Characterization of Volatile Organic Compounds of Vinegars with Novel Electronic Nose System Combined with Multivariate Analysis

Guan, Binbin; Zhao, Jiewen; Hao, Lin; Zhang, Xiaobo

doi:10.1007/s12161-013-9715-4

Cited by 35 publications

(17 citation statements)

References 35 publications

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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%

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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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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“…In recent years, EN detection technique has aroused increasing research interest, especially in food, such as vinegars, 44,45 truffle flavored oils, 34 cherry tomato juices, 46 Chinese green tea. 47 As it shown in Figure 6, EN system's structure consists of 3 main components: signal control and its collection (U1), sensory arrays (U2) and gas supply device (U3).…”

Section: En Detection Systemmentioning

confidence: 99%

Kiwi fruit (Actinidia chinensis) quality determination based on surface acoustic wave resonator combined with electronic nose

Liu

Hui

2015

Bioengineered

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Keywords: electronic nose, frequency, Kiwi fruit quality, signal-to-noise ratio, surface acoustic wave resonatorIn this study, electronic nose (EN) combined with a 433 MHz surface acoustic wave resonator (SAWR) was used to determine Kiwi fruit quality under 12-day storage. EN responses to Kiwi samples were measured and analyzed by principal component analysis (PCA) and stochastic resonance (SR) methods. SAWR frequency eigen values were also measured to predict freshness. Kiwi fruit sample's weight loss index and human sensory evaluation were examined to characteristic its quality and freshness. Kiwi fruit's quality predictive models based on EN, SAWR, and EN combined with SAWR were developed, respectively. Weight loss and human sensory evaluation results demonstrated that Kiwi fruit's quality decline and overall acceptance decrease during the storage. Experiment result indicated that the PCA method could qualitatively discriminate all Kiwi fruit samples with different storage time. Both SR and SAWR frequency analysis methods could successfully discriminate samples with high regression coefficients (R = 0.98093 and R = 0.99014, respectively). The validation experiment results showed that the mixed predictive model developed using EN combined with SAWR present higher quality prediction accuracy than the model developed either by EN or by SAWR. This method exhibits some advantages including high accuracy, non-destructive, low cost, etc. It provides an effective way for fruit quality rapid analysis.

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“…An E-nose is an instrument that comprises an array of electronic chemical sensors with partial specificity and an appropriate pattern recognition system, which is capable of recognizing simple or complex odors (Gardner & Bartlett, 1994). It can mimic the human perception of odor and enable the gathering of comprehensive information on the sensory data and analytical properties, which can be related via appropriate chemometric methods such as pattern recognition techniques and multivariate calibration methods (Guan, Zhao, Lin and Zou, 2014;Dong, Zhao, Hu, Dong, & Tan, 2017;Dai, Huang, Lv, Zhang, Sun, and Aheto, 2018). Fish freshness was detected nondestructively by combining E-nose and electronic tongue (E-tongue) in conjunction with chemometric methods with satisfactory results (Han, Huang, Teye, Gu, & Gu, 2013).…”

Section: Introductionmentioning

confidence: 99%

Rapid and nondestructive detection of freshness quality of postharvest spinaches based on machine vision and electronic nose

Huang

et al. 2019

Journal of Food Safety

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Artificial sensory evaluation and physicochemical analysis tend to be tedious and time consuming. Therefore, this study employs machine vision, electronic nose (E‐nose), and multisensory data fusion to rapidly and nondestructively detect spinach freshness during storage. Spinach freshness during refrigeration was classified into four grades by 10 trained panelists. Machine vision and E‐nose were then used to obtain the image and odor information of the samples, respectively. K‐nearest neighbor (KNN), support vector machine (SVM), and back‐propagation artificial neural network (BPNN) were applied to predict spinach freshness. Result from the BPNN model based on machine vision achieved the same result as the KNN model with a classification accuracy of 85.42%. The BPNN model based on E‐nose achieved a better result than the SVM model with classification accuracies of 81.25% and 75.00%, respectively. Furthermore, the BPNN model based on multisensory data fusion greatly improved the detection accuracy of spinach freshness with a classification accuracy of 93.75%. Multisensory data fusion approaches based on machine vision and E‐nose are capable of intelligently detecting the freshness of postharvest spinach during storage. The main advantage is to allow a rapid and nondestructive detection of spinach freshness without using any chemical pretreatments. Practical Applications Fresh spinach is an important source of minerals, vitamins, carotenes, and other nutrients. Timely and accurately knowing the grade of spinach freshness before decay is of great interest in ensuring edible quality and reducing economic losses. Traditional analytical methods of spinach freshness tend to be tedious and time consuming. Novel analytical techniques such as machine vision and electronic nose (E‐nose) showed good feasibility in detecting spinach freshness during refrigerated storage. The detection accuracy could be highly improved based on the multisensory data fusion of machine vision and E‐nose. The results of this study confirmed that machine vision and E‐nose could constitute a rapid and nondestructive method to detect spinach freshness during storage instead of tedious and time‐consuming methods. The method of combining machine vision and E‐nose could be applied in the fruit and vegetable industry in the future.

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Characterization of Volatile Organic Compounds of Vinegars with Novel Electronic Nose System Combined with Multivariate Analysis

Cited by 35 publications

References 35 publications

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

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

Kiwi fruit (Actinidia chinensis) quality determination based on surface acoustic wave resonator combined with electronic nose

Rapid and nondestructive detection of freshness quality of postharvest spinaches based on machine vision and electronic nose

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