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dc.contributor.authorPrzybył, Krzysztof
dc.contributor.authorKoszela, Krzysztof
dc.contributor.authorAdamski, Franciszek
dc.contributor.authorSamborska, Katarzyna
dc.contributor.authorWalkowiak, Katarzyna
dc.contributor.authorPolarczyk, Mariusz
dc.date.accessioned2021-09-22T09:05:39Z
dc.date.available2021-09-22T09:05:39Z
dc.date.issued2021-08-30
dc.identifier.citationPrzybył, K.; Koszela, K.; Adamski, F.; Samborska, K.; Walkowiak, K.; Polarczyk, M. Deep and Machine Learning Using SEM, FTIR, and Texture Analysis to Detect Polysaccharide in Raspberry Powders. Sensors 2021, 21, 5823. https://doi.org/10.3390/s21175823en
dc.identifier.issn1424-8220
dc.identifier.otherhttps://doi.org/10.3390/s21175823
dc.identifier.urihttps://depot.ceon.pl/handle/123456789/20405
dc.description.abstractIn the paper, an attempt was made to use methods of artificial neural networks (ANN) and Fourier transform infrared spectroscopy (FTIR) to identify raspberry powders that are different from each other in terms of the amount and the type of polysaccharide. Spectra in the absorbance function (FTIR) were prepared as well as training sets, taking into account the structure of microparticles acquired from microscopic images with Scanning Electron Microscopy (SEM). In addition to the above, Multi-Layer Perceptron Networks (MLPNs) with a set of texture descriptors (machine learning) and Convolution Neural Network (CNN) with bitmap (deep learning) were devised, which is an innovative attitude to solving this issue. The aim of the paper was to create MLPN and CNN neural models, which are characterized by a high efficiency of classification. It translates into recognizing microparticles (obtaining their homogeneity) of raspberry powders on the basis of the texture of the image pixel., (angielski)en
dc.language.isoen
dc.publisherMDPIen
dc.rightsCreative Commons Uznanie autorstwa 4.0*
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/legalcode*
dc.subjectraspberry powdersen
dc.subjectFTIRen
dc.subjectSEMen
dc.subjecttexture analysisen
dc.subjectdehumidified spray-dryingen
dc.titleDeep and Machine Learning Using SEM, FTIR, and Texture Analysis to Detect Polysaccharide in Raspberry Powdersen
dc.typearticleen
dc.contributor.organizationFood Sciences and Nutrition, Department of Food Technology of Plant Origin, Poznań University of Life Sciences, Polanden
dc.contributor.organizationDepartment of Biosystems Engineering, Poznań University of Life Sciences, Polanden
dc.contributor.organizationFood Sciences and Nutrition, Department of Food Technology of Plant Origin, Poznań University of Life Sciences, Polanden
dc.contributor.organizationInstitute of Food Sciences, Warsaw University of Life Sciences WULS-SGGW, Polanden
dc.contributor.organizationFood Sciences and Nutrition, Department of Physics and Biophysics, Poznań University of Life Sciences, Polanden
dc.contributor.organizationMain Library and Scientific Information Centre, Poznań University of Life Sciences, Polanden


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