The Regular Interaction Pattern among Odorants of the Same Type and Its Application in Odor Intensity Assessment

Abstract

The olfactory evaluation function (e.g., odor intensity rating) of e-nose is always one of the most challenging issues in researches about odor pollution monitoring. But odor is normally produced by a set of stimuli, and odor interactions among constituents significantly influenced their mixture’s odor intensity. This study investigated the odor interaction principle in odor mixtures of aldehydes and esters, respectively. Then, a modified vector model (MVM) was proposed and it successfully demonstrated the similarity of the odor interaction pattern among odorants of the same type. Based on the regular interaction pattern, unlike a determined empirical model only fit for a specific odor mixture in conventional approaches, the MVM distinctly simplified the odor intensity prediction of odor mixtures. Furthermore, the MVM also provided a way of directly converting constituents’ chemical concentrations to their mixture’s odor intensity. By combining the MVM with usual data-processing algorithm of e-nose, a new e-nose system was established for an odor intensity rating. Compared with instrumental analysis and human assessor, it exhibited accuracy well in both quantitative analysis (Pearson correlation coefficient was 0.999 for individual aldehydes (n = 12), 0.996 for their binary mixtures (n = 36) and 0.990 for their ternary mixtures (n = 60)) and odor intensity assessment (Pearson correlation coefficient was 0.980 for individual aldehydes (n = 15), 0.973 for their binary mixtures (n = 24), and 0.888 for their ternary mixtures (n = 25)). Thus, the observed regular interaction pattern is considered an important foundation for accelerating extensive application of olfactory evaluation in odor pollution monitoring.

Visual Analysis of Odor Interaction Based on Support Vector Regression Method

“…As typical odor pollutants in odor sources like landfills and sewage treatment plants, odor data (i.e., odor threshold, Table 1; olfactory measured odor intensity and chemical concentration) of ethyl acetate (EA), butyl acetate (BA), ethyl butyrate (EB), propionaldehyde (PA), n-valeraldehyde (VA), n-heptaldehyde (HEP), benzene (B), toluene (T), ethylbenzene (E), and some of their binary mixtures were collected from our previous studies [23][24][25]. In these experiments, the odor samples were prepared through transferring a certain amount of standard gas to an odor-free plastic bag (3 L volume; Sinodour, Tianjin, China) and diluted with purified air.…”

“…Although many studies have established empirical models to explain the odor interaction phenomenon and made conclusions, we still hope to develop more analytical methods through the reasonable use of machine learning methods. Since it has been proved that there is a simple linear relationship between OI and lnOAV of an individual substance, we think that using the lnOAV value to represent the content of a component is also helpful in odor interaction studies [23,24]. As illustrated in Figure 2a, c, and d, scatter plots of the relationship between each component's content (in the form of lnOAV) and the odor interaction degree (in the form of OI reduction values; i.e., the color of each dot) was plotted.…”

Electronic nose – an instrument for odour nuisances monitoring

“…The coefficient α found in the equations (3) and (4) is approximately constant for one pair of mixture components. Recently, studies on the determination of this coefficient for compounds from the group of aldehydes, esters or aromatic hydrocarbons were carried out by researchers who determined its value at the level of: 0.1, 0.2 and 0.3 respectively [13].…”

Appraisal and identification of different sources of smell by primary school children in the air quality test chamber of the SenseLab

“…In the last years, scientists have focused on developing devices analogue to human senses, such as electronic noses that once calibrated they can be used to perform odour assessment on a continuous basis at a minimum cost (Hudon, Guy, and Hermia 2000;Szulczynski et al 2018). However, the range of odour mixtures, concentrations and intensities that the device can detect is still limited (Yan et al 2017). Due to this limitation the use of these devices is still restricted at the moment to monitor environmental odours (Yan et al 2017).…”

“…However, the range of odour mixtures, concentrations and intensities that the device can detect is still limited (Yan et al 2017). Due to this limitation the use of these devices is still restricted at the moment to monitor environmental odours (Yan et al 2017). Scientists recommend to combine odour measurement procedures using the human nose as detector together with a scientific method and instruments (Brattoli et al 2011;Walker 2001).…”