Masking effects on iso-valeric acid recognition by sub-threshold odor mixture.

Masking unpleasant odors with pleasant-smelling odorants has a long history and is utilized in various industries, including perfumery and consumer products. However, the effectiveness of odor masking is idiosyncratic and temporary. In this study, we employed Sniff olfactometry (SO) to investigate the psychophysics of masking using brief 70 ms stimulations with mixtures of the mal-odorant iso-valeric acid (IVA) and different masking agents. IVA is a component of human sweat that can overpower its smell and is often associated with unpleasant descriptors such as "gym locker," "smelly feet," "dirty clothes," and so on. Traditionally, high concentrations of pleasant-smelling odorants are used to mitigate the unpleasantness of IVA in situations involving clothing or environments contaminated with IVA. To examine the masking effects of sub-threshold levels of various masking agents (neohivernal, geraniol, florhydral, decanal, iso-longifolanone, methyl iso-eugenol, and s-limonene) on IVA, we conducted experiments using SO to measure the probability of recognizing IVA after 70 ms stimulations with headspaces containing mixtures of super-threshold concentrations of IVA and sub-threshold concentrations of IVA suppressors. The study involved nine subjects, and on average, a single masking agent was found to decrease IVA recognition probability by 14-72%. Moreover, a sub-threshold odor mixture consisting of 6 masking agents demonstrated a substantial decrease in IVA recognition, with a reduction of 96%.

Computing Odor Images.

This perspective examines psychophysical methods that may reveal the algorithms that encode odor images by integrating current data from sensory measurement into a computational model of odor perception. There is evidence that algorithms used by the nervous system to process odor sensations require input from only a few odorants, between three and eight. Furthermore, the number of recognizable odors in foods that contribute anything to the aroma of all foods is approximately 250. This may imply that it is the ratio of a small number of key odorants (KOs) that create a multitude of food odors. Studies with large mixtures of odorants (formulated to be of equal potency) show that a subject's ability to detect individual odorants in these mixtures was vanishingly small. These large mixtures had weak and nondescript but similar odor character. If only a few stimulants are used to represent complex images, it is direct evidence of the simplicity and therefore the tractability of the computational process.

Functional odor classification through a medicinal chemistry approach.

Crucial for any hypothesis about odor coding is the classification and prediction of sensory qualities in chemical compounds. The relationship between perceptual quality and molecular structure has occupied olfactory scientists throughout the 20th century, but details of the mechanism remain elusive. Odor molecules are typically organic compounds of low molecular weight that may be aliphatic or aromatic, may be saturated or unsaturated, and may have diverse functional polar groups. However, many molecules conforming to these characteristics are odorless. One approach recently used to solve this problem was to apply machine learning strategies to a large set of odors and human classifiers in an attempt to find common and unique chemical features that would predict a chemical's odor. We use an alternative method that relies more on the biological responses of olfactory sensory neurons and then applies the principles of medicinal chemistry, a technique widely used in drug discovery. We demonstrate the effectiveness of this strategy through a classification for esters, an important odorant for the creation of flavor in wine. Our findings indicate that computational approaches that do not account for biological responses will be plagued by both false positives and false negatives and fail to provide meaningful mechanistic data. However, the two approaches used in tandem could resolve many of the paradoxes in odor perception.

Characterization of cachaça and rum aroma.

Cachaça, the most popular alcoholic beverage in Brazil, is a sugar cane spirit similar to rum. Its production is around 2 billion liters per year, of which <1% is exported. Although rum is similar to cachaça its flavor difference is easily recognizable. Using gas chromatography-olfactometry (GCO) to separate and characterize the odorants present in cachaça and rum, these two sugar cane products were compared and standards identified to use in a descriptive sensory analysis (DSA). In the DSA cachaça was more intense in the grassy, spicy, sulfury, and vinegar descriptors, whereas apple and caramel were the same in both rum and cachaça. The GCO data for the apple-smelling compounds beta-damascenone along with ethyl butyrate, isobutyrate, and 2-methylbutyrate were at the same potency in both cachaça and rum, whereas the spicy-smelling eugenol, 4-ethylguaiacol, and 2,4-nonadienal were much more potent in cachaça.

Identification of metallic-smelling 1-octen-3-one and 1-nonen-3-one from solutions of ferrous sulfate.

