An Electronic Nose for Royal Delicious Apple Quality Assessment – A Tri-layer Approach

Ezhilan, Madeshwari; Nesakumar, Noel; Babu, K. Jayanth; Srinandan, C. S.; Rayappan, John Bosco Balaguru

doi:10.1016/j.foodres.2018.04.009

Cited by 55 publications

(17 citation statements)

References 23 publications

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“…2 Acetone is also one of the signicant biomarkers indicating the freshness levels of fruits and vegetables. 3 From a diagnostic perspective, the presence of trace level acetone (1.8 ppm) in human exhaled breath indicates the presence of Diabetic Ketoacidosis (DKA). 4 The concentration of a specic marker present in the exhaled breath is directly related to the metabolic disorders in human beings.…”

Section: Introductionmentioning

confidence: 99%

Development of an acetone sensor using nanostructured Co₃O₄ thin films for exhaled breath analysis

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Section: Introductionmentioning

confidence: 99%

Development of an acetone sensor using nanostructured Co₃O₄ thin films for exhaled breath analysis

et al. 2019

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“…E‐nose has played an important role in many food applications such as but not limited to food safety and quality analysis in beverages, fruits and vegetables, and other foodstuffs. Food safety and quality applications include freshness determination (Chen, Zhang, & Guo, ; Xiao et al, ), adulterant detection (Ghasemi‐Varnamkhasti et al, ; Wang, Li, et al, ; Wang, Wang, et al, ), microorganism discrimination (Balasubramanian, Amamcharla, & Shin, ; Ezhilan, Nesakumar, Jayanth Babu, Srinandan, & Rayappan, ), fungal deterioration (Gancarz et al, ) and flavor detection (Dai et al, ; Guan, Zhao, Lin, & Zou, ).…”

Section: Introductionmentioning

confidence: 99%

“…E-nose has played an important role in many food applications such as but not limited to food safety and quality analysis in beverages, fruits and vegetables, and other foodstuffs. Food safety and quality applications include freshness determination (Chen, Zhang, & Guo, 2019;, adulterant detection , microorganism discrimination (Balasubramanian, Amamcharla, & Shin, 2016;Ezhilan, Nesakumar, Jayanth Babu, Srinandan, & Rayappan, 2018), fungal deterioration (Gancarz et al, 2017a) and flavor detection (Dai et al, 2018;Guan, Zhao, Lin, & Zou, 2014). E-noses systems based on metal-oxide-semiconductor (MOS) gas sensors have been employed for volatile detection emitted from microorganisms (Balasubramanian et al, 2012).…”

Section: Introductionmentioning

confidence: 99%

Electronic nose classification and differentiation of bacterial foodborne pathogens based on support vector machine optimized with particle swarm optimization algorithm

Bonah

Huang

Ren

et al. 2019

J Food Process Engineering

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Rapid detection of bacterial foodborne pathogens is crucial in reducing the incidence of diseases associated with food contaminated with pathogens and toxins. This article presents a classification model of support vector machine (SVM) together with a metaheuristic framework using genetic algorithm (GA), particle swarm optimization (PSO), and grid searching (GS) optimization algorithms for bacterial foodborne pathogen classification and differentiation. LDA and SVM showed classification accuracies of training (90.3% and 91%) and prediction (89.5% and 90.6%), respectively using the Matlab classification learner app. Optimization of the modeling parameters c and g for SVM were performed to increase efficiency and classification accuracy. Simulated results show classification accuracies of 100% (training set) and 98.95% (prediction set) for five selected bacterial pathogens acquired using electronic nose dataset and PSO‐SVM model. Electronic nose recognition system made up of 12 metal oxide semiconductor sensors produced different distinctive response signals for each bacterial and could differentiate Escherichia coli, Escherichia coli O157: H7, Listeria monocytogenes, Salmonella enteritidis, and Salmonella Typhimurium. PSO‐SVM algorithm can be efficiently used in bacterial discrimination at the species and strain level by electronic nose and has the good ability both in learning and in generalization. Practical Applications Existing microbial methods depend on traditional culture‐based methods which are time‐wise lengthy, require trained and qualified personnel, and are not suitable as point‐of‐use (POU) sensing devices. The application of electronic nose for the detection and classification of bacterial foodborne pathogens have shown great promise and proven to be effective with increasing sensitivity and selectivity as compared to traditional methods. The ability for E‐nose to discriminate between individual bacteria colonies at both species and strain level is of great public health importance since most bacteria have many strains. Virulence and pathogenicity are often associated with only a subset of these strains and it is essential for a method to be able to differentiate between pathogenic and nonpathogenic strains during a foodborne outbreak.

