Prediction of Listeria monocytogenes ATCC 7644 growth on fresh-cut produce treated with bacteriophage and sucrose monolaurate by using artificial neural network

Oladunjoye, Adebola O.; Oyewole, S.A.; Singh, Surendra; Ijabadeniyi, Oluwatosin Ademola

doi:10.1016/j.lwt.2016.10.042

Cited by 32 publications

(20 citation statements)

References 46 publications

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“…Oladunjoye et al [53] used ANN for the prediction of bacterial growth on fresh-cut foods (tomatoes and carrots) treated with bactericide agents (bacteriophage and sucrose monolaurate). The prediction with logistic activation function showed the highest positive correlation between predicted and observed values with R 2 0.9, hence offering an adequate prediction capacity for phage biocontrol of pathogens in fresh foods.…”

Section: E Classification and Quality Controlmentioning

confidence: 99%

The Use of Artificial Neural Networks (ANN) in Food Process Engineering

Guiné¹,

Dets²

2019

ijfe

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 -Artificial neural networks (ANN) aim to solve problems of artificial intelligence, by building a system with links that simulate the human brain. This approach includes the learning process by trial and error. The ANN is a system of neurons connected by synaptic connections and divided into incoming neurons, which receive stimulus from the external environment, internal or hidden neurons and output neurons, that communicate with the outside of the system. The ANNs present many advantages, such as good adaptability characteristics, possibility of generalization and high noise tolerance, among others. Neural networks have been successfully used in various areas, for example, business, finance, medicine, and industry, mainly in problems of classification, prediction, pattern recognition and control. In the food industry, food processing, food engineering, food properties or quality control, statistical tools are frequently present, and ANNs can process more efficiently data comprising multiple input and output variables. The objective of this review was to highlight the application of ANN to food processing, and evaluate its range of use and adaptability to different food systems. For that a systematic review was undertaken from the scientific literature and the selection of the information was based on inclusion criteria defined. The results indicated that ANN is widely used for modelling and prediction in food systems, showing good accuracy and applicability to a wide range of situations and processes in food engineering.

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Section: E Classification and Quality Controlmentioning

confidence: 99%

The Use of Artificial Neural Networks (ANN) in Food Process Engineering

Guiné¹,

Dets²

2019

ijfe

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“…Perhaps an effective solution would be the use of bacteriophages in combination with the spraying of organic acids that are used in food technology. In the research of Oladunjoye et al (2017), attention was paid to the improved efficacy of Listex TM P100 when used in combination with sucrose monolaurate, i.e. a compound with antimicrobial activity.…”

Section: Commercial Bacteriophage Preparationsmentioning

confidence: 99%

The concept of using bacteriophages to improve the microbiological quality of minimally processed foods

Wójcicki

Błażejak

Gientka

et al. 2019

Acta Sci Pol Technol Aliment

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Phages were discovered relatively recently -at the turn of the 19th and 20th centuries. The idea of using bacteriophages for therapeutic purposes was promoted by d'Herelle, who conducted the first successful experiments with prokaryotic viruses. Works of contemporary scientists on phage therapy were, however, halted due to the discovery of penicillin by Alexander Fleming in 1928. Today, when many bacterial strains have developed resistance to common antibiotics offered by the pharmaceutical industry and when new, so far unknown, bacterial strains have appeared, the concept of using bacteriophages to treat bacterial infections has been revived. Considering the food sector, the search for novel solutions that would ensure the appropriate microbiological quality of minimally processed foods may bring an effective method for eradicating bacterial pathogens that induce food-borne infections. The employment of chemical and physical methods of food preservation often lead to the deterioration of its nutritive value and of its physical and organoleptic properties. Minimally processed foods manufactured without any drastic preservation methods can be especially at risk of developing microorganisms, including the pathogenic ones. Low-temperature production processes and cold-storage facilitate the development of psychrophilic microorganisms, while another threat is posed by the high microbiological contamination of raw materials. This work presents a biological method for the eradication of bacteria most commonly found in a food-based environment. The study concept postulated the use of bacteriophages to improve the microbiological quality of food, with special attention paid to minimally processed foods.

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“…A rede neural artificial foi modelada utilizando-se os algoritmos de aprendizado de "Filtro de Kalman" e "Propagação reversa", obtendo-se excelentes resultados, como R² de até 0,96. Oladunjoye et al (2017) também testaram redes neurais, porém o seu trabalho tratava da modelagem do crescimento de Listeria monocytogenes em cenoura e tomate combinado com monolaurato de sucrose e bacteriófagos. As redes neurais permitem a interação de diversas variáveis e fatores, e respostas não lineares.…”

Section: Redes Neurais Aplicadas a Microbiologia Preditivaunclassified

Microbiologia Preditiva: Aspectos Gerais E Tendências

Schlei¹,

Reiter²,

Bertoli³

et al. 2018

REPECT

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Os primeiros trabalhos que procuraram predizer o crescimento de microrganismos em função do tempo foram realizados por pesquisadores como Bigelow, Esty e Meyer na década de 1920. Nos últimos anos, órgãos de saúde pública de diversos países incentivaram estudos referentes à cinética de microrganismos em alimentos a fim de entender processos de crescimento de deteriorantes e patógenos. A partir destes trabalhos obtiveram-se modelos primários determinísticos e não determinísticos do crescimento de microrganismos. Novas abordagens permitem modelar o comportamento de microrganismos em função de variáveis do meio. Nas últimas décadas a criação de modelos estocásticos permitiu o desenvolvimento de novos modelos determinísticos, porém agora baseados em redes de fluxos metabólicos que permitiram o aumento da efetividade da predição do crescimento em função do tempo e de diversas variáveis e substratos. A modelagem em redes neurais artificiais pode ser aplicada em conjunto com outros modelos matemáticos e esta nova abordagem permite incluir qualquer variável na modelagem a partir de treinamento da rede neural. O desenvolvimento de softwares que utilizam modelos matemáticos de crescimento é de grande importância para a indústria de alimentos, pois permite maior compreensão dos processos microbiológicos, estimativa de vida de prateleira e as inovações na área permitem simulação da influência de diversos fatores.

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Prediction of Listeria monocytogenes ATCC 7644 growth on fresh-cut produce treated with bacteriophage and sucrose monolaurate by using artificial neural network

Cited by 32 publications

References 46 publications

The Use of Artificial Neural Networks (ANN) in Food Process Engineering

The Use of Artificial Neural Networks (ANN) in Food Process Engineering

The concept of using bacteriophages to improve the microbiological quality of minimally processed foods

Microbiologia Preditiva: Aspectos Gerais E Tendências

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