An automated ranking platform for machine learning regression models for meat spoilage prediction using multi-spectral imaging and metabolic profiling

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dc.contributor.authorEstelles-Lopez, Lucia
dc.contributor.authorRopodi, Athina
dc.contributor.authorPavlidis, Dimitris
dc.contributor.authorFotopoulou, Jenny
dc.contributor.authorGkousari, Christina
dc.contributor.authorPeyrodie, Audrey
dc.contributor.authorPanagou, Efstathios
dc.contributor.authorNychas, George-John
dc.contributor.authorMohareb, Fady R.
dc.date.accessioned2017-05-25T13:51:40Z
dc.date.available2017-05-25T13:51:40Z
dc.date.issued2017-05-20
dc.description.abstractOver the past decade, analytical approaches based on vibrational spectroscopy, hyperspectral/multispectral imagining and biomimetic sensors started gaining popularity as rapid and efficient methods for assessing food quality, safety and authentication; as a sensible alternative to the expensive and time-consuming conventional microbiological techniques. Due to the multi-dimensional nature of the data generated from such analyses, the output needs to be coupled with a suitable statistical approach or machine-learning algorithms before the results can be interpreted. Choosing the optimum pattern recognition or machine learning approach for a given analytical platform is often challenging and involves a comparative analysis between various algorithms in order to achieve the best possible prediction accuracy. In this work, “MeatReg”, a web-based application is presented, able to automate the procedure of identifying the best machine learning method for comparing data from several analytical techniques, to predict the counts of microorganisms responsible of meat spoilage regardless of the packaging system applied. In particularly up to 7 regression methods were applied and these are ordinary least squares regression, stepwise linear regression, partial least square regression, principal component regression, support vector regression, random forest and k-nearest neighbours. MeatReg” was tested with minced beef samples stored under aerobic and modified atmosphere packaging and analysed with electronic nose, HPLC, FT-IR, GC–MS and Multispectral imaging instrument. Population of total viable count, lactic acid bacteria, pseudomonads, Enterobacteriaceae and B. thermosphacta, were predicted. As a result, recommendations of which analytical platforms are suitable to predict each type of bacteria and which machine learning methods to use in each case were obtained. The developed system is accessible via the link: http://elvis.misc.cranfield.ac.uk/SORF/.en_UK
dc.identifier.citationLucia Estelles-Lopez, Athina Ropodi, Dimitris Pavlidis, Jenny Fotopoulou, Christina Gkousari, Audrey Peyrodie, Efstathios Panagou, George-John Nychas, Fady Mohareb, An automated ranking platform for machine learning regression models for meat spoilage prediction using multi-spectral imaging and metabolic profiling, Food Research International, Vol. 99, Part 1, September 2017, pp. 206-215en_UK
dc.identifier.issn0963-9969
dc.identifier.urihttp://dx.doi.org/10.1016/j.foodres.2017.05.013
dc.identifier.urihttp://dspace.lib.cranfield.ac.uk/handle/1826/11933
dc.language.isoenen_UK
dc.publisherElsevieren_UK
dc.rightsAttribution-NonCommercial-NoDerivatives 4.0 International
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/
dc.subjectMachine learningen_UK
dc.subjectPattern recognitionen_UK
dc.subjectMeat spoilageen_UK
dc.subjectMetabolic profilingen_UK
dc.subjectData scienceen_UK
dc.subjectFood qualityen_UK
dc.titleAn automated ranking platform for machine learning regression models for meat spoilage prediction using multi-spectral imaging and metabolic profilingen_UK
dc.typeArticleen_UK

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