination of iron concentration was developed. In particular, the full compliance with the Lambert-Beer law, the range of iron concentration, the influence of pH, and the interference of other metal ions have been taken into account. The proposed method was validated in terms of LoD, LoQ, linearity, precision, and trueness, and has been applied for total iron determination in natural water certified material and in biological reference materials such as control human urine and control serum.

Nowadays, the mislabeling of honey floral origin is a very common fraudulent practice. The scientific community is intensifying its efforts to provide the bodies responsible for controlling the authenticity of honey with fast and reliable analytical protocols. In this study, the classification of various monofloral honeys from Sardinia, Italy, was attempted by means of ATR-FTIR spectroscopy and random forest. Four different floral origins were considered: strawberry-tree (Arbutus Unedo L.), asphodel (Asphodelus microcarpus), thistle (Galactites tormentosa), and eucalyptus (Eucalyptus calmadulensis). Training a random forest on the infrared spectra allowed achieving an average accuracy of 87% in a cross-validation setting. The identification of the significant wavenumbers revealed the important role played by the region 1540–1175 cm⁻¹ and, to a lesser extent, the region 1700–1600 cm⁻¹. The contribution of the phenolic fraction was identified as the main responsible for this observation.