Noninvasive Early Disease Diagnosis by Electronic-Nose and Related VOC-Detection Devices

Wilson, A. D.

doi:10.3390/bios10070073

Cited by 44 publications

(29 citation statements)

References 6 publications

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“…The most recent achievements of detection of various VOCs in medicine were published in 2020 by Wilson (48). Recently, the attention of researchers was focused on gastrointestinal and infected wound diseases.…”

Section: E-nose Applicationsmentioning

confidence: 99%

What is the “Status questionis” of the e-nose. The future of artificial nose

2021

J B & Bio Engine;

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An artificial nose (e-nose) is a multipotential electronic device, based on various sensors with the ability to recognize different odours, in the same way that the human olfactory sense does. An updated e-nose system will allow us to detect different oncological and/or degenerative diseases of the human being that today are diagnosed late. Other options would include providing specific information on the quality and condition of food, analysing and detecting the degree of environmental pollution, analysing perfumes and their essences, determining the composition and characteristics of certain beverages such as wine, tea, oil, cocoa and other products. The application of new technologies, such as artificial intelligence and nanotechnology, make it easier to distinguish many different odours in less time. In this paper, we have made a current investigation of the different types of e-nose existing today. Aim of the study Currently nanotechnology, artificial intelligence and computer science are tools that have revolutionary possibilities for the construction of a new e-nose device and progress over the current scopes. If we manage to unite the advantages provided by each of these new technologies that we have mentioned, we will be able to build a very useful device applicable in various fields such as health, food and beverage industry, perfumes and environment. Therefore, the objectives we have in this study are the following: To learn where artificial nose technology stands and what has been developed so far. Based on these findings, ask ourselves the following question: Is it possible to achieve an effective functioning or is it an unreachable project? To compile the majority of scientific articles published mainly in the last 10 years with examples of the use that has been made of artificial noses in the measurement of volatile compounds in the different fields mentioned. In the same way to gather the information published in the last years in relation to the usefulness, existence in the market and purposes of equipment that can measure the olfaction in the human being, what we will call the Smell-o-meter or olfactometer for human use. Material and Methods In the first part of this research we will gather most of the information existing so far in the international bibliography, as well as the achievements and utilities obtained to date. Following we will analyse all the new concepts related to e-nose devices that exist on sensors, gas chromatography, nanotechnology application, electronic engineering, materials and techniques as preliminary ideas for the development of the devices.

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Section: E-nose Applicationsmentioning

confidence: 99%

What is the “Status questionis” of the e-nose. The future of artificial nose

2021

J B & Bio Engine;

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“…To obviate these issues, many nature-inspired sensors have emerged that are bioreceptor-free. Some of the most notable examples that have made great progress include the electronic nose (Enose) [ 7 , 8 , 9 , 10 , 11 ] and electronic tongue (Etongue) [ 12 , 13 , 14 , 15 , 16 ]. Additionally, surface enhanced Raman spectroscopy (SERS)-based sensors have demonstrated incredible chemosensing ability [ 17 , 18 , 19 , 20 , 21 ].…”

Section: Introductionmentioning

confidence: 99%

Machine Learning Enhances the Performance of Bioreceptor-Free Biosensors

Schackart

Yoon

2021

Sensors

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Since their inception, biosensors have frequently employed simple regression models to calculate analyte composition based on the biosensor’s signal magnitude. Traditionally, bioreceptors provide excellent sensitivity and specificity to the biosensor. Increasingly, however, bioreceptor-free biosensors have been developed for a wide range of applications. Without a bioreceptor, maintaining strong specificity and a low limit of detection have become the major challenge. Machine learning (ML) has been introduced to improve the performance of these biosensors, effectively replacing the bioreceptor with modeling to gain specificity. Here, we present how ML has been used to enhance the performance of these bioreceptor-free biosensors. Particularly, we discuss how ML has been used for imaging, Enose and Etongue, and surface-enhanced Raman spectroscopy (SERS) biosensors. Notably, principal component analysis (PCA) combined with support vector machine (SVM) and various artificial neural network (ANN) algorithms have shown outstanding performance in a variety of tasks. We anticipate that ML will continue to improve the performance of bioreceptor-free biosensors, especially with the prospects of sharing trained models and cloud computing for mobile computation. To facilitate this, the biosensing community would benefit from increased contributions to open-access data repositories for biosensor data.

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“…Metabolomics studies the changes in the concentrations of specific metabolites in tissues and biological fluids. The metabolite profiles help understand the disease’s pathogenesis, and they can be used as biomarkers for diagnosis [ 29 , 30 , 31 , 32 , 33 ]. The most perspective direction in BRD diagnosis using metabolomics involves noninvasive methods when researching the metabolic profiles of exhaled breath gas [ 34 ], exhaled breath condensate [ 35 ], and nasal secretions [ 28 , 36 ].…”

Section: Introductionmentioning

confidence: 99%

Portable Electronic Nose for Analyzing the Smell of Nasal Secretions in Calves: Toward Noninvasive Diagnosis of Infectious Bronchopneumonia

Кучменко

Шуба

Умарханов

et al. 2021

Veterinary Sciences

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The paper demonstrates a new approach to identify healthy calves (“healthy”) and naturally occurring infectious bronchopneumonia (“sick”) calves by analysis of the gaseous phase over nasal secretions using 16 piezoelectric sensors in two portable devices. Samples of nasal secretions were obtained from 50 red-motley Holstein calves aged 14–42 days. Calves were subjected to rectal temperature measurements, clinical score according to the Wisconsin respiratory scoring chart, thoracic auscultation, and radiography (Carestream DR, New York, USA). Of the 50 calves, we included samples from 40 (20 “healthy” and 20 “sick”) in the training sample. The remaining ten calves (five “healthy” and five “sick”) were included in the test sample. It was possible to divide calves into “healthy” and “sick” groups according to the output data of the sensor arrays (maximum sensor signals and calculated parameters Ai/j) using the principal component linear discriminant analysis (PCA–LDA) with an accuracy of 100%. The adequacy of the PCA–LDA model was verified on a test sample. It was found that data of sensors with films of carbon nanotubes, zirconium nitrate, hydroxyapatite, methyl orange, bromocresol green, and Triton X-100 had the most significance for dividing samples into groups. The differences in the composition of the gaseous phase over the samples of nasal secretions for such a classification could be explained by the appearance or change in the concentrations of ketones, alcohols, organic carboxylic acids, aldehydes, amines, including cyclic amines or those with a branched hydrocarbon chain.

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Noninvasive Early Disease Diagnosis by Electronic-Nose and Related VOC-Detection Devices

Cited by 44 publications

References 6 publications

What is the “Status questionis” of the e-nose. The future of artificial nose

What is the “Status questionis” of the e-nose. The future of artificial nose

Machine Learning Enhances the Performance of Bioreceptor-Free Biosensors

Portable Electronic Nose for Analyzing the Smell of Nasal Secretions in Calves: Toward Noninvasive Diagnosis of Infectious Bronchopneumonia

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