Duffy Emer, Cauven Emme, Morrin Aoife
INSIGHT SFI Research Centre for Data Analytics, National Centre for Sensor Research, School of Chemical Sciences, Dublin City University, Glasnevin, Dublin 9, Ireland.
School of Natural Science, Fontys University of Applied Sciences, Romdom 1, 5612AP Eindhoven, The Netherlands.
ACS Omega. 2021 Mar 8;6(11):7394-7401. doi: 10.1021/acsomega.0c05667. eCollection 2021 Mar 23.
Measurement of cooking-associated air pollution indoors is an integral part of exposure monitoring and human health risk assessment. There is a need for easy to use, fast, and economical detection systems to quantify the various emissions from different sources in the home. Addressing this challenge, a colorimetric sensor array (CSA) is reported as a new method to characterize volatile organic compounds produced from cooking, a major contributor to indoor air pollution. The sensor array is composed of pH indicators and aniline dyes from classical spot tests, which enabled molecular recognition of a variety of aldehydes, ketones, and carboxylic acids as demonstrated by hierarchical clustering and principal component analyses. To demonstrate the concept, these CSAs were employed for differentiation of emissions from heated cooking oils (sunflower, rapeseed, olive, and groundnut oils). Sensor results were validated by gas chromatography-mass spectrometry analysis, highlighting the potential of the sensor array for evaluating cooking emissions as a source of indoor air pollution.
室内烹饪相关空气污染的测量是暴露监测和人类健康风险评估的一个组成部分。需要易于使用、快速且经济的检测系统来量化家庭中不同来源的各种排放物。为应对这一挑战，一种比色传感器阵列（CSA）被报道为一种表征烹饪产生的挥发性有机化合物的新方法，烹饪是室内空气污染的主要来源。该传感器阵列由经典斑点试验中的pH指示剂和苯胺染料组成，通过层次聚类和主成分分析表明，它能够对多种醛、酮和羧酸进行分子识别。为了验证这一概念，这些CSA被用于区分加热食用油（向日葵油、菜籽油、橄榄油和花生油）的排放物。传感器结果通过气相色谱 - 质谱分析得到验证，突出了该传感器阵列在评估作为室内空气污染来源的烹饪排放物方面的潜力。
