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Diagnosing Fungal Infection in Wheat Kernels by Integrating Spectroscopic Technology and Digital Color Imaging System: Artificial Neural Network, Principal Component Analysis and Correlation Feature Selection Techniques

2024Journal article Open access
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dc.contributor.authorZohrabi, Saman
dc.contributor.authorSeiiedlou, Seyed Sadegh
dc.contributor.authorGolpour, Iman
dc.contributor.authorLefsrud, Mark
dc.contributor.authorGuiné, Raquel
dc.contributor.authorSturm, Barbara
dc.date.accessioned2024-11-18T10:48:43Z
dc.date.available2024-11-18T10:48:43Z
dc.date.issued2024
dc.description.abstractContamination of cereal grain, especially wheat, with fungal infections can cause significant economic impacts and it endangers the health of humans and livestock. This study aims to appraise the UV/VIS–NIR and digital color (RGB) imaging systems and spectroscopic methodology to detect wheat kernels infected by fungi such as Penicillium expansum and Fusarium graminearum. NIR spectra of 190–1100 nm at 10 nm intervals, visible color reflectance images and non-visible reflectance images of wheat kernels in the ultraviolet and near-infrared ranges were applied to develop the multi-layer perceptron (MLP) artificial neural network model. The optimum wavelengths were selected by application of the principal component analysis (PCA) after preprocessing the raw spectra. A confusion matrix was used in the correlation feature selection method (CFS) for the decision tree classifier of selected features. The results showed that the four UV wavelengths of 310, 330, 400, and 410 nm were the best wavelengths using PCA to distinguish healthy and unhealthy wheat kernels. Considering the intensity of the wavelengths as the neural network inputs, samples were classified into healthy and unhealthy categories with an accuracy of 90.9%. Also, 18 features of color images in RGB, LAB, HSV, HSI, YCbCr, and YIQ spaces provided the highest average accuracy of 44.4% in classifying healthy and infected wheat kernels by using a CCD Proline camera in the ultraviolet range. In contrast, other cameras in the visible and invisible range showed low accuracy. Furthermore, the best classification accuracy of the healthy and infected samples by the use of the CFS method was obtained at 88.1%. Based on the findings, spectroscopic methodology proved to be highly effective for detecting, classifying and automatic cleaning of various agricultural seeds, with a particular emphasis on wheat kernals.pt_PT
dc.description.versioninfo:eu-repo/semantics/publishedVersionpt_PT
dc.identifier.citationZohrabi S, Seiiedlou SS, Golpour I, Lefsrud M, Guiné RPF, Sturm B. (2024) Diagnosing Fungal Infection in Wheat Kernels by Integrating Spectroscopic Technology and Digital Color Imaging System: Artificial Neural Network, Principal Component Analysis and Correlation Feature Selection Techniques. Journal of Food Process Engineering, 47(11): e14767 (14 pp.).pt_PT
dc.identifier.doi10.1111/jfpe.14767pt_PT
dc.identifier.urihttp://hdl.handle.net/10400.19/8645
dc.language.isoengpt_PT
dc.peerreviewedyespt_PT
dc.subjectartificial neural network (ANN)pt_PT
dc.subjectwheatpt_PT
dc.subjectprincipal component analysis (PCA)pt_PT
dc.subjectspectroscopy techniquept_PT
dc.subjectcorrelation feature selection method (CFS)pt_PT
dc.subjectfungi infectionpt_PT
dc.titleDiagnosing Fungal Infection in Wheat Kernels by Integrating Spectroscopic Technology and Digital Color Imaging System: Artificial Neural Network, Principal Component Analysis and Correlation Feature Selection Techniquespt_PT
dc.typejournal article
dspace.entity.typePublication
oaire.citation.issue11pt_PT
oaire.citation.startPagee14767pt_PT
oaire.citation.titleJournal of Food Process Engineeringpt_PT
oaire.citation.volume47pt_PT
person.familyNamede Pinho Ferreira Guiné
person.givenNameRaquel
person.identifierhttps://scholar.google.pt/citations?user=abFDovIAAAAJ&hl=pt-PT
person.identifier.ciencia-id8B13-5492-0F23
person.identifier.orcid0000-0003-0595-6805
person.identifier.scopus-author-id6603138390
rcaap.rightsopenAccesspt_PT
rcaap.typearticlept_PT
relation.isAuthorOfPublication59580952-77cc-4e4e-ae90-527a8b994f9f
relation.isAuthorOfPublication.latestForDiscovery59580952-77cc-4e4e-ae90-527a8b994f9f

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