Browse
Submit Data
Databases
API
Help

Dataset Information

39 Views

0 Connections

0 Citations

0 Reanalyses

0 Downloads

Omics score: 0

A dataset on odor intensity and odor pleasantness of 222 binary mixtures of 72 key food odorants rated by a sensory panel of 30 trained assessors.

ABSTRACT: This paper describes data collected on a set of 222 binary mixtures, based on a set of 72 odorants chiefly found in food, rated by 30 selected and trained assessors for odor intensity and pleasantness. The data included odor intensity (IAB) and pleasantness (PAB) of the mixtures, the intensity (IA, IB) and the pleasantness (PA, PB) of the two components. Moreover, the intensity (IAmix, IBmix) of the two components' odor perceived within the mixture are included. The quality of the dataset was evaluated by checking subjects' performance and by testing repeatability using the 24 duplicated trials for each attribute. This set of experimental data would be especially valuable to investigate theories of odor mixture perception in human and to test new models to predict odor perception of odor mixtures.

SUBMITTER: Ma Y

PROVIDER: S-EPMC8144660 | biostudies-literature | 2021 Jun

REPOSITORIES: biostudies-literature

ACCESS DATA

Json Xml

Publications

A dataset on odor intensity and odor pleasantness of 222 binary mixtures of 72 key food odorants rated by a sensory panel of 30 trained assessors.

Ma Yue Y Tang Ke K Xu Yan Y Thomas-Danguin Thierry T

Data in brief 20210515

This paper describes data collected on a set of 222 binary mixtures, based on a set of 72 odorants chiefly found in food, rated by 30 selected and trained assessors for odor intensity and pleasantness. The data included odor intensity (IAB) and pleasantness (PAB) of the mixtures, the intensity (IA, IB) and the pleasantness (PA, PB) of the two components. Moreover, the intensity (IAmix, IBmix) of the two components' odor perceived within the mixture are included. The quality of the dataset was ev ...[more]

PMID: 34041322

Similar Datasets

Predicting odor pleasantness with an electronic nose.

Project description:A primary goal for artificial nose (eNose) technology is to report perceptual qualities of novel odors. Currently, however, eNoses primarily detect and discriminate between odorants they previously "learned". We tuned an eNose to human odor pleasantness estimates. We then used the eNose to predict the pleasantness of novel odorants, and tested these predictions in naïve subjects who had not participated in the tuning procedure. We found that our apparatus generated odorant pleasantness ratings with above 80% similarity to average human ratings, and with above 90% accuracy at discriminating between categorically pleasant or unpleasant odorants. Similar results were obtained in two cultures, native Israeli and native Ethiopian, without retuning of the apparatus. These findings suggest that unlike in vision and audition, in olfaction there is a systematic predictable link between stimulus structure and stimulus pleasantness. This goes in contrast to the popular notion that odorant pleasantness is completely subjective, and may provide a new method for odor screening and environmental monitoring, as well as a critical building block for digital transmission of smell.

| S-EPMC2855315 | biostudies-literature

Odor Characterization of White Wines Produced from Indigenous Greek Grape Varieties Using the Frequency of Attribute Citation Method with Trained Assessors.

Project description:The aim of this study was to investigate the sensory aroma profiles of white wines of the indigenous Greek grape varieties Assyrtiko, Malagousia, Moschofilero, and Roditis. Twenty-three panelists evaluated 17 wines of the aforementioned varieties using the frequency of attribute citation method. Three indices were calculated to assess panel performance in terms of reproducibility. Correspondence analysis and cluster analysis were employed to investigate the sensory space of the wines. Samples of the Roditis variety were characterized mainly by Banana and Vanilla odors; Assyrtiko samples had Earthy, Mushroom, and Nutty odors, as well as Lemon and Honey for some of the samples. Malagousia wines were described as having Lemon, Grapefruit, and Citrus blossom character, and they shared some descriptors with Assyrtiko wines, such as Mushroom and Earthy, and some with Moschofilero samples, i.e., floral and citrus notes. All Moschofilero wines exhibited a floral odor profile: specifically, Rose, Jasmine, or more Citrus blossom-like. Moreover, some Moschofilero samples also revealed a Grapefruit, Lemon, and/or Earthy character, while others expressed Honey notes. In conclusion, despite common characteristics found within varieties, some samples of different varieties exhibited overlapping profiles, and in some cases, samples of the same variety were quite different from each other.

| S-EPMC7600498 | biostudies-literature

Behavioral Evidence for Enhanced Processing of the Minor Component of Binary Odor Mixtures in Larval Drosophila.

