Preprint Article Version 1 Preserved in Portico This version is not peer-reviewed

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

Krzysztof Przybył
,
Jolanta Gawałek
,
Krzysztof Koszela
* ORCID logo ,
Jacek Przybył
,
Magdalena Rudzinska
,
Łukasza Gierz
ORCID logo ,
Ewa Domian
Version 1 : Received: 15 September 2019 / Approved: 16 September 2019 / Online: 16 September 2019 (11:04:14 CEST)

A peer-reviewed article of this Preprint also exists.

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. 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.

Abstract

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 quality identification of chokeberry powders on account of their highest dying power, the highest bioactivity as well as technologically satisfying looseness of powder. The article presents neural models with vision technique backed up by devices such as digital camera as well as electron microscope. Reduction in size of input variables with PCA has influence on improving the processes of learning data sets, thus increasing 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 are presented by classifying abilities as well as low Root Mean Square Error (RMSE), for which the best results are achieved with 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.

Keywords

Artificial Neural Network (ANN); classification; image analysis; chokeberry powder; colors; spray-drying

Subject

Engineering, Control and Systems Engineering

Copyright: This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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