Odours metrology and industrial olfactometry
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The influence of key odorants of icewine on odor mixture perception and perceptual interactions was explored in a set of 90 mixtures of odorants, which varied in composition. The results showed that the addition of odorants affected both the characteristic odor of the odorants and other odor characteristics in the mixture, and several specific perceptual interactions were revealed for the first time. When one to six odorants were combined with the complex icewine odor, regardless of the combination, a maximum of two corresponding odor characteristics were affected in the mixture. Moreover, the changes in the overall odor profile were more pronounced for the addition of odorants than the omission of odorants, especially when less than three odorants were manipulated. These effects highlight the complexity of the perception of odor mixtures and their intricate effects on the processing of complex food aromas.
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N-butanol is used as a reference odor to train odor panelists and field odor sniffers to measure odor concentrations and intensities, respectively. It has different characteristics from that of livestock odors. Odors from headspace samples of stored swine manure and wastewater sludge were analyzed with GC/MS. Seventeen identified compounds were selected as candidate constituents of an artificial swine odor (ASO). Twenty-four formulations of these constituents were developed. Three trials were conducted to evaluate these 24 formulations based on six criteria: the formulation would be safe to human panelists, the odor character was similar to swine odor, the persistence was similar to that of livestock odor, and the formulation was chemically stable, would not contaminate the olfactometer, and was easy to prepare and apply. Tedlar bags were filled with 16 L of carbon-filtered air and 1.82 (L) ASO (equivalent to 40 ppm). Odor concentrations of all ASO samples were measured by an olfactometer. The most representative formulation contained reduced sulfur, fatty acid, aromatic alcohol, aliphatic hydrocarbon, indole, and aliphatic alcohol. The ASO character is similar to that of swine manure, has a similar detection threshold to n-butanol, has a low persistence, is safe for use by panelists, and does not contaminate the olfactometer.
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Current olfactometers can insert a target odor into the flow of odorless air as a pulse (i.e., replace odorless air with target odor for a very short time), but no previously designed olfactometer can insert a pulse of target odor into a flow of background odor (i.e., replace background odor with target odor for a very short time). To measure reaction time to a target odor during presentation of a background odor, we developed an expanded olfactometer by adding an attachment to an existing olfactometer. We conducted three experiments to evaluate the performance of the expanded olfactometer. Additionally, four volunteers participated in trial measurement of reaction time for detection of the target odor under background odor and odorless air conditions using the expanded olfactometer. We did not observe a significant difference in gas onset time or rise time of the target odor between background and odorless air conditions. Additionally, the gas onset time and rise time of the target odor were on the order of milliseconds, whereas the gas onset time and rise time of the background odor were on the order of seconds. The reaction time was marginally significantly longer under the background odor condition than the odorless air condition. We did not observe a significant difference in gas onset time or rise time of the target odor between the existing olfactometer and our expanded olfactometer. We succeeded in developing an attachment capable of inserting a target odor into a flow of background odor. Our results revealed that performance related to the presentation of the target odor was comparable between the existing and expanded olfactometers. To more rigorously examine the effect of background odor on detection speed of target odor, we intend to increase the number of participants in the near future.
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This study was conducted to evaluate the dilution accuracy of the dynamic olfactometer made in Republic of Korea and analyze the correlation of odor levels from the olfactometry method and Liquid Chromatography/Mass Spectrometry (LC/MS). The evaluation of dilution accuracy using CH₄ standard gas for the dynamic olfactometer at lower dilution ratios of 3, 10, 30, 100 and 300, and at higher dilution ratios of 100, 300, 1000, 3000 and 10000 showed the relative errors of 1.48~3.40% and 2.06~4.76% respectively showing a good dilution accuracy. Twenty odor samples from the stacks of odor-monitoring factories in the industrial complex located at the western coastal area of ROK were analyzed with the dynamic olfactometer for complex odor and LC/MS for five types of aldehydes, and a very weak correlation of R² = 0.1276 between OU(Odor Unit) from the olfactometer data and SOQ (Summation of Odor Quotient) from LCjMS data was obtained. Because of the complexity of the odor composition, using concentration of single or group of gases to represent odor level has not been proved to fully estimate the presence or level of odors. Therefore, the dynamic olfactometry which has a good dilution accuracy and a standardized odor evaluation system is considered as a very resonable method to assess complex odor.
