A selective feature optimized multi-sensor based e-nose system detecting illegal drugs validated in diverse laboratory conditions

Noh, Hyung Wook; Jang, Yongwon; Park, Hwin Dol; kim, Dohyeun; Choi, Jae Hun; Ahn, Chang–Geun

doi:10.1016/j.snb.2023.133965

Cited by 6 publications

(2 citation statements)

References 49 publications

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“…The well-known problem of sensors is their lack of selectivity [ 6 , 7 , 8 , 9 ]. To overcome this problem, several approaches exist which have given rise to specific research.…”

Section: Introductionmentioning

confidence: 99%

Linear and Non-Linear Modelling Methods for a Gas Sensor Array Developed for Process Control Applications

Lakhmi,

Fischer,

Darves-Blanc

et al. 2024

Sensors

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New process developments linked to Power to X (energy storage or energy conversion to another form of energy) require tools to perform process monitoring. The main gases involved in these types of processes are H2, CO, CH4, and CO2. Because of the non-selectivity of the sensors, a multi-sensor matrix has been built in this work based on commercial sensors having very different transduction principles, and, therefore, providing richer information. To treat the data provided by the sensor array and extract gas mixture composition (nature and concentration), linear (Multi Linear Regression—Ordinary Least Square “MLR-OLS” and Multi Linear Regression—Partial Least Square “MLR-PLS”) and non-linear (Artificial Neural Network “ANN”) models have been built. The MLR-OLS model was disqualified during the training phase since it did not show good results even in the training phase, which could not lead to effective predictions during the validation phase. Then, the performances of MLR-PLS and ANN were evaluated with validation data. Good concentration predictions were obtained in both cases for all the involved analytes. However, in the case of methane, better prediction performances were obtained with ANN, which is consistent with the fact that the MOX sensor’s response to CH4 is logarithmic, whereas only linear sensor responses were obtained for the other analytes. Finally, prediction tests performed on one-year aged sensor platforms revealed that PLS model predictions on aged platforms mainly suffered from concentration offsets and that ANN predictions mainly suffered from a drop of sensitivity.

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“…The well-known problem of sensors is their lack of selectivity [ 6 , 7 , 8 , 9 ]. To overcome this problem, several approaches exist which have given rise to specific research.…”

Section: Introductionmentioning

confidence: 99%

Linear and Non-Linear Modelling Methods for a Gas Sensor Array Developed for Process Control Applications

Lakhmi,

Fischer,

Darves-Blanc

et al. 2024

Sensors

View full text Add to dashboard Cite

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“…Extracting effective odor features from the response signals and then training models are the two key steps for odor identification by using the E-nose. At present, the commonly used ways to extract the odor features are time-domain features [18,20,21], frequency domain features [22,23], curve fitting [24][25][26], and data compression [27,28]. However, the effectiveness of the feature-extraction methods is affected by many factors, such as the target gases and the application scenarios.…”

Section: Introductionmentioning

confidence: 99%

Identification of Adulterants in Chili Powder Based on the Histogram of Oriented Gradients Algorithm by Using an Electronic Nose

Peng,

Ba,

Zhang

et al. 2024

Applied Sciences

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Driven by economic interests, adding adulterations in chili powder is a problem which threatens people’s health. Thanks to its nondestructive, rapid, and portable advantages, electronic nose has more potential to be used for adulteration detection than the traditional methods. An approach for identifying the adulterants in chili powder was proposed in this paper. Firstly, an electronic nose system with 10 gas sensors was designed, and then the response images were drawn based on the response signals of the electronic nose. Afterwards, gas features were extracted from those response images by using a histogram of oriented gradients (HOG) algorithm. Finally, an SVM-based identification model was constructed to achieve the identification of plant adulterants in chili powder. The experimental results showed that the identification accuracy of the adulterant categories (almond shell, red beetroot, and tomato peel) based on the HOG features could reach up to 98.3%, and the identification results for adulterant content were 94.2%, 93.3%, and 95%, respectively. Furthermore, in order to compare the efficiency of the proposed identification approach, the widely used model AlexNet was also investigated and discussed.

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Electrochemical multisensor systems and arrays in the era of artificial intelligence

Voitechovič,

Pauliukaite

2023

Current Opinion in Electrochemistry

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A selective feature optimized multi-sensor based e-nose system detecting illegal drugs validated in diverse laboratory conditions

Cited by 6 publications

References 49 publications

Linear and Non-Linear Modelling Methods for a Gas Sensor Array Developed for Process Control Applications

Linear and Non-Linear Modelling Methods for a Gas Sensor Array Developed for Process Control Applications

Identification of Adulterants in Chili Powder Based on the Histogram of Oriented Gradients Algorithm by Using an Electronic Nose

Electrochemical multisensor systems and arrays in the era of artificial intelligence

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