Project description:A fundamental problem in deciding between mutually exclusive options is that the decision needs to be categorical although the properties of the options often differ but in grade. We developed an experimental handle to study this aspect of behavior organization. Larval Drosophila were trained such that in one set of animals odor A was rewarded, but odor B was not (A+/B), whereas a second set of animals was trained reciprocally (A/B+). We then measured the preference of the larvae either for A, or for B, or for "morphed" mixtures of A and B, that is for mixtures differing in the ratio of the two components. As expected, the larvae showed higher preference when only the previously rewarded odor was presented than when only the previously unrewarded odor was presented. For mixtures of A and B that differed in the ratio of the two components, the major component dominated preference behavior-but it dominated less than expected from a linear relationship between mixture ratio and preference behavior. This suggests that a minor component can have an enhanced impact in a mixture, relative to such a linear expectation. The current paradigm may prove useful in understanding how nervous systems generate discrete outputs in the face of inputs that differ only gradually.

| S-EPMC5672140 | biostudies-literature

Noise and accustomation: A pilot study of trained assessors' olfactory performance.

Project description:While recent studies suggest an influence of noise on olfactory performance, it is unclear as to what extent the influence varies between subjects who are accustomed to noise and those who are not. Two groups of panelists were selected: a University panel usually working under silent conditions and an abattoir panel usually working on the slaughter line with abattoir noise. Odor discrimination, odor identification, and odor detection thresholds were studied. Furthermore, a sensory quality control task using 40 boar samples was performed. All tests were accomplished both with and without extraneous noise recorded at an abattoir (70 dB) using headphones. Contrary to the researchers' expectations, abattoir noise hardly affected the olfactory tests nor was the quality control task impaired. Abattoir noise did not influence the perceived intensity of boar taint and the classification results of the testers, regardless of whether they were accustomed to such noise or not. The results indicate that sensory quality control can be conducted in a manufacturing environment with constant noise without diminishing the assessors' performance.

| S-EPMC5381871 | biostudies-literature

Odor Pleasantness Modulates Functional Connectivity in the Olfactory Hedonic Processing Network.

Project description:Olfactory hedonic evaluation is the primary dimension of olfactory perception and thus central to our sense of smell. It involves complex interactions between brain regions associated with sensory, affective and reward processing. Despite a recent increase in interest, several aspects of olfactory hedonic evaluation remain ambiguous: uncertainty surrounds the communication between, and interaction among, brain areas during hedonic evaluation of olfactory stimuli with different levels of pleasantness, as well as the corresponding supporting oscillatory mechanisms. In our study we investigated changes in functional interactions among brain areas in response to odor stimuli using electroencephalography (EEG). To this goal, functional connectivity networks were estimated based on phase synchronization between EEG signals using the weighted phase lag index (wPLI). Graph theoretic metrics were subsequently used to quantify the resulting changes in functional connectivity of relevant brain regions involved in olfactory hedonic evaluation. Our results indicate that odor stimuli of different hedonic values evoke significantly different interaction patterns among brain regions within the olfactory cortex, as well as in the anterior cingulate and orbitofrontal cortices. Furthermore, significant hemispheric laterality effects have been observed in the prefrontal and anterior cingulate cortices, specifically in the beta ((13-30) Hz) and gamma ((30-40) Hz) frequency bands.

| S-EPMC9599424 | biostudies-literature

Influence of the Chemical Structure on Odor Qualities and Odor Thresholds of Halogenated Guaiacol-Derived Odorants.

Project description:Chlorinated guaiacol derivatives are found in waste water of pulp mills using chlorine in the bleaching process of wood pulp. They can also be detected in fish tissue, possibly causing off-odors. To date, there is no systematic investigation on the odor properties of halogenated guaiacol derivatives. To close this gap, odor thresholds in air and odor qualities of 14 compounds were determined by gas chromatography-olfactometry. Overall, the investigated compounds elicited smells that are characteristic for guaiacol, namely smoky, sweet, vanilla-like, but also medicinal and plaster-like. Their odor thresholds in air were, however, very low, ranging from 0.00072 to 23 ng/Lair. The lowest thresholds were found for 5-chloro- and 5-bromoguaiacol, followed by 4,5-dichloro- and 6-chloroguaiacol. Moreover, some inter-individual differences in odor threshold values could be observed, with the highest variations having been recorded for the individual values of 5-iodo- and 4-bromoguaiacol.

| S-EPMC5741668 | biostudies-literature

Honeybees learn odour mixtures via a selection of key odorants.