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The aim of the present study was to estimate a prediction model for odor from pig production facilities based on measurements of odorants by Proton-Transfer-Reaction Mass spectrometry (PTR-MS). Odor measurements were performed at four different pig production facilities with and without odor abatement technologies using a newly developed mobile odor laboratory equipped with a PTR-MS for measuring odorants and an olfactometer for measuring the odor concentration by human panelists. A total of 115 odor measurements were carried out in the mobile laboratory and simultaneously air samples were collected in Nalophan bags and analyzed at accredited laboratories after 24 h. The dataset was divided into a calibration dataset containing 94 samples and a validation dataset containing 21 samples. The prediction model based on the measurements in the mobile laboratory was able to explain 74% of the variation in the odor concentration based on odorants, whereas the prediction models based on odor measurements with bag samples explained only 46–57%. This study is the first application of direct field olfactometry to livestock odor and emphasizes the importance of avoiding any bias from sample storage in studies of odor-odorant relationships. Application of the model on the validation dataset gave a high correlation between predicted and measured odor concentration (R2 = 0.77). Significant odorants in the prediction models include phenols and indoles. In conclusion, measurements of odorants on-site in pig production facilities is an alternative to dynamic olfactometry that can be applied for measuring odor from pig houses and the effects of odor abatement technologies.
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This study investigated the odor dilution rate at 15 locations around three schools in Seoul using an onsite olfactometer. In addition, odor intensity, odor quality, and hedonic tone by direct sensory method were measured along with measurement of the field odor dilution rate, and instrument analysis using odor sensor array and TD-GC was also measured. Onsite olfactometer measurements show that only one of the three schools measured odors exceeding the strict emission acceptance standard of 10 at three points. The average odor intensity at each point measured by the direct sensory method of five persons was in the range of 2.7 to 0.3. The difference in the number of odor dilution rates around schools in Seoul could be related to the level of income by region. The odor environment around each school was judged to be well managed in areas with higher income levels, indicating a lower odor dilution rate. The correlation coeffcient between the odor intensity measured by the direct sensory method and the onsite olfactometer was 0.79, indicating high correlation. The correlation coefficient of sensor array and TD-GC toward the odor intensity was -0.28 and 0.02, respectively. This suggests that a method based on a person's sense of smell should be introduced when measuring low-level odor dilution rates in non-industrial areas, such as school zones.
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Triatomines, vectors of Chagas Disease, are hematophagous insects. Efforts have been made to develop synthetic attractants based on vertebrate odor—to lure them into traps. However, because those lures are not practical or have low capture efficiency, they are not in use in control programs. Therefore, more work is needed to reach a practical and efficient odor lure. Recently, a three-component, CO 2 -free, synthetic blend of vertebrate odor (consisting of ammonia, l -(+)-lactic acid, and hexanoic acid), known as Sweetscent (Biogents AG, Regensburg, Germany), was shown to attract and capture triatomines in the laboratory. In this study, using a trap olfactometer and an odor blend with constituents similar to those of Sweetscent (delivered from low-density polyethylene sachets) we found that the odorant ratios of the mixtures have a strong effect in the capture of triatomines. The blend with the most efficient combination of odorant ratios evoked ca. 81% capture in two relevant triatomine species. In the case of the most effective odor mixtures, we measured the odor mass emission for the three components of the mixture and therefore were able to estimate the odorant ratios emitted that were responsible for such a high capture performance. Thus, in those mixtures, pentanoic acid was the main component (ca. 65 %) followed by ammonia (ca. 28%) and, l (+)-lactic acid (ca. 7 %). Our results are encouraging as efficient, practical, and cheap odor baits to trap triatomines in the field would be within reach. The odor-delivery system used should be improved to increase stability of odor emission.
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