Dataset Information

Impact of a Diverse Combination of Metal Oxide Gas Sensors on Machine Learning-Based Gas Recognition in Mixed Gases.

ABSTRACT: A challenge for chemiresistive-type gas sensors distinguishing mixture gases is that for highly accurate recognition, massive data processing acquired from various types of sensor configurations must be considered. The impact of data processing is indeed ineffective and time-consuming. Herein, we systemically investigate the effect of the selectivity for a target gas on the prediction accuracy of gas concentration via machine learning based on a support vector machine model. The selectivity factor S(X) of a gas sensor for a target gas "X" is introduced to reveal the correlation between the prediction accuracy and selectivity of gas sensors. The presented work suggests that (i) the strong correlation between the selectivity factor and prediction accuracy has a proportional relationship, (ii) the enhancement of the prediction accuracy of an elemental sensor with a low sensitivity factor can be attained by a complementary combination of the other sensor with a high selectivity factor, and (iii) it can also be boosted by combining the sensor having even a low selectivity factor.

SUBMITTER: Bae G

PROVIDER: S-EPMC8444204 | biostudies-literature | 2021 Sep

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Impact of a Diverse Combination of Metal Oxide Gas Sensors on Machine Learning-Based Gas Recognition in Mixed Gases.

Bae Garam G Kim Minji M Song Wooseok W Myung Sung S Lee Sun Sook SS An Ki-Seok KS

ACS omega 20210903 36

A challenge for chemiresistive-type gas sensors distinguishing mixture gases is that for highly accurate recognition, massive data processing acquired from various types of sensor configurations must be considered. The impact of data processing is indeed ineffective and time-consuming. Herein, we systemically investigate the effect of the selectivity for a target gas on the prediction accuracy of gas concentration <i>via</i> machine learning based on a support vector machine model. The selectivi ...[more]

PMID: 34549116

Similar Datasets

Project description:Manufacturers of nanomaterial-enabled products need models of endpoints that are relevant to human safety to support the "safe by design" paradigm and avoid late-stage attrition. Increasingly, embryonic zebrafish (Danio Rerio) are recognised as a key human safety relevant in vivo test system. Hence, machine learning models were developed for identifying metal oxide nanomaterials causing lethality to embryonic zebrafish up to 24 hours post-fertilisation, or excess lethality in the period of 24-120 hours post-fertilisation, at concentrations of 250 ppm or less. Models were developed using data from the Nanomaterial Biological-Interactions Knowledgebase for a dataset of 44 diverse, coated and uncoated metal or, in one case, metalloid oxide nanomaterials. Different modelling approaches were evaluated using nested cross-validation on this dataset. Models were initially developed for both lethality endpoints using multiple descriptors representing the composition of the core, shell and surface functional groups, as well as particle characteristics. However, interestingly, the 24 hours post-fertilisation data were found to be harder to predict, which could reflect different exposure routes. Hence, subsequent analysis focused on the prediction of excess lethality at 120 hours-post fertilisation. The use of two data augmentation approaches, applied for the first time in nano-QSAR research, was explored, yet both failed to boost predictive performance. Interestingly, it was found that comparable results to those originally obtained using multiple descriptors could be obtained using a model based upon a single, simple descriptor: the Pauling electronegativity of the metal atom. Since it is widely recognised that a variety of intrinsic and extrinsic nanomaterial characteristics contribute to their toxicological effects, this is a surprising finding. This may partly reflect the need to investigate more sophisticated descriptors in future studies. Future studies are also required to examine how robust these modelling results are on truly external data, which were not used to select the single descriptor model. This will require further laboratory work to generate comparable data to those studied herein.

Dataset Information

Impact of a Diverse Combination of Metal Oxide Gas Sensors on Machine Learning-Based Gas Recognition in Mixed Gases.

Publications

Impact of a Diverse Combination of Metal Oxide Gas Sensors on Machine Learning-Based Gas Recognition in Mixed Gases.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets