Sensor array based measurement technique for fast-responding cigarette smoke analysis

Ködderitzsch, Peter; Bischoff, R.; Veitenhansl, Petra; Lorenz, W.; Bischoff, G.

doi:10.1016/j.snb.2004.11.007

Cited by 12 publications

(4 citation statements)

References 28 publications

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“…However, for the in-field and on-site analysis of cigarettes, simpler and portable devices such as E-nose systems are demanding. Using a biomimetic nose system based on colorimetric sensor arrays to analyze cigarettes is rarely reported, and nearly all reports are based on artificial E-nose sensing systems [36,[39][40][41]. To the best of our knowledge, this work is the first report on using a paper-based O-nose for the analysis of cigarettes.…”

Section: Introductionmentioning

confidence: 99%

A Paper-Based Biomimetic Sensing Device for the Discrimination of Original and Fraudulent Cigarette Brands Using Mixtures of MoS2 Quantum Dots and Organic Dyes

2023

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In this study, we investigated the combined effects of MoS2 QDs’ catalytic properties and the colorimetric responses of organic reagents to create a sniffing device based on the sensor array concept of the mammalian olfactory system. The aim was to differentiate the volatile organic compounds (VOCs) present in cigarette smoke. The designed optical nose device was utilized for the classification of various cigarette VOCs. Unsupervised Principal Component Analysis (PCA) and supervised Linear Discriminant Analysis (LDA) methods were employed for data analysis. The LDA analysis showed promising results, with 100% accuracy in both training and cross-validation. To validate the sensor’s performance, we assessed its ability to discriminate between five cigarette brands, achieving 100% accuracy in the training set and 82% in the cross-validation set. Additionally, we focused on studying four popular Iranian cigarette brands (Bahman Kootah, Omega, Montana Gold, and Williams), including fraudulent samples. Impressively, the developed sensor array achieved a perfect 100% accuracy in distinguishing these brands and detecting fraud. We further analyzed a total of 126 cigarette samples, including both original and fraudulent ones, using LDA with a matrix size of (126 × 27). The resulting LDA model demonstrated an accuracy of 98%. Our proposed analytical procedure is characterized by its efficiency, affordability, user-friendliness, and reliability. The selectivity exhibited by the developed sensor array positions it as a valuable tool for differentiating between original and counterfeit cigarettes, thus aiding in border control efforts worldwide.

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Section: Introductionmentioning

confidence: 99%

A Paper-Based Biomimetic Sensing Device for the Discrimination of Original and Fraudulent Cigarette Brands Using Mixtures of MoS2 Quantum Dots and Organic Dyes

2023

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“…The measurements done by these stations are based on high performance industrial gas analyzers, which offer an excellent selectivity and precision towards targeted gases. However their high price and heavy weight make their use complicated [1], especially for some application where selectivity and precision are not required [2] like pollution meshing [3] or source detection [4].…”

Section: Introductionmentioning

confidence: 99%

Development of a normalized multi-sensors system for low cost on-line atmospheric pollution detection

Barakeh

Breuil

Redon

et al. 2017

Sensors and Actuators B: Chemical

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International audienceAir Pollution monitoring and measurement are generally done using sampling techniques and analysis equipment often heavy, complex and expensive. Although these methods offer a high measurement precision which is essential to answer standards requirements, they are not adapted for quality oriented applications where simple information with low precision can be sufficient. The use of semiconductor gas sensors networks can provide the answer for a “low cost” system intended for such applications in air pollution detection fields. Three identical portable autonomous sensors arrays were built, each containing nine commercial semiconductor sensors especially chosen to detect a large range of pollutants usually encountered in ambient air and for a large part of them regulated. In order to overcome the temporal instability and the lack of reproducibility of these sensors, a calibration and normalisation procedure was developed. The obtained systems were used for on-site pollution monitoring in association with the French National Network of Accredited Associations for Air Quality Monitoring (AASQA). Gathered data from sensors systems and network data (NO, NO₂, CO, PM2,5, …) were treated using nonlinear regression algorithms like Neural Networks with an original “fuzzy logic” type pre-treatment in order to compute a model able to predict the membership degree for three predefined pollution categories: traffic, urban and photochemical pollution, along with a pollution index describing the severity of the predominant pollution. The prediction rate was estimated system per system, and site per site for six sites. It has been shown that it was possible to obtain a quasi-universal model with a success rate over 80%

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“…17 Moreover, electronic nose systems have been successfully used to distinguish different cigarettes. 16,18 Based on 18 metal oxide semiconductor sensors, an electronic nose (Fox 4000) changes the electrical resistances of its sensors when these sensors are exposed to volatile substances, thus generating an analytical signal. 19,20 It is widely used to establish prediction models such as to assess the harvesting season of peaches, 21 to predict the chemical parameters of controlled oxidation tallow, 22 and to evaluate the sensory qualities of pork.…”

Section: Introductionmentioning

confidence: 99%

A rapid and novel method for predicting nicotine alkaloids in tobacco through electronic nose and partial least-squares regression analysis

Lin

Zhang

2016

Anal. Methods

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Sensor array based measurement technique for fast-responding cigarette smoke analysis

Cited by 12 publications

References 28 publications

A Paper-Based Biomimetic Sensing Device for the Discrimination of Original and Fraudulent Cigarette Brands Using Mixtures of MoS2 Quantum Dots and Organic Dyes

A Paper-Based Biomimetic Sensing Device for the Discrimination of Original and Fraudulent Cigarette Brands Using Mixtures of MoS2 Quantum Dots and Organic Dyes

Development of a normalized multi-sensors system for low cost on-line atmospheric pollution detection

A rapid and novel method for predicting nicotine alkaloids in tobacco through electronic nose and partial least-squares regression analysis

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