Project description:The honeybee has to detect, process and learn numerous complex odours from her natural environment on a daily basis. Most of these odours are floral scents, which are mixtures of dozens of different odorants. To date, it is still unclear how the bee brain unravels the complex information contained in scent mixtures.This study investigates learning of complex odour mixtures in honeybees using a simple olfactory conditioning procedure, the Proboscis-Extension-Reflex (PER) paradigm. Restrained honeybees were trained to three scent mixtures composed of 14 floral odorants each, and then tested with the individual odorants of each mixture. Bees did not respond to all odorants of a mixture equally: They responded well to a selection of key odorants, which were unique for each of the three scent mixtures. Bees showed less or very little response to the other odorants of the mixtures. The bees' response to mixtures composed of only the key odorants was as good as to the original mixtures of 14 odorants. A mixture composed of the other, non-key-odorants elicited a significantly lower response. Neither an odorant's volatility or molecular structure, nor learning efficiencies for individual odorants affected whether an odorant became a key odorant for a particular mixture. Odorant concentration had a positive effect, with odorants at high concentration likely to become key odorants.Our study suggests that the brain processes complex scent mixtures by predominantly learning information from selected key odorants. Our observations on key odorant learning lend significant support to previous work on olfactory learning and mixture processing in honeybees.

| S-EPMC2817008 | biostudies-literature

Odorant mixtures elicit less variable and faster responses than pure odorants.

Project description:In natural environments, odors are typically mixtures of several different chemical compounds. However, the implications of mixtures for odor processing have not been fully investigated. We have extended a standard olfactory receptor model to mixtures and found through its mathematical analysis that odorant-evoked activity patterns are more stable across concentrations and first-spike latencies of receptor neurons are shorter for mixtures than for pure odorants. Shorter first-spike latencies arise from the nonlinear dependence of binding rate on odorant concentration, commonly described by the Hill coefficient, while the more stable activity patterns result from the competition between different ligands for receptor sites. These results are consistent with observations from numerical simulations and physiological recordings in the olfactory system of insects. Our results suggest that mixtures allow faster and more reliable olfactory coding, which could be one of the reasons why animals often use mixtures in chemical signaling.

| S-EPMC6287832 | biostudies-literature

Characterization and coding of behaviorally significant odor mixtures.

Project description:For animals to execute odor-driven behaviors, the olfactory system must process complex odor signals and maintain stimulus identity in the face of constantly changing odor intensities [1-5]. Surprisingly, how the olfactory system maintains identity of complex odors is unclear [6-10]. We took advantage of the plant-pollinator relationship between the Sacred Datura (Datura wrightii) and the moth Manduca sexta[11, 12] to determine how olfactory networks in this insect's brain represent odor mixtures. We combined gas chromatography and neural-ensemble recording in the moth's antennal lobe to examine population codes for the floral mixture and its fractionated components. Although the floral scent of D. wrightii comprises at least 60 compounds, only nine of those elicited robust neural responses. Behavioral experiments confirmed that these nine odorants mediate flower-foraging behaviors, but only as a mixture. Moreover, the mixture evoked equivalent foraging behaviors over a 1000-fold range in dilution, suggesting a singular percept across this concentration range. Furthermore, neural-ensemble recordings in the moth's antennal lobe revealed that reliable encoding of the floral mixture is organized through synchronized activity distributed across a population of glomerular coding units, and this timing mechanism may bind the features of a complex stimulus into a coherent odor percept.

| S-EPMC2677194 | biostudies-literature

Predicting the response of olfactory sensory neurons to odor mixtures from single odor response.

Project description:The response of olfactory receptor neurons to odor mixtures is not well understood. Here, using experimental constraints, we investigate the mathematical structure of the odor response space and its consequences. The analysis suggests that the odor response space is 3-dimensional, and predicts that the dose-response curve of an odor receptor can be obtained, in most cases, from three primary components with specific properties. This opens the way to an objective procedure to obtain specific olfactory receptor responses by manipulating mixtures in a mathematically predictable manner. This result is general and applies, independently of the number of odor components, to any olfactory sensory neuron type with a response curve that can be represented as a sigmoidal function of the odor concentration.

| S-EPMC4823664 | biostudies-literature

OmicsDI is part of the ELIXIR infrastructure

OmicsDI is an Elixir interoperability service. Learn more ›

Tweets

OmicsDI Databases

PRIDE
PeptideAtlas
MassIVE
JPOST Repository
Physiome Model Repository

EGA
EVA
ENA
LINCS
PAXDB
Cell Collective

MetaboLights
Metabolomics Workbench
MetabolomeExpress
GNPS
BioModels
FAIRDOMHub

ArrayExpress
dbGaP
ExpressionAtlas
GEO
NODE

Information

Databases
Help
API
Contact us
Code on GitHub
Terms of use
Submit Data