dc.contributor.author | Przybył, Krzysztof | |
dc.contributor.author | Koszela, Krzysztof | |
dc.contributor.author | Adamski, Franciszek | |
dc.contributor.author | Samborska, Katarzyna | |
dc.contributor.author | Walkowiak, Katarzyna | |
dc.contributor.author | Polarczyk, Mariusz | |
dc.date.accessioned | 2021-09-22T09:05:39Z | |
dc.date.available | 2021-09-22T09:05:39Z | |
dc.date.issued | 2021-08-30 | |
dc.identifier.citation | Przybył, 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/s21175823 | en |
dc.identifier.issn | 1424-8220 | |
dc.identifier.other | https://doi.org/10.3390/s21175823 | |
dc.identifier.uri | https://depot.ceon.pl/handle/123456789/20405 | |
dc.description.abstract | In 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.iso | en | |
dc.publisher | MDPI | en |
dc.rights | Creative Commons Uznanie autorstwa 4.0 | * |
dc.rights.uri | http://creativecommons.org/licenses/by/4.0/legalcode | * |
dc.subject | raspberry powders | en |
dc.subject | FTIR | en |
dc.subject | SEM | en |
dc.subject | texture analysis | en |
dc.subject | dehumidified spray-drying | en |
dc.title | Deep and Machine Learning Using SEM, FTIR, and Texture Analysis to Detect Polysaccharide in Raspberry Powders | en |
dc.type | article | en |
dc.contributor.organization | Food Sciences and Nutrition, Department of Food Technology of Plant Origin, Poznań University of Life Sciences, Poland | en |
dc.contributor.organization | Department of Biosystems Engineering, Poznań University of Life Sciences, Poland | en |
dc.contributor.organization | Food Sciences and Nutrition, Department of Food Technology of Plant Origin, Poznań University of Life Sciences, Poland | en |
dc.contributor.organization | Institute of Food Sciences, Warsaw University of Life Sciences WULS-SGGW, Poland | en |
dc.contributor.organization | Food Sciences and Nutrition, Department of Physics and Biophysics, Poznań University of Life Sciences, Poland | en |
dc.contributor.organization | Main Library and Scientific Information Centre, Poznań University of Life Sciences, Poland | en |