dc.creator | Vega Márquez, Belén | es |
dc.creator | Nepomuceno Chamorro, Isabel de los Ángeles | es |
dc.creator | Jurado Campos, Natividad | es |
dc.creator | Rubio Escudero, Cristina | es |
dc.date.accessioned | 2020-03-23T10:32:46Z | |
dc.date.available | 2020-03-23T10:32:46Z | |
dc.date.issued | 2020 | |
dc.identifier.citation | Vega Márquez, B., Nepomuceno Chamorro, I.d.l.Á., Jurado Campos, N. y Rubio Escudero, C. (2020). Deep Learning Techniques to Improve the Performance of Olive Oil Classification. Frontiers in Chemistry, january 2020 | |
dc.identifier.issn | 2296-2646 | es |
dc.identifier.uri | https://hdl.handle.net/11441/94425 | |
dc.description.abstract | The olive oil assessment involves the use of a standardized sensory analysis according
to the “panel test” method. However, there is an important interest to design novel
strategies based on the use of Gas Chromatography (GC) coupled to mass spectrometry
(MS), or ion mobility spectrometry (IMS) together with a chemometric data treatment
for olive oil classification. It is an essential task in an attempt to get the most robust
model over time and, both to avoid fraud in the price and to know whether it is suitable
for consumption or not. The aim of this paper is to combine chemical techniques and
Deep Learning approaches to automatically classify olive oil samples from two different
harvests in their three corresponding classes: extra virgin olive oil (EVOO), virgin olive oil
(VOO), and lampante olive oil (LOO). Our Deep Learning model is built with 701 samples,
which were obtained from two olive oil campaigns (2014–2015 and 2015–2016). The
data from the two harvests are built from the selection of specific olive oil markers from
the whole spectral fingerprint obtained with GC-IMS method. In order to obtain the
best results we have configured the parameters of our model according to the nature
of the data. The results obtained show that a deep learning approach applied to data
obtained from chemical instrumental techniques is a good method when classifying oil
samples in their corresponding categories, with higher success rates than those obtained
in previous works. | es |
dc.description.sponsorship | Ministerio de Economía y Competitividad TIN2017-88209-C2-2-R | es |
dc.format | application/pdf | es |
dc.format.extent | 10 | es |
dc.language.iso | eng | es |
dc.publisher | Frontiers Editorial | es |
dc.relation.ispartof | Frontiers in Chemistry, january 2020 | |
dc.rights | Attribution-NonCommercial-NoDerivatives 4.0 Internacional | * |
dc.rights.uri | http://creativecommons.org/licenses/by-nc-nd/4.0/ | * |
dc.subject | Olive oil classification | es |
dc.subject | Chemometric approaches | es |
dc.subject | GC-IMS method | es |
dc.subject | Machine learning | es |
dc.subject | Deep learning | es |
dc.subject | Feed-forward neural network | es |
dc.title | Deep Learning Techniques to Improve the Performance of Olive Oil Classification | es |
dc.type | info:eu-repo/semantics/article | es |
dcterms.identifier | https://ror.org/03yxnpp24 | |
dc.type.version | info:eu-repo/semantics/publishedVersion | es |
dc.rights.accessRights | info:eu-repo/semantics/openAccess | es |
dc.contributor.affiliation | Universidad de Sevilla. Departamento de Lenguajes y Sistemas Informáticos | es |
dc.relation.projectID | TIN2017-88209-C2-2-R. | es |
dc.relation.publisherversion | https://www.frontiersin.org/articles/10.3389/fchem.2019.00929/full | es |
dc.identifier.doi | 10.3389/fchem.2019.00929 | es |
dc.journaltitle | Frontiers in Chemistry | es |
dc.publication.issue | january 2020 | es |
dc.contributor.funder | Ministerio de Economía y Competitividad (MINECO). España | es |