2020
DOI: 10.3390/s20092687
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Selective Detection of Target Volatile Organic Compounds in Contaminated Air Using Sensor Array with Machine Learning: Aging Notes and Mold Smells in Simulated Automobile Interior Contaminant Gases

Abstract: We investigated the selective detection of target volatile organic compounds (VOCs) which are age-related body odors (namely, 2-nonenal, pelargonic acid, and diacetyl) and a fungal odor (namely, acetic acid) in the presence of interference VOCs from car interiors (namely, n-decane, and butyl acetate). We used eight semiconductive gas sensors as a sensor array; analyzing their signals using machine learning; principal-component analysis (PCA), and linear-discriminant analysis (LDA) as dimensionality-reduction m… Show more

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Cited by 27 publications
(18 citation statements)
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“…Acetone was reported to be capable of altering resistance of the polymeric film by altering the intermolecular packing. 55,58 A linear discriminant analysis 59,60 was conducted using response data from all eleven polymers, plus poly(thiophene-3butyric acid) and its methyl ester characterized previously 36 to differentiate NO 2 versus acetone, both of which induced currentincrease responses from all the polymers. All the tested concentrations of NO 2 (0.5 ppm, 1 ppm, 2 ppm, 5 ppm) and acetone (20 ppm, 50 ppm, 100 ppm, 200 ppm, 500 ppm) were included.…”
Section: G Acetone Sensitivity and Data Analysismentioning
confidence: 99%
“…Acetone was reported to be capable of altering resistance of the polymeric film by altering the intermolecular packing. 55,58 A linear discriminant analysis 59,60 was conducted using response data from all eleven polymers, plus poly(thiophene-3butyric acid) and its methyl ester characterized previously 36 to differentiate NO 2 versus acetone, both of which induced currentincrease responses from all the polymers. All the tested concentrations of NO 2 (0.5 ppm, 1 ppm, 2 ppm, 5 ppm) and acetone (20 ppm, 50 ppm, 100 ppm, 200 ppm, 500 ppm) were included.…”
Section: G Acetone Sensitivity and Data Analysismentioning
confidence: 99%
“…From the diagnostic perspective, test accuracy and validity of the classification model are of particular importance. Here, we applied an adapted multivariate classification analysis based on the random forest method, which has been used in various cases in the field of metabolomics [ 20 , 30 , 31 ]. In this context, some considerations were taken into account.…”
Section: Discussionmentioning
confidence: 99%
“…Afterward, the models are trained on the obtained signature patterns through appropriate cross-validation (CV) approaches for the realization of the best fit model with maximum accuracy. The final step is the accuracy testing of the trained models on the unknown dataset [122][123][124][125][126]. Various kinds of features and their extraction techniques for chemiresistive and FET sensor devices are discussed in the following sections.…”
Section: Machine Learning-based Smart Gas Sensorsmentioning
confidence: 99%
“…The hold-out and k-fold (stratified k-fold CV, leave-one-out (LOOCV), leave p-out (LPOCV), etc.) are the commonly reported CV techniques [122,125,126]. The selection of an appropriate CV technique for a particular dataset is the key to develop a highly efficient machine learning model.…”
Section: Machine Learning-based Smart Gas Sensorsmentioning
confidence: 99%