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“…Nowadays, the machine olfaction systems applying in the food industry for such purposes, which imitates the human olfactory system and its use has become increasingly popular. In this method, by using an array of gas sensors, distinctions are detected among the tested samples (Ezhilan, Nesakumar, Babu, Srinandan, & Rayappan, 2018;Farmanesh, Mohtasebi, & Omid, 2019;Loutfi, Coradeschi, Mani, Shankar, & Rayappan, 2015). The olfaction machine is a system comprised of chemical sensors and is used in combination with pattern recognition techniques to detect simple and complex odors.…”

mentioning

confidence: 99%

Application of an olfactory system to detect and distinguish bitter chocolates with different percentages of cocoa

Nouri

Mohtasebi

Jahanbakhshi

2019

J Food Process Engineering

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Chocolate is one of the most consumed products in the world. Chocolate is a mixture of roasted cocoa beans and ultrafine particles of sugar (or without containing cocoa butter). The mixture is supplied with different percentages of cocoa in the market. In this research, the ability of an olfactory system is evaluated as a fast, nondestructive, and low‐cost method to detect bitter chocolates with different cocoa percentages. For this purpose, bitter chocolates with three different percentages of cocoa (78, 85, and 96%) were selected for evaluation. Separation rate of electronic nose (E‐nose) dataset obtained 99%, by using principal components analysis method. To assess the classification accuracy of the bitter chocolate samples, linear discriminant analysis, and support vector machine applied. The results showed that both methods were able to classify bitter chocolates of different percentages of cocoa with the highest accuracy of 100%. This research demonstrates the potential of using the olfactory system as a fast, nondestructive, low‐cost, and reliable method for distinguishing and categorizing different cocoa percentages in bitter chocolates with high precision. Practical applications Most researches were focused on a specific percentage of cocoa, while there are few reports on the detection of different percentages of cocoa of the same type. In our study, the linear discriminant analysis and support vector machine methods were employed to the distinguishing of various percentages of cocoa of same type (bitter chocolate), and it was observed that both methods achieved the better classification performance, which indicated the feasibility of distinguishing different percentages by E‐nose. It is well‐known that the detection of different cocoa percentages from the same type would take a long time and require developed instrumental laborites with experts, thus it is of great significance to establish a rapid nondestructive detection method for the related study in the field of the cocoa industry.

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An Electronic Nose for Royal Delicious Apple Quality Assessment – A Tri-layer Approach

Cited by 55 publications

References 23 publications

Development of an acetone sensor using nanostructured Co₃O₄ thin films for exhaled breath analysis

Development of an acetone sensor using nanostructured Co₃O₄ thin films for exhaled breath analysis

Electronic nose classification and differentiation of bacterial foodborne pathogens based on support vector machine optimized with particle swarm optimization algorithm

Application of an olfactory system to detect and distinguish bitter chocolates with different percentages of cocoa

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An Electronic Nose for Royal Delicious Apple Quality Assessment – A Tri-layer Approach

Cited by 55 publications

References 23 publications

Development of an acetone sensor using nanostructured Co3O4 thin films for exhaled breath analysis

Development of an acetone sensor using nanostructured Co3O4 thin films for exhaled breath analysis

Electronic nose classification and differentiation of bacterial foodborne pathogens based on support vector machine optimized with particle swarm optimization algorithm

Application of an olfactory system to detect and distinguish bitter chocolates with different percentages of cocoa

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Development of an acetone sensor using nanostructured Co₃O₄ thin films for exhaled breath analysis

Development of an acetone sensor using nanostructured Co₃O₄ thin films for exhaled breath analysis