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Open AccessArticle

Neural Image Analysis and Electron Microscopy to Detect and Describe Selected Quality Factors of Fruit and Vegetable Spray-Dried Powders—Case Study: Chokeberry Powder

1
Food Sciences and Nutrition, Institute of Food Technology of Plant Origin, Poznan University of Life Sciences, Wojska Polskiego 31, 60-624 Poznan, Poland
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Institute of Biosystems Engineering, Poznan University of Life Sciences, Wojska Polskiego 50, 60-625 Poznan, Poland
3
Faculty of Machines and Transport, Poznan University of Technology, 60-624 Poznan, Poland
4
Department of Food Engineering and Process Management, Warsaw University of Life Sciences—SGGW, 02-787 Warsaw, Poland
*
Author to whom correspondence should be addressed.
Sensors 2019, 19(20), 4413; https://doi.org/10.3390/s19204413
Received: 15 September 2019 / Revised: 7 October 2019 / Accepted: 8 October 2019 / Published: 12 October 2019
(This article belongs to the Section Intelligent Sensors)
The study concentrates on researching possibilities of using computer image analysis and neural modeling in order to assess selected quality discriminants of spray-dried chokeberry powder. The aim of the paper is the quality identification of chokeberry powders on account of their highest dying power, the highest bioactivity, as well as technologically satisfying looseness of the powder. The article presents neural models with vision techniques backed up by devices such as digital cameras, as well as an electron microscope. The reduction in size of input variables with PCA has an influence on improving the processes of learning data sets, thus increasing the effectiveness of identifying chokeberry fruit powders included in digital pictures, which is shown in the results of the conducted research. The effectiveness of image recognition is presented by classifying abilities, as well as low Root Mean Square Error (RMSE), for which the best results are achieved with a typology of network type Multi-Layer Perceptron (MLP). The selected networks type MLP are characterized by the highest degree of classification at 0.99 and RMSE at 0.11 at most at the same time. View Full-Text
Keywords: Artificial Neural Network (ANN); classification; image analysis; chokeberry powder; colors; spray-drying Artificial Neural Network (ANN); classification; image analysis; chokeberry powder; colors; spray-drying
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MDPI and ACS Style

Przybył, K.; Gawałek, J.; Koszela, K.; Przybył, J.; Rudzińska, M.; Gierz, Ł.; Domian, E. Neural Image Analysis and Electron Microscopy to Detect and Describe Selected Quality Factors of Fruit and Vegetable Spray-Dried Powders—Case Study: Chokeberry Powder. Sensors 2019, 19, 4413.

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    Doi: doi: 10.20944/preprints201909.0163.v1
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