• Environmental Analysis Health and Toxicology
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• v.15 no.3
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• pp.81-91
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• 2000

Residents who live near petrochemical industrial areas are exposed to a variety of petrochemicals, including benzene or benzene-containing liquids. It is a serious concern because some VOCs are carcinogens naturally present in petroleum and gasoline. The aim of this study was to assess the exposure to VOCs, measured by personal/indoor/outdoor air sampling, and to estimate the relationship between the air samples and biological monitoring data. Through biological monitoring, we investigated VOCs in blood and s-phenylmercapturic acid (s-PMA) , minor urinary metabolites of benzene. The external benzene exposure of subjects was measured using passive dosimeters and urinary s-PMA and blood-benzene were determined by GC/MS. More than 80% of subjects were detected for m-xylene, ethylbenzene, and toluene in blood samples and not detected at all for chloroform, 1 , 1 , 1 -trichloroethylene, and tetrachloroethylene. The mean concentration of benzene in the breathing zone of residents was 6.3 $\mu\textrm{g}$/m$^3$, personal, indoor and outdoor concentrations were strongly correlated to each other. s-PMA detected in all subject samples was affected by personal exposure (p< 0.05) and the level was different by age (p< 0.01). Blood benzene was not affected by external benzene during these periods .

• Journal of Sensor Science and Technology
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• v.33 no.2
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• pp.112-116
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• 2024

Gas-sensor technology for volatile organic compounds (VOC) biomarker detection offers significant advantages for noninvasive diagnostics, including rapid response time and low operational costs, exhibiting promising potential for disease diagnosis. Colorimetric gas sensors, which enable intuitive analysis of gas concentrations through changes in color, present additional benefits for the development of personal diagnostic kits. However, the traditional method of visually monitoring these sensors can limit quantitative analysis and consistency in detection threshold evaluation, potentially affecting diagnostic accuracy. To address this, we developed a machine vision platform based on metal-organic framework (MOF) for colorimetric gas sensor arrays, designed to accurately detect disease-related VOC biomarkers. This platform integrates a CMOS camera module, gas chamber, and colorimetric MOF sensor jig to quantitatively assess color changes. A specialized machine vision algorithm accurately identifies the color-change Region of Interest (ROI) from the captured images and monitors the color trends. Performance evaluation was conducted through experiments using a platform with four types of low-concentration standard gases. A limit-of-detection (LoD) at 100 ppb level was observed. This approach significantly enhances the potential for non-invasive and accurate disease diagnosis by detecting low-concentration VOC biomarkers and offers a novel diagnostic tool.