Using Field Asymmetric Ion Mobility Spectrometry for Odor Assessment of Automobile Interior Components

Li, Juan; Gutierrez‐Osuna, Ricardo; Hodges, R.D.; Luckey, Gail; Crowell, J.; Schiffman, Susan S.; Nagle, H. Troy

doi:10.1109/jsen.2016.2568209

Cited by 15 publications

(12 citation statements)

References 22 publications

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“…One has to keep in mind that IMS measures volatile organic compounds (VOCs). Thus, if there is one or more VOCs in large quantities having similar molecular properties, and hence similar time of flight, in both scents, then IMS readings will be similar to each other [5]. For example, grapes contain β-Damascenone in significant quantities, which is the most powerful scent that is not fermentation related [26].…”

Section: Scent Classification With and Without Baseline Correctionmentioning

confidence: 99%

“…Ideally, the chosen eNose will enable real-time monitoring, quick analysis, show fast response time, show little to no temporal performance degradation, be relatively affordable, commercially available, and portable. One technology that fulfills all these requirements is ion-mobility spectrometry (IMS) [3,5,6]. It is a time of flight analytical technique [7], which enables temporal or physical separation of ionized molecules [8].…”

Section: Introductionmentioning

confidence: 99%

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Online Scent Classification by Ion-Mobility Spectrometry Sequences

Müller

Salminen

Kontunen

et al. 2019

Front. Appl. Math. Stat.

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For ion-mobility spectrometry (IMS)-based electronic noses (eNose) samples of scents are markedly time-dependent, with a transient phase and a highly volatile stable phase in certain conditions. At the same time, the samples depend on various environmental factors, such as temperature and humidity. This makes fast classification of scents challenging. The present aim was to develop and test an algorithm for online scent classification that mitigates these dependencies by using both baseline measurements and sequences of samples for classification. A classifier based on the K nearest neighbors approach was derived. The classifier is able to use measurements from both transient and stable phase, yields a label for the analyzed scent, and information on the trustworthiness of the returned label. In order to avoid the classifier being fooled by irrelevant features and to reduce the dimensionality of the feature space, principal component analysis was applied to the data. The classifier was tested with four food scents, each presented in two different ways to the IMS. By using baseline measurements, the misclassification rate was reduced from 20.0 to 13.3%. A second experiment showed that the used IMS type experiences device heterogeneity.

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Section: Scent Classification With and Without Baseline Correctionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Online Scent Classification by Ion-Mobility Spectrometry Sequences

Müller

Salminen

Kontunen

et al. 2019

Front. Appl. Math. Stat.

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“…Despite artificial sensory evaluation is direct to sense the odor intensity [19], but causes damage to the health of the valuators working for a long time. Moreover, the valuators are probably affected by various factors, such as subjective preferences, working condition, and physical health [7], [20]. Above all, the automobile manufacturers and part plants employ their own valuators to measure the odor intensity of the same product, which gives rise to disputes for the quality of product.…”

Section: Introductionmentioning

confidence: 99%

Quantification of Acrylonitrile Butadiene Styrene Odor Intensity Based on a Novel Odor Assessment System With a Sensor Array

et al. 2020

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As an engineering plastic, acrylonitrile butadiene styrene (ABS) has been widely used in the automobile trims. The odor intensity of ABS can be considered as an important reference for the quality of in-vehicle air. Currently, many automobile manufacturers employ their own testing methods to measure the odor intensity of the trims. Different rules lead that the market lacks a unified standard to evaluate the odor intensity. In this paper, a novel odor evaluation system was proposed to measure the odor intensity of ABS. According to coefficient of variation (CV), analysis of variance (ANOVA), and principal component analysis (PCA), eight sensors were selected to compose an array with stability, repeatability, and selectivity. By means of the pretreatment and the feature extraction, the odor features were quantified by grey relation analysis (GRA). Then, the regression models were constructed by extreme learning machine (ELM), random forest (RF), and support vector machine (SVM) to predict the odor intensity. The results indicated that the quantified data could describe the odor intensity accurately and be predicted well by three models. This study demonstrated that the system achieved perception and quantification of the odor intensity of ABS. In conclusion, a self-developed system was put forward, offering a new technique to evaluate the odor intensity, prospective to replace the manual testing.

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“…FAIMS has been used for odor analysis; for example, Surakka et al reported a system prototype employing IMS to record the odor of jasmine oil [20]. Li et al reported that FAIMS could be used for the odor assessment of interior automobile components [21]. However, gas mixture quantification using FAIMS has not yet been conducted, as previous researchers focused only on odor classification.…”

Section: Introductionmentioning

confidence: 99%

Ternary Gas Mixture Quantification Using Field Asymmetric Ion Mobility Spectrometry (FAIMS)

Yokoshiki

Nakamoto

2019

Sensors

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Gas mixture quantification is essential for the recording and reproducing odors, because an odor consists of multiple chemical compounds. Gas mixture quantification using field asymmetric ion mobility spectrometry (FAIMS) was studied. Acetone, ethanol, and diethyl ether were selected as components of a ternary gas mixture sample as representatives of the ketone, alcohol, and ether chemical classes, respectively. One hundred and twenty-five points with different concentrations were measured. The results were evaluated by error hypersurface, variance, and the coefficient of variation. The error hypersurface showed that it is possible to reach the target composition by following the error-hypersurface gradient. Successful convergence was achieved with the gradient descent method in a simulation based on the measurement data. This result verified the feasibility of the quantification of a gas mixture using FAIMS.

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Using Field Asymmetric Ion Mobility Spectrometry for Odor Assessment of Automobile Interior Components

Cited by 15 publications

References 22 publications

Online Scent Classification by Ion-Mobility Spectrometry Sequences

Online Scent Classification by Ion-Mobility Spectrometry Sequences

Quantification of Acrylonitrile Butadiene Styrene Odor Intensity Based on a Novel Odor Assessment System With a Sensor Array

Ternary Gas Mixture Quantification Using Field Asymmetric Ion Mobility Spectrometry (FAIMS)

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