An intelligent based prediction of microbial behaviour in beef
| dc.authorid | yücel, özgün/0000-0001-8916-2628 | |
| dc.authorid | yücel, özgün/0000-0001-8916-2628 | |
| dc.authorwosid | yücel, özgün/AAE-3071-2020 | |
| dc.authorwosid | yücel, özgün/JAN-6493-2023 | |
| dc.contributor.author | Yucel, Ozgun | |
| dc.contributor.author | Tarlak, Fatih | |
| dc.date.accessioned | 2024-06-13T20:18:01Z | |
| dc.date.available | 2024-06-13T20:18:01Z | |
| dc.date.issued | 2023 | |
| dc.department | İstanbul Gedik Üniversitesi | |
| dc.description.abstract | The main purpose of this work was to develop different machine learning-based regression methods referred to as decision tree regression (DTR), generalized additive model regression (GAMR) and random forest regression (RFR) to predict bacterial population on beef. For this purpose, 2654 bacterial data points of Listeria monocytogenes, Escherichia coli and Pseudomonas spp. Which are the most investigated bacterial genera in beef were collected from the ComBase database (www.combase.cc). Temperature, salt concentration, water activity and acidity were used as the main predictor variables to estimate the growth or survival behaviour of the microorganisms in beef. The hyperparameters are optimized for proposed machine learning-based regression methods with nested cross-validation. The fitting capabilities of the proposed machine learning algorithms were compared considering their statistical indices (coefficient of determination R2 and root mean square error RMSE). Each regression method provided satisfactory predictions with being 0.931 < R2 < 0.949 and 0.597 < RMSE < 0.692 considering each of the microorganism populations. However, the RFR yielded the best prediction capability and therefore its prediction capability was further assessed. The RFR in the external validation process provided statistical indices being 1.017 < Bias factor <1.151 and 1.137 < Accuracy factor <1.370, indicating that the random forest regression can be reliably employed as an alternative way of describing simultaneously survival and growth behaviour of microorganisms in beef and has a significant potential to be used as an alternative simulation method by skipping a secondary model step in two-step modelling approach, traditionally utilized in the predictive microbiology field. | |
| dc.identifier.doi | 10.1016/j.foodcont.2023.109665 | |
| dc.identifier.issn | 0956-7135 | |
| dc.identifier.issn | 1873-7129 | |
| dc.identifier.scopus | 2-s2.0-85146719931 | |
| dc.identifier.scopusquality | Q1 | |
| dc.identifier.uri | https://doi.org/10.1016/j.foodcont.2023.109665 | |
| dc.identifier.uri | https://hdl.handle.net/11501/1157 | |
| dc.identifier.volume | 148 | |
| dc.identifier.wos | WOS:000968025800001 | |
| dc.identifier.wosquality | N/A | |
| dc.indekslendigikaynak | Web of Science | |
| dc.indekslendigikaynak | Scopus | |
| dc.language.iso | en | |
| dc.publisher | Elsevier Sci Ltd | |
| dc.relation.ispartof | Food Control | |
| dc.relation.publicationcategory | Makale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı | |
| dc.rights | info:eu-repo/semantics/closedAccess | |
| dc.subject | Data Mining | |
| dc.subject | Decision Tree Regression | |
| dc.subject | Generalized Additive Model Regression | |
| dc.subject | Random Forest Regression | |
| dc.subject | Predictive Microbiology | |
| dc.subject | Random Forest | |
| dc.subject | Growth | |
| dc.title | An intelligent based prediction of microbial behaviour in beef | |
| dc.type | Article |
