Microbial volatile organic compounds and their application in microorganism identification in foodstuff

Wang, Yu; Li, Yongxin; Yang, Jinling; Ruan, Jia; Sun, Chengjun

doi:10.1016/j.trac.2015.08.010

Cited by 95 publications

(55 citation statements)

References 103 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…VOCs are regarded as indicators of microbial growth during metabolism (primary and secondary metabolism) as byproducts. The production of these volatile compounds is largely affected by the type of microbial species and strains involved (Wang et al, ). The sensors of W5S, W6S, W1S, WIW, W2S, and W2W are sensitive to the nitrogen oxides, hydrogen, methane, sulfur compounds, alcohols, partially aromatic compounds, aromatics compounds, and sulfur organic compounds as shown by their related peaks during Salmonella and listeria species sampling.…”

Section: Resultsmentioning

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

View full text Add to dashboard Cite

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.

show abstract

Section: Resultsmentioning

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

View full text Add to dashboard Cite

show abstract

“…Thus, direct analysis in real time has been recently used to classify tree species . Extractive electrospray ionization and secondary electrospray ionization have demonstrated some potentialities . Ambient mass spectrometry techniques and their applications were reviewed .…”

Section: Introductionmentioning

confidence: 99%

“…Thus, monitoring of processes or storage conditions during shelf life of various foodstuffs was undertaken with SIFT‐MS instruments: cocoa roasting, beef under modified atmosphere packaging, chemical spoilage in retail meat, storage and transformation of various fruits and vegetables, food fermentations, and storage of cabbage . Iberian hams from pigs reared under different conditions could be distinguished based on SIFT‐MS fingerprints, and microbial VOCs were used to identify microorganisms in various foodstuffs spoilage …”

Section: Introductionmentioning

confidence: 99%

Volatile compounds profiling by using proton transfer reaction‐time of flight‐mass spectrometry (PTR‐ToF‐MS). The case study of dark chocolates organoleptic differences

et al. 2019

View full text Add to dashboard Cite

Direct‐injection mass spectrometry (DIMS) techniques have evolved into powerful methods to analyse volatile organic compounds (VOCs) without the need of chromatographic separation. Combined to chemometrics, they have been used in many domains to solve sample categorization issues based on volatilome determination. In this paper, different DIMS methods that have largely outperformed conventional electronic noses (e‐noses) in classification tasks are briefly reviewed, with an emphasis on food‐related applications. A particular attention is paid to proton transfer reaction mass spectrometry (PTR‐MS), and many results obtained using the powerful PTR‐time of flight‐MS (PTR‐ToF‐MS) instrument are reviewed. Data analysis and feature selection issues are also summarized and discussed. As a case study, a challenging problem of classification of dark chocolates that has been previously assessed by sensory evaluation in four distinct categories is presented. The VOC profiles of a set of 206 chocolate samples classified in the four sensory categories were analysed by PTR‐ToF‐MS. A supervised multivariate data analysis based on partial least squares regression‐discriminant analysis allowed the construction of a classification model that showed excellent prediction capability: 97% of a test set of 62 samples were correctly predicted in the sensory categories. Tentative identification of ions aided characterisation of chocolate classes. Variable selection using dedicated methods pinpointed some volatile compounds important for the discrimination of the chocolates. Among them, the CovSel method was used for the first time on PTR‐MS data resulting in a selection of 10 features that allowed a good prediction to be achieved. Finally, challenges and future needs in the field are discussed.

show abstract

“…They have reduced TAUs in the IS samples compared to UC, indicating possible influence of the starter culture. One of the compounds belonging to class 3 was ethylbenzene, which presence in the meat product may be very significant as it may be associated with spoilage of meat and fish [17,26]. The reduced values of TAUs of the compound in the IS samples is therefore desirable towards possible reduction of spoilage in the meat product.…”

Section: Resultsmentioning

confidence: 99%

“…The reduced values of TAUs of the compound in the IS samples is therefore desirable towards possible reduction of spoilage in the meat product. The nitrogenous compounds (class 4) consisted mostly of pyrazines, which are regarded as products of maillard reactions; their formation in meat products could be attributed to application of heat and salting during processing [26]. Contribution of pyrazines to development of desirable sensory characteristics of grilled and roasted meat has been reported [16].…”

Section: Resultsmentioning

confidence: 99%

Spoilage volatiles and sensory properties of a grilled stick meat product inoculated with Pediococcus acidilactici FLE07 as starter culture

Olaoye¹

2016

Ukr. food j.

View full text Add to dashboard Cite

show abstract

Microbial volatile organic compounds and their application in microorganism identification in foodstuff

Cited by 95 publications

References 103 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

Volatile compounds profiling by using proton transfer reaction‐time of flight‐mass spectrometry (PTR‐ToF‐MS). The case study of dark chocolates organoleptic differences

Spoilage volatiles and sensory properties of a grilled stick meat product inoculated with Pediococcus acidilactici FLE07 as starter culture

Contact Info

Product

Resources

About