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© 2023 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.

Abstract

Dried red-fleshed apples are considered a promising high-quality product from the functional foods category. The objective of this study was to compare the flesh features of freeze-dried red-fleshed apples belonging to the ‘Alex Red’, ‘Trinity’, ‘314’, and ‘602’ genotypes and indicate which parameters and shapes of dried samples are the most useful to distinguish apple genotypes. Apple samples were at the stage of harvest maturity. The average fruit weight, starch index, internal ethylene concentration, flesh firmness, total soluble sugar content, and titratable acidity were determined. One hundred apple slices with a thickness of 4 mm and one hundred cubes with dimensions of 1.5 cm × 1.5 cm × 1.5 cm of each genotype were subjected to freeze-drying. For each apple sample (slice or cube), 2172 image texture parameters were extracted from images in 12 color channels, and color parameters L*, a*, and b* were determined. The classification models were developed based on a set of selected image textures and a set of combined selected image textures and color parameters of freeze-dried apple slices and cubes using various traditional machine-learning algorithms. Models built based on selected textures of slice images in 11 selected color channels correctly classified freeze-dried red-fleshed apple genotypes with an overall accuracy reaching 90.25% and mean absolute error of 0.0545; by adding selected color parameters (L*, b*) to models, an increase in the overall accuracy to 91.25% and a decrease in the mean absolute error to 0.0486 were observed. The classification of apple cube images using models including selected texture parameters from images in 11 selected color channels was characterized by an overall accuracy of up to 74.74%; adding color parameters (L*, a*, b*) to models resulted in an increase in the overall accuracy to 80.50%. The greatest mixing of cases was observed between ‘Alex Red’ and ‘Trinity’ as well as ‘314’ and ‘602’ apple slices and cubes. The developed models can be used in practice to distinguish freeze-dried red-fleshed apples in a non-destructive and objective manner. It can avoid mixing samples belonging to different genotypes with different chemical properties. Further studies can focus on using deep learning in addition to traditional machine learning to build models to distinguish dried red-fleshed apple samples. Moreover, other drying techniques can be applied, and image texture parameters and color features can be used to predict the changes in flesh structure and estimate the chemical properties of dried samples.

Details

Title
Evaluating the Classification of Freeze-Dried Slices and Cubes of Red-Fleshed Apple Genotypes Using Image Textures, Color Parameters, and Machine Learning
Author
Ropelewska, Ewa 1   VIAFID ORCID Logo  ; Kruczyńska, Dorota E 2   VIAFID ORCID Logo  ; Rady, Ahmed M 3   VIAFID ORCID Logo  ; Rutkowski, Krzysztof P 1   VIAFID ORCID Logo  ; Konopacka, Dorota 1   VIAFID ORCID Logo  ; Celejewska, Karolina 1   VIAFID ORCID Logo  ; Mieszczakowska-Frąc, Monika 1   VIAFID ORCID Logo 

 Fruit and Vegetable Storage and Processing Department, The National Institute of Horticultural Research, Konstytucji 3 Maja 1/3, 96-100 Skierniewice, Poland 
 Cultivar Testing, Nursery and Gene Bank Resources Department, The National Institute of Horticultural Research, Konstytucji 3 Maja 1/3, 96-100 Skierniewice, Poland 
 Teagasc Food Research Centre, Scribbletown, D15 DY05 Dublin, Ireland; Food, Water, Waste Research Group, Faculty of Engineering, University of Nottingham, University Park Campus, Nottingham NG7 2RD, UK 
First page
562
Publication year
2023
Publication date
2023
Publisher
MDPI AG
e-ISSN
20770472
Source type
Scholarly Journal
Language of publication
English
ProQuest document ID
2791552299
Copyright
© 2023 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.