Evaluating the Classification of Freeze-Dried Slices and Cubes of Red-Fleshed Apple Genotypes Using Image Textures, Color Parameters, and Machine Learning
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 paramete...
| Main Authors: | , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2023-02-01
|
| Series: | Agriculture |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2077-0472/13/3/562 |
| _version_ | 1827751920273457152 |
|---|---|
| author | Ewa Ropelewska Dorota E. Kruczyńska Ahmed M. Rady Krzysztof P. Rutkowski Dorota Konopacka Karolina Celejewska Monika Mieszczakowska-Frąc |
| author_facet | Ewa Ropelewska Dorota E. Kruczyńska Ahmed M. Rady Krzysztof P. Rutkowski Dorota Konopacka Karolina Celejewska Monika Mieszczakowska-Frąc |
| author_sort | Ewa Ropelewska |
| collection | DOAJ |
| description | 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 <i>L*</i>, <i>a*</i>, and <i>b*</i> 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 (<i>L*</i>, <i>b*</i>) 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 (<i>L*</i>, <i>a*</i>, <i>b*</i>) 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. |
| first_indexed | 2024-03-11T07:04:57Z |
| format | Article |
| id | doaj.art-c981569ff6644f029aff58cd8b20e52c |
| institution | Directory Open Access Journal |
| issn | 2077-0472 |
| language | English |
| last_indexed | 2024-03-11T07:04:57Z |
| publishDate | 2023-02-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Agriculture |
| spelling | doaj.art-c981569ff6644f029aff58cd8b20e52c2023-11-17T09:00:12ZengMDPI AGAgriculture2077-04722023-02-0113356210.3390/agriculture13030562Evaluating the Classification of Freeze-Dried Slices and Cubes of Red-Fleshed Apple Genotypes Using Image Textures, Color Parameters, and Machine LearningEwa Ropelewska0Dorota E. Kruczyńska1Ahmed M. Rady2Krzysztof P. Rutkowski3Dorota Konopacka4Karolina Celejewska5Monika Mieszczakowska-Frąc6Fruit and Vegetable Storage and Processing Department, The National Institute of Horticultural Research, Konstytucji 3 Maja 1/3, 96-100 Skierniewice, PolandCultivar Testing, Nursery and Gene Bank Resources Department, The National Institute of Horticultural Research, Konstytucji 3 Maja 1/3, 96-100 Skierniewice, PolandTeagasc Food Research Centre, Scribbletown, D15 DY05 Dublin, IrelandFruit and Vegetable Storage and Processing Department, The National Institute of Horticultural Research, Konstytucji 3 Maja 1/3, 96-100 Skierniewice, PolandFruit and Vegetable Storage and Processing Department, The National Institute of Horticultural Research, Konstytucji 3 Maja 1/3, 96-100 Skierniewice, PolandFruit and Vegetable Storage and Processing Department, The National Institute of Horticultural Research, Konstytucji 3 Maja 1/3, 96-100 Skierniewice, PolandFruit and Vegetable Storage and Processing Department, The National Institute of Horticultural Research, Konstytucji 3 Maja 1/3, 96-100 Skierniewice, PolandDried 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 <i>L*</i>, <i>a*</i>, and <i>b*</i> 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 (<i>L*</i>, <i>b*</i>) 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 (<i>L*</i>, <i>a*</i>, <i>b*</i>) 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.https://www.mdpi.com/2077-0472/13/3/562red-fleshed applesapple slicesapple cubesfreeze-dryingmachine-learning algorithms |
| spellingShingle | Ewa Ropelewska Dorota E. Kruczyńska Ahmed M. Rady Krzysztof P. Rutkowski Dorota Konopacka Karolina Celejewska Monika Mieszczakowska-Frąc Evaluating the Classification of Freeze-Dried Slices and Cubes of Red-Fleshed Apple Genotypes Using Image Textures, Color Parameters, and Machine Learning Agriculture red-fleshed apples apple slices apple cubes freeze-drying machine-learning algorithms |
| title | Evaluating the Classification of Freeze-Dried Slices and Cubes of Red-Fleshed Apple Genotypes Using Image Textures, Color Parameters, and Machine Learning |
| title_full | Evaluating the Classification of Freeze-Dried Slices and Cubes of Red-Fleshed Apple Genotypes Using Image Textures, Color Parameters, and Machine Learning |
| title_fullStr | Evaluating the Classification of Freeze-Dried Slices and Cubes of Red-Fleshed Apple Genotypes Using Image Textures, Color Parameters, and Machine Learning |
| title_full_unstemmed | Evaluating the Classification of Freeze-Dried Slices and Cubes of Red-Fleshed Apple Genotypes Using Image Textures, Color Parameters, and Machine Learning |
| title_short | Evaluating the Classification of Freeze-Dried Slices and Cubes of Red-Fleshed Apple Genotypes Using Image Textures, Color Parameters, and Machine Learning |
| title_sort | evaluating the classification of freeze dried slices and cubes of red fleshed apple genotypes using image textures color parameters and machine learning |
| topic | red-fleshed apples apple slices apple cubes freeze-drying machine-learning algorithms |
| url | https://www.mdpi.com/2077-0472/13/3/562 |
| work_keys_str_mv | AT ewaropelewska evaluatingtheclassificationoffreezedriedslicesandcubesofredfleshedapplegenotypesusingimagetexturescolorparametersandmachinelearning AT dorotaekruczynska evaluatingtheclassificationoffreezedriedslicesandcubesofredfleshedapplegenotypesusingimagetexturescolorparametersandmachinelearning AT ahmedmrady evaluatingtheclassificationoffreezedriedslicesandcubesofredfleshedapplegenotypesusingimagetexturescolorparametersandmachinelearning AT krzysztofprutkowski evaluatingtheclassificationoffreezedriedslicesandcubesofredfleshedapplegenotypesusingimagetexturescolorparametersandmachinelearning AT dorotakonopacka evaluatingtheclassificationoffreezedriedslicesandcubesofredfleshedapplegenotypesusingimagetexturescolorparametersandmachinelearning AT karolinacelejewska evaluatingtheclassificationoffreezedriedslicesandcubesofredfleshedapplegenotypesusingimagetexturescolorparametersandmachinelearning AT monikamieszczakowskafrac evaluatingtheclassificationoffreezedriedslicesandcubesofredfleshedapplegenotypesusingimagetexturescolorparametersandmachinelearning |