Rice represents the main source of calorie intake in many world countries and about 60% of the world population include rice in their staple diet. Whole grain rice, also called brown rice, represent the unpolished version of the more common white rice including bran, germ, and endosperm. Many health-promoting properties have been associated to the consumption of whole grain rice and, for this reason, great attention has been paid by the scientific community towards the identification and the quantification of bioactive compounds in this food item. In this contribution, the last five years progresses in the quali-quantitative determination of phenolic compounds in rice have been highlighted. Special attention has been devoted to the most recent strategies for the extraction of the target compounds from rice along with the analytical approaches adopted for the separation, identification and quantification of phenolic acids, flavonoids, anthocyanins, and proanthocyanidins. More specifically, the main features of the “traditional” extraction methods (i.e., maceration, ultrasound-assisted extraction) have been described, as well as the more innovative protocols involving advanced extraction techniques, such as MAE (microwave-assisted extraction). The predominant role of HPLC in the definition of the phenolic profile has been examined also presenting the most recent results obtained by using mass spectrometry-based detection systems. In addition, the most common procedures aimed to the quantification of the total amount of the cited classes of phenolic compounds have been described together with the spectrophotometric protocols aimed to the evaluation of the antioxidant properties of rice phenolic extracts (i.e., FRAP, DPPH, ABTS and ORAC).

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.

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.