Application of spectroscopic and multispectral imaging technologies on the assessment of ready-to-eat pineapple quality: A performance evaluation study of machine learning models generated from two commercial data analytics tools

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dc.contributor.authorManthou, Evanthia
dc.contributor.authorLago, Sergio-Llaneza
dc.contributor.authorDagres, Evaggelos
dc.contributor.authorLianou, Alexandra
dc.contributor.authorTsakanikas, Panagiοtis
dc.contributor.authorPanagou, Efstathios Z.
dc.contributor.authorAnastasiadi, Maria
dc.contributor.authorMohareb, Fady
dc.contributor.authorNychas, George-John E.
dc.date.accessioned2020-06-05T14:59:51Z
dc.date.available2020-06-05T14:59:51Z
dc.date.issued2020-06-03
dc.description.abstractRecently, rapid, non-invasive analytical methods relying on vibrational spectroscopy and hyper/multispectral imaging, are increasingly gaining popularity in food science. Although such instruments offer a promising alternative to the conventional methods, the analysis of generated data demands complex multidisciplinary approaches based on data analytics tools utilization. Therefore, the objective of this work was to (i) assess the predictive power of different analytical platforms (sensors) coupled with machine learning algorithms in evaluating quality of ready-to-eat (RTE) pineapple (Ananas comosus) and (ii) explore the potentials of The Unscrambler software and the online machine-learning ranking platform, SorfML, in developing the predictive models required by such instruments to assess quality indices. Pineapple samples were stored at 4, 8, 12 °C and dynamic temperatures and were subjected to microbiological (total mesophilic microbial populations, TVC) and sensory analysis (colour, odour, texture) with parallel acquisition of spectral data. Fourier-transform infrared, fluorescence (FLUO) and visible sensors, as well as Videometer instrument were used. For TVC, almost all the combinations of sensors and Partial-least squares regression (PLSR) algorithm from both analytics tools reached values of root mean square error of prediction (RMSE) up to 0.63 log CFU/g, as well as the highest coefficient of determination values (R2). Moreover, Linear Support Vector Machine (SVM Linear) combined with each one of the sensors reached similar performance. For odour, FLUO sensor achieved the highest overall performance, when combined with Partial-least squares discriminant analysis (PLSDA) in both platforms with accuracy close to 85%, but also with values of sensitivity and specificity above 85%. The SVM Linear and MSI combination also achieved similar performance. On the other hand, all models developed for colour and texture showed poor prediction performance. Overall, the use of both analytics tools, resulted in similar trends concerning the feasibility of the different analytical platforms and algorithms on quality evaluation of RTE pineapple.en_UK
dc.identifier.citationManthou E, Lago S-L, Dagres E, et al., (2020) Application of spectroscopic and multispectral imaging technologies on the assessment of ready-to-eat pineapple quality: A performance evaluation study of machine learning models generated from two commercial data analytics tools. Computers and Electronics in Agriculture, Volume 175, August 2020, Article number 105529en_UK
dc.identifier.issn0168-1699
dc.identifier.urihttps://doi.org/10.1016/j.compag.2020.105529
dc.identifier.urihttp://dspace.lib.cranfield.ac.uk/handle/1826/15479
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.subjectPineappleen_UK
dc.subjectQualityen_UK
dc.subjectVibrational spectroscopyen_UK
dc.subjectMultispectral imagingen_UK
dc.subjectMachine learningen_UK
dc.subjectSorfMLen_UK
dc.titleApplication of spectroscopic and multispectral imaging technologies on the assessment of ready-to-eat pineapple quality: A performance evaluation study of machine learning models generated from two commercial data analytics toolsen_UK
dc.typeArticleen_UK

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