Detection of Mycobacterium tuberculosis (TB) in vitro and in situ using an electronic nose in combination with a neural network system

Pavlou, Alexandros; Magan, Naresh; Jones, Jeff Meecham; Brown, John; Klatser, P. R.; Turner, Anthony P. F.

doi:10.1016/j.bios.2004.03.002

Cited by 126 publications

(69 citation statements)

References 12 publications

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“…ANNs are computational modeling tools that have been extensively used in many disciplines to model complex problems [21]. They have been applied to E-nose data for the purpose of classification [22][23][24][25][26][27][28][29]. The trained ANN can be employed for classification of fish freshness and the identification of the day after catching.…”

Section: Neural Network Classifiersmentioning

confidence: 99%

Intelligent Fish Freshness Assessment

Hosseini

Luo

et al. 2008

Journal of Sensors

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Recommended by Subhas MukhopadhyayFish species identification and automated fish freshness assessment play important roles in fishery industry applications. This paper describes a method based on support vector machines (SVMs) to improve the performance of fish identification systems. The result is used for the assessment of fish freshness using artificial neural network (ANN). Identification of the fish species involves processing of the images of fish. The most efficient features were extracted and combined with the down-sampled version of the images to create a 1D input vector. Max-Win algorithm applied to the SVM-based classifiers has enhanced the reliability of sorting to 96.46%. The realisation of Cyranose 320 Electronic nose (E-nose), in order to evaluate the fish freshness in real-time, is experimented. Intelligent processing of the sensor patterns involves the use of a dedicated ANN for each species under study. The best estimation of freshness was provided by the most sensitive sensors. Data was collected from four selected species of fishes over a period of ten days. It was concluded that the performance can be increased using individual trained ANN for each specie. The proposed system has been successful in identifying the number of days after catching the fish with an accuracy of up to 91%.

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Section: Neural Network Classifiersmentioning

confidence: 99%

Intelligent Fish Freshness Assessment

Hosseini

Luo

et al. 2008

Journal of Sensors

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“…In more recent years, an increasing number of studies have focused on the application of sensor array-based devices (e-noses) to obtain volatile smell prints in order to discriminate between TB patients and noninfected controls (66)(67)(68)). An earlier study used electroconductive sensor-based e-nose technology for demonstrating the discrimination of M. tuberculosis from other bacterial species in sputum samples (66).…”

Section: Respiratory Infectionsmentioning

confidence: 99%

Clinical Application of Volatile Organic Compound Analysis for Detecting Infectious Diseases

2013

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SUMMARY This review article introduces the significance of testing of volatile organic compounds (VOCs) in clinical samples and summarizes important features of some of the technologies. Compared to other human diseases such as cancer, studies on VOC analysis in cases of infectious diseases are limited. Here, we have described results of studies which have used some of the appropriate technologies to evaluate VOC biomarkers and biomarker profiles associated with infections. The publications reviewed include important infections of the respiratory tract, gastrointestinal tract, urinary tract, and nasal cavity. The results highlight the use of VOC biomarker profiles resulting from certain infectious diseases in discriminating between infected and healthy subjects. Infection-related VOC profiles measured in exhaled breath as well as from headspaces of feces or urine samples are a source of information with respect to disease detection. The volatiles emitted in clinical matrices may on the one hand represent metabolites of the infecting pathogen or on the other hand reflect pathogen-induced host responses or, indeed, a combination of both. Because exhaled-breath samples are easy to collect and online instruments are commercially available, VOC analysis in exhaled breath appears to be a promising tool for noninvasive detection and monitoring of infectious diseases.

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“…In human medicine, 'artificial' or 'electronic' nose (e-nose) technology has been employed in several areas of medical diagnosis, including rapid detection of tuberculosis [1], Helicobacter pylori [2], bacterial sinusitis [3] and urinary tract infections [4,5]. In veterinary medicine, the usefulness of e-nose technology was principally demonstrated for the first time in 2005 by discriminating serum samples obtained from badgers and cattle infected with Mycobacterium bovis from those obtained in non-infected controls [6].…”

Section: Introductionmentioning

confidence: 99%