Structure–activity relationships on the odor detectability of homologous carboxylic acids by humans

We measured concentration detection functions for the odor detectability of the homologs: formic, acetic, butyric, hexanoic, and octanoic acids. Subjects (14 ≤ n ≤ 18) comprised young (19–37 years), healthy, nonsmoker, and normosmic participants from both genders. Vapors were delivered by air dilution olfactometry, using a three-alternative forced-choice procedure against carbon-filtered air, and an ascending concentration approach. Delivered concentrations were established by gas chromatography (flame ionization detector) in parallel with testing. Group and individual olfactory functions were modeled by a sigmoid (logistic) equation from which two parameters are calculated: C, the odor detection threshold (ODT) and D, the steepness of the function. Thresholds declined with carbon chain length along formic, acetic, and butyric acid where they reached a minimum (ODTs = 514, 5.2, and 0.26 ppb by volume, respectively). Then, they increased for hexanoic (1.0 ppb) and octanoic (0.86 ppb) acid. Odor thresholds and interindividual differences in olfactory acuity among these young, normosmic participants were lower than traditionally thought and reported. No significant effects of gender on odor detectability were observed. The finding of an optimum molecular size for odor potency along homologs confirms a prediction made by a model of ODTs based on a solvation equation. We discuss the mechanistic implications of this model for the process of olfactory detection

Characterization of Italian honeys (Marche Region) on the basis of their mineral content and some typical quality parameters

<p>Abstract</p> <p>Background</p> <p>The characterization of three types of Marche (Italy) honeys (Acacia, Multifloral, Honeydew) was carried out on the basis of the their quality parameters (pH, sugar content, humidity) and mineral content (Na, K, Ca, Mg, Cu, Fe, and Mn). Pattern recognition methods such as principal components analysis (PCA) and linear discriminant analysis (LDA) were performed in order to classify honey samples whose botanical origins were different, and identify the most discriminant parameters. Lastly, using ANOVA and correlations for all parameters, significant differences between diverse types of honey were examined.</p> <p>Results</p> <p>Most of the samples' water content showed good maturity (98%) whilst pH values were in the range 3.50 – 4.21 confirming the good quality of the honeys analysed. Potassium was quantitatively the most relevant mineral (mean = 643 ppm), accounting for 79% of the total mineral content. The Ca, Na and Mg contents account for 14, 3 and 3% of the total mineral content respectively, while other minerals (Cu, Mn, Fe) were present at very low levels. PCA explained 75% or more of the variance with the first two PC variables. The variables with higher discrimination power according to the multivariate statistical procedure were Mg and pH. On the other hand, all samples of acacia and honeydew, and more than 90% of samples of multifloral type have been correctly classified using the LDA. ANOVA shows significant differences between diverse floral origins for all variables except sugar, moisture and Fe.</p> <p>Conclusion</p> <p>In general, the analytical results obtained for the Marche honeys indicate the products' high quality. The determination of physicochemical parameters and mineral content in combination with modern statistical techniques can be a useful tool for honey classification.</p

Relevant principal component analysis applied to the characterisation of Portuguese heather honey

The main purpose of this study was the characterisation of ‘Serra da Lousã’ heather honey by using novel statistical methodology, relevant principal component analysis, in order to assess the correlations between production year, locality and composition. Herein, we also report its chemical composition in terms of sugars, glycerol and ethanol, and physicochemical parameters. Sugars profiles from ‘Serra da Lousã’ heather and ‘Terra Quente de Tra´ s-os-Montes’ lavender honeys were compared and allowed the discrimination: ‘Serra da Lousã’ honeys do not contain sucrose, generally exhibit lower contents of turanose, trehalose and maltose and higher contents of fructose and glucose. Different localities from ‘Serra da Lousã ’ provided groups of samples with high and low glycerol contents. Glycerol and ethanol contents were revealed to be independent of the sugars profiles. These data and statistical models can be very useful in the comparison and detection of adulterations during the quality control analysis of ‘Serra da Lousã’ honey