Application of electronic nose as a non-invasive technique for odor fingerprinting and detection of bacterial foodborne pathogens: a review

Bonah, Ernest; Huang, Xingyi; Aheto, Joshua Harrington; Osae, Richard

doi:10.1007/s13197-019-04143-4

Cited by 86 publications

(46 citation statements)

References 94 publications

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“…Bacteria are known to emit varied volatile molecules, which types and concentrations are strain-dependent [1,2]. Bacterially produced volatile compounds such as alcohols, aldehydes, ketones and others have been used as microbial biomarkers [3,4], and bacterially emitted volatile metabolite mixtures have been employed as distinctive "odor profile" vehicles for bacterial identification [5][6][7]. In particular, colorimetric arrays for sensing volatile compounds have been developed, capable of distinguishing among different bacterial strains [8].…”

Section: Introductionmentioning

confidence: 99%

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: Introductionmentioning

confidence: 99%

Sniffing Bacteria with a Carbon-Dot Artificial Nose

et al. 2021

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“…Pan et al [16] used a combination of electronic nose and GC-MS to detect and classify postharvest pathogenic fungal diseases of strawberry fruits, and the accuracy rate of discriminating the types of strawberry fruit fungal infection was 96.6%. Bonah used electronic nose to classify and identify bacterial foodborne pathogens [17] and summarized the methods and pattern recognition tools used by electronic nose in the detection of foodborne pathogens [18]. Biondi et al [19] used the electronic nose to detect potato ring rot and brown rot, where the linear discriminant analysis (LDA) used passive sampling with an accuracy of 81.3%.…”

Section: Introductionmentioning

confidence: 99%

Classification for Penicillium expansum Spoilage and Defect in Apples by Electronic Nose Combined with Chemometrics

Guo

Chen

et al. 2020

Sensors

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It is crucial for the efficacy of the apple storage to apply methods like electronic nose systems for detection and prediction of spoilage or infection by Penicillium expansum. Based on the acquisition of electronic nose signals, selected sensitive feature sensors of spoilage apple and all sensors were analyzed and compared by the recognition effect. Principal component analysis (PCA), principle component analysis-discriminant analysis (PCA-DA), linear discriminant analysis (LDA), partial least squares discriminate analysis (PLS-DA) and K-nearest neighbor (KNN) were used to establish the classification model of apple with different degrees of corruption. PCA-DA has the best prediction, the accuracy of training set and prediction set was 100% and 97.22%, respectively. synergy interval (SI), genetic algorithm (GA) and competitive adaptive reweighted sampling (CARS) are three selection methods used to accurately and quickly extract appropriate feature variables, while constructing a PLS model to predict plaque area. Among them, the PLS model with unique variables was optimized by CARS method, and the best prediction result of the area of the rotten apple was obtained. The best results are as follows: Rc = 0.953, root mean square error of calibration (RMSEC) = 1.28, Rp = 0.972, root mean square error of prediction (RMSEP) = 1.01. The results demonstrated that the electronic nose has a potential application in the classification of rotten apples and the quantitative detection of spoilage area.

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“…So far, this review has focused on the hardware stage, and in particular on VOC sensitive materials, whereas this section aims to provide the reader with a complementary overview of the basic attributes of e-noses and their application. As the scope of this topic is vast, the section is not intended to be exhaustive and for deeper insight into e-noses, their principles, and application, we invite the reader to revise recent reviews, for instance, [ 148 , 149 , 150 , 151 , 152 ].…”

Section: Enabling the Materials Properties For Their Practical Usementioning

confidence: 99%

VOCs Sensing by Metal Oxides, Conductive Polymers, and Carbon-Based Materials

et al. 2021

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This review summarizes the recent research efforts and developments in nanomaterials for sensing volatile organic compounds (VOCs). The discussion focuses on key materials such as metal oxides (e.g., ZnO, SnO2, TiO2 WO3), conductive polymers (e.g., polypyrrole, polythiophene, poly(3,4-ethylenedioxythiophene)), and carbon-based materials (e.g., graphene, graphene oxide, carbon nanotubes), and their mutual combination due to their representativeness in VOCs sensing. Moreover, it delves into the main characteristics and tuning of these materials to achieve enhanced functionality (sensitivity, selectivity, speed of response, and stability). The usual synthesis methods and their advantages towards their integration with microsystems for practical applications are also remarked on. The literature survey shows the most successful systems include structured morphologies, particularly hierarchical structures at the nanometric scale, with intentionally introduced tunable “decorative impurities” or well-defined interfaces forming bilayer structures. These groups of modified or functionalized structures, in which metal oxides are still the main protagonists either as host or guest elements, have proved improvements in VOCs sensing. The work also identifies the need to explore new hybrid material combinations, as well as the convenience of incorporating other transducing principles further than resistive that allow the exploitation of mixed output concepts (e.g., electric, optic, mechanic).

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Application of electronic nose as a non-invasive technique for odor fingerprinting and detection of bacterial foodborne pathogens: a review

Cited by 86 publications

References 94 publications

Sniffing Bacteria with a Carbon-Dot Artificial Nose

Sniffing Bacteria with a Carbon-Dot Artificial Nose

Classification for Penicillium expansum Spoilage and Defect in Apples by Electronic Nose Combined with Chemometrics

VOCs Sensing by Metal Oxides, Conductive Polymers, and Carbon-Based Materials

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