Prediction of Aspergillus parasiticus inhibition and aflatoxin mitigation in red pepper flakes treated by pulsed electric field treatment using machine learning and neural networks

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dc.contributor.authorAkdemir Evrendilek, Gulsun
dc.contributor.authorBulut, Nurullah
dc.contributor.authorAtmaca, Bahar
dc.contributor.authorUzuner, Sibel
dc.date.accessioned2023-01-11T07:17:09Z
dc.date.available2023-01-11T07:17:09Z
dc.date.issued2022en_US
dc.departmentMAÜ, Rektörlüğe Bağlı Bölümler, Uygulama - Araştırma Merkezleri ve Koordinatörlükleren_US
dc.description.abstractPresence of aflatoxins in agricultural products is a worldwide problem. Because of their high heat stability and resistance to most of the food processing technologies, aflatoxin degradation is still a big challenge. Thus, efficacy of pulsed electric fields (PEF) by energies ranging from 0.97 to 17.28 J was tested to determine changes in quality properties in red pepper flakes, mitigation of aflatoxins, inactivation of aflatoxin producing Aspergillus parasiticus, reduction in aflatoxin mutagenity, and modelling of A. parasiticus inactivation in addition to aflatoxin mitigation. Maximum inactivation rate of 64.37 % with 17.28 J was encountered on the mean initial A. parasiticus count. A 99.88, 99.47, 97.75, and 99.58 % reductions were obtained on the mean initial AfG1, AfG2, AfB1, and AfB2 concentrations. PEF treated samples by 0.97, 1.36, 5.76, and 17.28 J at 1 μg/plate, 0.97, 1.92, 7.78, 10.80 J at 10 μg/plate, and 0.97, 1.92, 2.92, 4.08, 5.76, 4.86, 6.80, 9.60, 10.80, and 10.89 J at 100 μg/plate were not mutagenic. Modelling with gradient boosting regression tree (GBRT), random forest regression (RFR), and artificial neural network (ANN) provided the lowest RMSE and highest R2 value for GBRT model for the predicted inactivation of A. parasiticus, whereas ANN model provided the lowest RMSE and highest R2 for predicted mitigation of AfG1, AfB1, and AfB2. PEF treatment possess a viable alternative for aflatoxin degradation with reduced mutagenity and without adverse effect on quality properties of red pepper flakes.en_US
dc.identifier.citationEvrendilek, G. A., Bulut, N., Atmaca, B., & Uzuner, S. (2022). Prediction of Aspergillus parasiticus inhibition and aflatoxin mitigation in red pepper flakes treated by pulsed electric field treatment using machine learning and neural networks. Food Research International, 162, 111954.en_US
dc.identifier.doi10.1016/j.foodres.2022.111954en_US
dc.identifier.pmid36461206en_US
dc.identifier.scopus2-s2.0-85138450121en_US
dc.identifier.scopusqualityN/Aen_US
dc.identifier.urihttps://doi.org/10.1016/j.foodres.2022.111954
dc.identifier.urihttps://hdl.handle.net/20.500.12514/3305
dc.identifier.volume162en_US
dc.identifier.wosWOS:000870023800005en_US
dc.identifier.wosqualityQ1en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.indekslendigikaynakPubMeden_US
dc.language.isoenen_US
dc.publisherElsevieren_US
dc.relation.ispartofFood Research Internationalen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectPulsed electric fieldsen_US
dc.titlePrediction of Aspergillus parasiticus inhibition and aflatoxin mitigation in red pepper flakes treated by pulsed electric field treatment using machine learning and neural networksen_US
dc.typeArticleen_US

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