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.
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Claims 8
Claim · model-written summary
Texture models classified freeze-dried apple slices with 90.25% overall accuracy.
- Taxon
- apple
Malus domesticae0 - Outcome
- overall accuracye0
- Value
- 90.25%e0
- Qualifier
- slice images in 11 selected color channelse0
Organisms pictured · Representative pictures of the matched species, not figures from the paper.
Verbatim
e0“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”
Claim · model-written summary
Texture models classified freeze-dried apple slices with mean absolute error 0.0545.
- Taxon
- appleMalus domesticae0
- Outcome
- mean absolute errore0
- Value
- 0.0545e0
- Qualifier
- slice images in 11 selected color channelse0
Organisms pictured · Representative pictures of the matched species, not figures from the paper.
Verbatim
e0“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”
Claim · model-written summary
Adding color parameters raised slice classification accuracy to 91.25%.
- Taxon
- AppleMalus domesticat0
- Treatment
- selected color parameters (L*, b*)e0
- Outcome
- overall accuracye0
- Direction
- increasee0
- Value
- 91.25%e0
- Comparator
- modelse0
Organisms pictured · Representative pictures of the matched species, not figures from the paper.
Verbatim
e0“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.”
t0“Apple”
Claim · model-written summary
Adding color parameters reduced slice classification mean absolute error to 0.0486.
- Taxon
- AppleMalus domesticat0
- Treatment
- selected color parameters (L*, b*)e0
- Outcome
- mean absolute errore0
- Direction
- decreasee0
- Value
- 0.0486e0
- Comparator
- modelse0
Organisms pictured · Representative pictures of the matched species, not figures from the paper.
Verbatim
e0“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.”
t0“Apple”
Claim · model-written summary
Texture models classified apple cube images with accuracy up to 74.74%.
- Taxon
- appleMalus domesticae0
- Outcome
- overall accuracye0
- Value
- 74.74%e0
- Qualifier
- cube imagese0
Organisms pictured · Representative pictures of the matched species, not figures from the paper.
Verbatim
e0“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%”
Claim · model-written summary
Adding color parameters increased cube classification accuracy to 80.50%.
- Taxon
- AppleMalus domesticat0
- Treatment
- color parameters (L*, a*, b*)e0
- Outcome
- overall accuracye0
- Direction
- increasee0
- Value
- 80.50%e0
- Comparator
- modelse0
Organisms pictured · Representative pictures of the matched species, not figures from the paper.
Verbatim
e0“adding color parameters (L*, a*, b*) to models resulted in an increase in the overall accuracy to 80.50%.”
t0“Apple”
Claim · model-written summary
The greatest classification mixing was between Alex Red and Trinity apple samples.
- Taxon
- appleMalus domesticae0
- Material
- ‘Alex Red’e0
- Outcome
- greatest mixing of casese0
- Qualifier
- apple slices and cubese0
- Comparator
- ‘Trinity’e0
Organisms pictured · Representative pictures of the matched species, not figures from the paper.
Verbatim
e0“The greatest mixing of cases was observed between ‘Alex Red’ and ‘Trinity’ as well as ‘314’ and ‘602’ apple slices and cubes.”
Claim · model-written summary
The greatest classification mixing also occurred between 314 and 602 apple samples.
- Taxon
- appleMalus domesticae0
- Material
- ‘314’e0
- Outcome
- greatest mixing of casese0
- Qualifier
- apple slices and cubese0
- Comparator
- ‘602’e0
Organisms pictured · Representative pictures of the matched species, not figures from the paper.
Verbatim
e0“The greatest mixing of cases was observed between ‘Alex Red’ and ‘Trinity’ as well as ‘314’ and ‘602’ apple slices and cubes.”