Sensory Profile of Greek Islands Thyme Honey

The sensory profiles of thyme honey from the Greek islands with different thymus pollen grain contents (A: >60%, B: 40–60%, and C: 18–40%) were studied. The results of the physico-chemical analyses fulfilled the criteria set by international quality standards and, specifically, Greek legislation (moisture content 20 DN). The sensory results showed that there were significant differences between groups with different pollen grain contents (p < 0.01) for all attributes except for floral aroma, with the Group A samples being the lightest in color (4.9 ± 1.8) and having the highest floral odor intensity (5.0 ± 2.0) and salty taste (3.5 ± 1.1). Additionally, samples with the highest pollen grain content (i.e., Group A) had olfactory notes of wood/wax/resin and a chemical aroma

Correlations of sensory parameters with physicochemical characteristics of Argentinean honeys by multivariate statistical techniques

Honey acceptability is mainly determined by its colour, crystallisation degree and aroma. In the presentwork, the sensory characteristics and physicochemical parameters of Argentinean honeys from differentecoregions were analysed. Moisture content, Pfund colour, diastase activity, hydroxymethylfurfural content,electrical conductivity, sugar profile and volatile compounds were analytically determined in honeysamples, while sensory characteristics (crystal size, fluency score, sweetness, persistence, granularity, crystallisation,colour intensity and aroma) were evaluated by a trained panel. Significant correlations werefound between honey crystallisation degree and hydroxymethylfurfural content and diastase activity(P ≤ 0.05). It could be confirmed that honey crystallisation interferes with the visual perception of colour.Floral, fresh fruit, ripe fruit, balsamic and wood aromas could be successfully linked to honey volatileprofile (P ≤ 0.05). These results demonstrate that the parameters that could best guarantee the consumers?preference can be successfully associated with the chemical composition of honey by multivariate statisticalanalysis.Centro de Investigación y Desarrollo en Criotecnología de Alimento

Assessing the performance of analytical methods for propolis – A collaborative trial by the international honey commission

Propolis is a resinous beehive product with extraordinary bioactivity and chemical richness, linked with the botanical sources of the resin. The potential of this product keeps captivating the scientific community, conducting to continuous and growing research on plant sources, composition, or applications in agriculture, cosmetics, pharmacy, odontology, etc. In all cases, the quality assessment is a requirement and relies on methods to extract the bioactive substances from the raw propolis and quantify different components. Unfortunately, besides the absence of international quality requirements, there is also a lack of standardized analytical procedures, despite the presence of several methodologies with unknown reliability, often not comparable. To overcome the current status, the International Honey Commission established an inter-laboratory study, with propolis samples from around the globe, to harmonize analytical methods and evaluate their accuracy. A common set of protocols was matched between twelve laboratories from nine countries, for quantification of ash, wax, and balsamic content in raw propolis, and spectrophotometric evaluation of total phenolics, flavone/flavonol, and flavanone/ dihydroflavonol in the extract. A total of 3428 results (97% valid data), were used to assess the methods’ accuracy following ISO-5725 guidelines. The within-laboratory precision, revealed good agreement levels for the majority of the methods, with relative variance below 5%. As expected, the between-laboratory variance increased, but, with exception of the flavanone method that revealed a clear lack of consistency, all the others maintained acceptable variability levels, below 30%. Because the performance of ultrasounds procedures was low, they cannot be recommended until further improvements are made.The authors are grateful to the Foundation for Science and Technology (FCT, Portugal) for financial support by national funds FCT/MCTES to CIMO (UIDB/00690/2020). Thanks to the Programa Apíıcola Nacional 2020-2022 (National Beekeeping Program) for funding the project "Standardization of production procedures and quality parameters of bee products" and to Project PDR2020-1.0.1- FEADER-031734: “DivInA-Diversification and Innovation on Beekeeping Production”. National funding by FCT – Foundation for Science and Technology, through the institutional scientific employment program-contract with Soraia I. Falcão. A special thanks is given to Hartmut Scheiter and Allwex Food Trading GmbH, Bremen, Germany, for providing, handling and delivering the propolis blind samples.info:eu-repo/semantics/publishedVersio

Concentrations and risks of p ‐dichlorobenzene in indoor and outdoor air

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/96332/1/ina796.pd

Multivariate statistical approaches for wine classification based on low molecular weight phenolic compounds

Background and Aims: Phenolic compounds influence the colour, flavour and astringency of wines. These compounds are extracted into the wine during grape fermentation and maceration and thus the winemaking process is the main factor affecting the phenolic content of wines, besides the varietal factor. In this work, we aimed to apply self organizing maps to investigate the relationships between the profile of phenolic compounds and grape variety of wines, as well as the changes in the phenolic profile resulting from the malolactic fermentation. The results are compared with principal component analysis, and variation partitioning. Methods and Results: A reversed phase liquid chromatography/DAD method was used for the analysis of major non-flavonoid phenolic compounds in wines. The method employed allowed to evaluate the impact of malolactic fermentation in low molecular phenolic compounds in different wine varieties: Trincadeira, Aragonez, Cabernet Sauvignon, Alfrocheiro, Castelão and Touriga Nacional. The malolactic fermentation process was also study in Trincadeira variety using indigenous bacteria and two different commercial lactic bacteria. The impact of malolactic fermentation and grape varieties on the phenolic profile was evaluated by different multivariate statistical approaches: principal component analysis, variation partitioning analysis and artificial neural network. Conclusions: Principal component analysis allowed to explain 86.5% of the total variance among samples, without any additional information. Artificial neural network showed a significant clustering of samples according to grape variety, and confirmed that malolactic fermentation has a minor effect on wines phenolic profile. Variance partitioning enable to extract more information about the data since it allow to identify explanatory variables responsible for variability among samples. In this study, it was possible to identify grape variety as the main responsible factor for explaining total variability (63.6%) being malolactic fermentation responsible only for 4.0% Significance of Study: The results obtained from each of the three multivariate statistical approaches showed clearly ways of analyzing and handling large chemistry experimental data sets. When explained variables are available in the data set, the variance partitioning method could be considered as a step forward in the data analysis, providing a more solid and complete information concerning the variability on the sample system allowing a more objective result not possible by PCA and neural networks alone