Taste threshold tests of ferrous sulfate (FeSO4) solutions have been confounded by the presence of putative odorants. To detect the presence of odorants released from these solutions solid-phase microextraction (SPME) was used to collect volatiles in the headspace above FeSO4 solutions. Gas chromatography-olfactometry of samples collected over three time periods (1, 5, and 16 h) and at two temperatures (22 and 37 degrees C) revealed the presence of several metallic-smelling odorants in the headspace. Using authentic standards, two of the odorants were conclusively identified as 1-octen-3-one and 1-nonen-3-one. Trace levels of other odorants were also detected, but dilution experiments indicated that 1-nonen-3-one was at least 10 times more potent than anything else released from the solutions. 1-Octen-3-one and 1-nonen-3-one are excellent candidates for the metallic odor responses often observed in threshold testing of solutions of FeSO4.

Sensory threshold of 1,1,6-trimethyl-1,2-dihydronaphthalene (TDN) and concentrations in young Riesling and non-Riesling wines.

1,1,6-Trimethyl-1,2-dihydronaphthalene (TDN) is well-known to contribute "petrol" aromas to aged Riesling wines, but its prevalence and contribution to young Riesling or non-Riesling wines is not well understood. TDN concentrations were measured in 1-3-year-old varietal wines produced from Cabernet franc (n = 14 wines), Chardonnay (17), Cabernet Sauvignon (4), Gewurztraminer (4), Merlot (9), Pinot gris (6), Pinot noir (9), Riesling (28), or Sauvignon blanc (6). TDN concentrations in the Riesling wines, 6.4 ± 3.8 µg/L, were significantly higher than in all other varietals, 1.3 ± 0.8 µg/L. The odor detection thresholds for TDN were then determined in both model wine and a neutral white wine. Group sensory thresholds were found to be the same in both matrices, 2 µg/L, indicating little masking of TDN due to the odorants in the neutral white. The TDN sensory threshold was a factor of 10 below the previously reported odor threshold. On the basis of this revised threshold, 27 of 28 Riesling wines had suprathreshold TDN, whereas only 7 of 69 non-Riesling wines had suprathreshold TDN. The monoterpenes linalool and geraniol were also measured in the Riesling wines, and odor activity values (OAVs) were calculated for the monoterpenes and TDN. The OAV for TDN was higher than for the monoterpenes in 25 of 28 Riesling wines.

Comparison of odor-active compounds in grapes and wines from vitis vinifera and non-foxy American grape species.

Native American grape (Vitis) species have many desirable properties for winegrape breeding, but hybrids of these non-vinifera wild grapes with Vitis vinifera often have undesirable aromas. Other than the foxy-smelling compounds in Vitis labrusca and Vitis rotundifolia , the aromas inherent to American Vitis species are not well characterized. In this paper, the key odorants in wine produced from the American grape species Vitis riparia and Vitis cinerea were characterized in comparison to wine produced from European winegrapes (V. vinifera). Volatile compounds were extracted by solid-phase microextraction (SPME) and identified by gas chromatography-olfactometry/mass spectrometry (GC-O/MS). On the basis of flavor dilution values, most grape-derived compounds with fruity and floral aromas were at similar potency, but non-vinifera wines had higher concentrations of odorants with vegetative and earthy aromas: eugenol, cis-3-hexenol, 1,8-cineole, 3-isobutyl-2-methoxypyrazine (IBMP), and 3-isopropyl-2-methoxypyrazine (IPMP). Elevated concentrations of these compounds in non-vinifera wines were confirmed by quantitative GC-MS. Concentrations of IBMP and IPMP were well above sensory threshold in both non-vinifera wines. In a follow-up study, IBMP and IPMP were surveyed in 31 accessions of V. riparia, V. rupestris, and V. cinerea. Some accessions had concentrations of >350 pg/g IBMP or >30 pg/g IPMP, well above concentrations reported in previous studies of harvest-ripe vinifera grapes. Methyl anthranilate and 2-aminoacetophenone, key odorants responsible for the foxiness of V. labrusca grapes, were undetectable in both the V. riparia and V. cinerea wines (<10 µg/L).

Potential use of chemical cues for colony-mate recognition in the big brown bat, Eptesicus fuscus.

Bats should benefit from recognition of their roost-mates when colonies form stable social units that persist over time. We used Y-maze experiments and gas chromatography-olfactometry (GC-O) to evaluate whether female big brown bats Eptesicus fuscus (Chiroptera: Vespertilionidae) use chemical cues to distinguish among conspecifics. In dual-choice Y-maze experiments, females chose the scent of another female from their own roost over a conspecific female from a different roost in a majority of trials. Analysis of total body odors using GC-O suggests that individuals from a given colony may share a more common odor signature with roost-mates than with non-roost-mate conspecifics. Using four principle components derived from 15 odor variables, discriminant function analysis correctly assigned most individuals to the correct colony.