An evaluation of masking nuisance odors from a source by chemical and sensory analyses

讨厌的人 遮罩(插图) 气味 感觉系统 环境科学 心理学 生物 生态学 艺术 认知心理学 神经科学 视觉艺术
作者
I. H. Suffet,V. Decottignies,Yubin Zhou,Yuge Bian,Tadeo Vitko
出处
期刊:Water Environment Research [Wiley]
卷期号:95 (7) 被引量:3
标识
DOI:10.1002/wer.10901
摘要

Abstract There are many products in the market advertised as masking agents used to overpower strong nuisance odors, such as in or around water resource recovery facilities, solid waste processing facilities, landfills, composting sites, and so forth. Very little is known about the chemical component of these masking agents because they are protected by trade secrets. This is a problem for the parties involved, as the process of choosing the most adequate agent for the particular odor source falls into guesswork. This paper demonstrates that it is possible to determine how effective the masking product would be before spending time and resources in trials. It proposes to show this by comparing the Weber–Fechner curves of the odor‐causing compounds known to be emitted at the facility with the curves from the potential masking agents prepared in the laboratory using an olfactometer. Several sensorial examples show that when the Weber–Fechner curves of the odorants and those of candidate masking agents are compared, it is possible to define the effectiveness of the masking agent tested. This is a novel use of the Weber–Fechner curves. The results show there is direct correlation between what is observed by a panel with real life odor samples subjected to incremental dilution and the Weber–Fechner odor intensity‐odor concentration curve interaction between the odorants involved. Future work characterizing additional potential masking compounds by Weber–Fechner accompanied by odor profiling with dynamic olfactometry should shed light on the definitive effectiveness of this method in predicting masking effects and discovering useful masking compounds. Practitioner Points Weber–Fechner curves provide relationships between odorant concentration and odor intensity. Dynamic olfactometry, in which real‐life air samples are sensorially analyzed by the odor profile method after subsequent dilutions, shows that odor masking occurs. Analyzing the Weber–Fechner curves of the odorants present in the dynamic olfactometry test show the existing odorant interactions. It is possible to predict the extent of the masking of potential compounds by comparing Weber–Fechner curves of masking agents against odorants causing nuisance. This methodology could help avoid spending resources in masking field trials that may result in further exacerbating the affected public.
最长约 10秒,即可获得该文献文件

科研通智能强力驱动
Strongly Powered by AbleSci AI
更新
PDF的下载单位、IP信息已删除 (2025-6-4)

科研通是完全免费的文献互助平台,具备全网最快的应助速度,最高的求助完成率。 对每一个文献求助,科研通都将尽心尽力,给求助人一个满意的交代。
实时播报
等DENG完成签到,获得积分10
刚刚
咖褐完成签到 ,获得积分10
1秒前
JUGG发布了新的文献求助10
2秒前
淡定自中发布了新的文献求助10
2秒前
2秒前
英俊的铭应助liuhao采纳,获得10
3秒前
3秒前
在水一方应助TristanGuan采纳,获得10
4秒前
5秒前
学术LJ完成签到,获得积分10
5秒前
九色可乐完成签到,获得积分10
6秒前
清秀豆芽发布了新的文献求助10
7秒前
8秒前
852应助老实天奇采纳,获得10
8秒前
科目三应助wewe采纳,获得10
9秒前
要减肥面包完成签到 ,获得积分10
10秒前
10秒前
12秒前
Sunny完成签到,获得积分10
12秒前
清新发布了新的文献求助10
12秒前
13秒前
积极念波发布了新的文献求助10
13秒前
13秒前
hahha完成签到 ,获得积分10
13秒前
13秒前
Wenzel发布了新的文献求助10
14秒前
15秒前
淡墨发布了新的文献求助10
15秒前
16秒前
汉堡包应助爱哭的小女孩采纳,获得10
17秒前
星辰大海应助luoyi采纳,获得10
17秒前
李健的小迷弟应助Rismond采纳,获得20
17秒前
贪玩星完成签到,获得积分10
18秒前
18秒前
20秒前
20秒前
积极念波完成签到,获得积分10
20秒前
20秒前
彬彬完成签到,获得积分10
21秒前
21秒前
高分求助中
(应助此贴封号)【重要!!请各用户(尤其是新用户)详细阅读】【科研通的精品贴汇总】 10000
Pipeline and riser loss of containment 2001 - 2020 (PARLOC 2020) 1000
Artificial Intelligence driven Materials Design 600
Comparing natural with chemical additive production 500
Machine Learning in Chemistry 500
Phylogenetic study of the order Polydesmida (Myriapoda: Diplopoda) 500
A Manual for the Identification of Plant Seeds and Fruits : Second revised edition 500
热门求助领域 (近24小时)
化学 医学 生物 材料科学 工程类 有机化学 内科学 生物化学 物理 计算机科学 纳米技术 遗传学 基因 复合材料 化学工程 物理化学 病理 催化作用 免疫学 量子力学
热门帖子
关注 科研通微信公众号,转发送积分 5194958
求助须知:如何正确求助?哪些是违规求助? 4377124
关于积分的说明 13631420
捐赠科研通 4232342
什么是DOI,文献DOI怎么找? 2321565
邀请新用户注册赠送积分活动 1319686
关于科研通互助平台的介绍 1270113