Improving the Chemical Selectivity of an Electronic Nose to TNT, DNT and RDX Using Machine Learning

Gradišek, Anton; Midden, Marion van; Koterle, Matija; Prezelj, Vid; Strle, Drago; Štefane, Bogdan; Brodnik, Helena; Trifkovič, Mario; Kvasić, Ivan; Zupanič, Erik; Muševič, Igor

doi:10.3390/s19235207

Cited by 15 publications

(11 citation statements)

References 22 publications

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“…Such a predictive performance underscores the capability of the C-dot-IDE platform to detect individual gas targets in mixtures. Overall, the ML analysis outlined in Table 1 underscores an excellent predictive performance, on par or better than reported ML applications in chemometrics [41][42][43].…”

Section: Machine Learning Algorithm Applicationmentioning

confidence: 71%

Sniffing Bacteria with a Carbon-Dot Artificial Nose

et al. 2021

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Highlights Novel artificial nose based upon electrode-deposited carbon dots (C-dots). Significant selectivity and sensitivity determined by “polarity matching” between the C-dots and gas molecules. The C-dot artificial nose facilitates, for the first time, real-time, continuous monitoring of bacterial proliferation and discrimination among bacterial species, both between Gram-positive and Gram-negative bacteria and between specific strains. Machine learning algorithm furnishes excellent predictability both in the case of individual gases and for complex gas mixtures. Abstract Continuous, real-time monitoring and identification of bacteria through detection of microbially emitted volatile molecules are highly sought albeit elusive goals. We introduce an artificial nose for sensing and distinguishing vapor molecules, based upon recording the capacitance of interdigitated electrodes (IDEs) coated with carbon dots (C-dots) exhibiting different polarities. Exposure of the C-dot-IDEs to volatile molecules induced rapid capacitance changes that were intimately dependent upon the polarities of both gas molecules and the electrode-deposited C-dots. We deciphered the mechanism of capacitance transformations, specifically substitution of electrode-adsorbed water by gas molecules, with concomitant changes in capacitance related to both the polarity and dielectric constants of the vapor molecules tested. The C-dot-IDE gas sensor exhibited excellent selectivity, aided by application of machine learning algorithms. The capacitive C-dot-IDE sensor was employed to continuously monitor microbial proliferation, discriminating among bacteria through detection of distinctive “volatile compound fingerprint” for each bacterial species. The C-dot-IDE platform is robust, reusable, readily assembled from inexpensive building blocks and constitutes a versatile and powerful vehicle for gas sensing in general, bacterial monitoring in particular.

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Section: Machine Learning Algorithm Applicationmentioning

confidence: 71%

Sniffing Bacteria with a Carbon-Dot Artificial Nose

et al. 2021

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“…A common criticism of eNose and IMS technology is that they are unable to determine the identity and concentration of individual VOCs, except for combined-technology instruments [60]. It has therefore been argued that this "black box" approach relies too heavily on pattern-recognition algorithms [157]. Moreover, this method cannot yield a targeted VOC biomarker test for the diagnosis of a particular condition or disease [158].…”

Section: Discussionmentioning

confidence: 99%

Sniffing Out Urinary Tract Infection—Diagnosis Based on Volatile Organic Compounds and Smell Profile

2020

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Current available methods for the clinical diagnosis of urinary tract infection (UTI) rely on a urine dipstick test or culturing of pathogens. The dipstick test is rapid (available in 1–2 min), but has a low positive predictive value, while culturing is time-consuming and delays diagnosis (24–72 h between sample collection and pathogen identification). Due to this delay, broad-spectrum antibiotics are often prescribed immediately. The over-prescription of antibiotics should be limited, in order to prevent the development of antimicrobial resistance. As a result, there is a growing need for alternative diagnostic tools. This paper reviews applications of chemical-analysis instruments, such as gas chromatography–mass spectrometry (GC-MS), selected ion flow tube mass spectrometry (SIFT-MS), ion mobility spectrometry (IMS), field asymmetric ion mobility spectrometry (FAIMS) and electronic noses (eNoses) used for the diagnosis of UTI. These methods analyse volatile organic compounds (VOCs) that emanate from the headspace of collected urine samples to identify the bacterial pathogen and even determine the causative agent’s resistance to different antibiotics. There is great potential for these technologies to gain wide-spread and routine use in clinical settings, since the analysis can be automated, and test results can be available within minutes after sample collection. This could significantly reduce the necessity to prescribe broad-spectrum antibiotics and allow the faster and more effective use of narrow-spectrum antibiotics.

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“…175 Chemical selectivity was further improved using machine learning. 176 Zandieh et. al report the detection of RDX and PETN using a multi-modal sensor.…”

Section: Micro-electromechanical Systemsmentioning

confidence: 99%

“…Detection of TNT was possible at 3.5 ppt, within 1 s at room temperature . Chemical selectivity was further improved using machine learning …”

Section: Sensorsmentioning

confidence: 99%

Recent Developments in the Field of Explosive Trace Detection

2020

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Explosive trace detection (ETD) technologies play a vital role in maintaining national security. ETD remains an active research area with many analytical techniques in operational use. This review details the latest advances in animal olfactory, ion mobility spectrometry (IMS), Raman and colorimetric detection methods. Developments in optical, biological, electrochemical, mass and thermal sensors are also covered in addition to the use of nanomaterials technology. Commercially available systems are presented as examples of current detection capabilities and as benchmarks for improvement. Attention is also drawn to recent collaborative projects involving government, academia and industry, to highlight the emergence of multimodal screening approaches and applications. The objective of the review is to provide a comprehensive overview of ETD by; highlighting challenges in ETD and providing an understanding of the principles, advantages and limitations of each technology and relating this to current systems.

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Improving the Chemical Selectivity of an Electronic Nose to TNT, DNT and RDX Using Machine Learning

Cited by 15 publications

References 22 publications

Sniffing Bacteria with a Carbon-Dot Artificial Nose

Sniffing Bacteria with a Carbon-Dot Artificial Nose

Sniffing Out Urinary Tract Infection—Diagnosis Based on Volatile Organic Compounds and Smell Profile

Recent Developments in the Field of Explosive Trace Detection

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