Deep and Machine Learning Using SEM, FTIR, and Texture Analysis to Detect Polysaccharide in Raspberry Powders
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) we...
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| Format: | Article |
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MDPI AG
2021-08-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/21/17/5823 |
| _version_ | 1797520858299760640 |
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| author | Krzysztof Przybył Krzysztof Koszela Franciszek Adamski Katarzyna Samborska Katarzyna Walkowiak Mariusz Polarczyk |
| author_facet | Krzysztof Przybył Krzysztof Koszela Franciszek Adamski Katarzyna Samborska Katarzyna Walkowiak Mariusz Polarczyk |
| author_sort | Krzysztof Przybył |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-10T08:04:31Z |
| format | Article |
| id | doaj.art-fc141011e22c4bdeb9e59acefd434c69 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-10T08:04:31Z |
| publishDate | 2021-08-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-fc141011e22c4bdeb9e59acefd434c692023-11-22T11:13:02ZengMDPI AGSensors1424-82202021-08-012117582310.3390/s21175823Deep and Machine Learning Using SEM, FTIR, and Texture Analysis to Detect Polysaccharide in Raspberry PowdersKrzysztof Przybył0Krzysztof Koszela1Franciszek Adamski2Katarzyna Samborska3Katarzyna Walkowiak4Mariusz Polarczyk5Food Sciences and Nutrition, Department of Food Technology of Plant Origin, Poznan University of Life Sciences, Wojska Polskiego 31, 60-624 Poznan, PolandDepartment of Biosystems Engineering, Poznan University of Life Sciences, Wojska Polskiego 50, 60-625 Poznan, PolandFood Sciences and Nutrition, Department of Food Technology of Plant Origin, Poznan University of Life Sciences, Wojska Polskiego 31, 60-624 Poznan, PolandInstitute of Food Sciences, Warsaw University of Life Sciences WULS-SGGW, Nowoursynowska 159c, 02-787 Warsaw, PolandFood Sciences and Nutrition, Department of Physics and Biophysics, Poznan University of Life Sciences, Wojska Polskiego 28, 60-637 Poznan, PolandMain Library and Scientific Information Centre, Poznan University of Life Sciences, Witosa 45, 61-693 Poznan, PolandIn 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.https://www.mdpi.com/1424-8220/21/17/5823raspberry powdersFTIRSEMANNtexture analysisdehumidified spray-drying |
| spellingShingle | Krzysztof Przybył Krzysztof Koszela Franciszek Adamski Katarzyna Samborska Katarzyna Walkowiak Mariusz Polarczyk Deep and Machine Learning Using SEM, FTIR, and Texture Analysis to Detect Polysaccharide in Raspberry Powders Sensors raspberry powders FTIR SEM ANN texture analysis dehumidified spray-drying |
| title | Deep and Machine Learning Using SEM, FTIR, and Texture Analysis to Detect Polysaccharide in Raspberry Powders |
| title_full | Deep and Machine Learning Using SEM, FTIR, and Texture Analysis to Detect Polysaccharide in Raspberry Powders |
| title_fullStr | Deep and Machine Learning Using SEM, FTIR, and Texture Analysis to Detect Polysaccharide in Raspberry Powders |
| title_full_unstemmed | Deep and Machine Learning Using SEM, FTIR, and Texture Analysis to Detect Polysaccharide in Raspberry Powders |
| title_short | Deep and Machine Learning Using SEM, FTIR, and Texture Analysis to Detect Polysaccharide in Raspberry Powders |
| title_sort | deep and machine learning using sem ftir and texture analysis to detect polysaccharide in raspberry powders |
| topic | raspberry powders FTIR SEM ANN texture analysis dehumidified spray-drying |
| url | https://www.mdpi.com/1424-8220/21/17/5823 |
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