Prediction of tannin, protein, and total phenolic content of grain sorghum using image analysis and machine learning

Nazari, Leyla; Khazaei, Azim; Ropelewska, Ewa

doi:10.1002/cche.10542

Cited by 2 publications

(1 citation statement)

References 17 publications

(24 reference statements)

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“…In further studies, models to predict the chemical properties of stored black currants can be developed. The available literature data indicate the possibility of using image processing and machine learning to estimate the chemical properties of plant materials [ 44 , 45 ].…”

Section: Resultsmentioning

confidence: 99%

Application of Imaging and Artificial Intelligence for Quality Monitoring of Stored Black Currant (Ribes nigrum L.)

Ropelewska

2022

Foods

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The objective of this study was to assess the influence of storage under different storage conditions on black currant quality in a non-destructive and inexpensive manner using image processing and artificial intelligence. Black currants were stored at a room temperature of 20 ± 1 °C and a temperature of 3 °C (refrigerator). The images of black currants directly after harvest and fruit stored for one and two weeks were obtained using a digital camera. Then, texture parameters were computed from the images converted to color channels R (red), G (green), B (blue), L (lightness component from black to white), a (green for negative and red for positive values), b (blue for negative and yellow for positive values), X (component with color information), Y (lightness), and Z (component with color information). Models for the classification of black currants were built using various machine learning algorithms based on selected textures for RGB, Lab, and XYZ color spaces. Models built using the IBk, multilayer perceptron, and multiclass classifier for textures from RGB color space, and the IBk algorithm for textures from Lab color space distinguished unstored black currants and samples stored in the room for one and two weeks with an average accuracy of 100%, and the kappa statistic and weighted averages of precision, recall, Matthews correlation coefficient (MCC), receiver operating characteristic (ROC) area, and precision–recall (PRC) area equal to 1.000. This indicated a very distinct change in the external structure of the fruit after the first week and more and more visible changes in quality with increasing storage time. A classification accuracy reaching 98.67% (multilayer perceptron, Lab color space) for the samples stored in the refrigerator may indicate smaller quality changes caused by storage at a low temperature. The approach combining image textures and artificial intelligence turned out to be promising to monitor the quality changes in black currants during storage.

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Section: Resultsmentioning

confidence: 99%

Application of Imaging and Artificial Intelligence for Quality Monitoring of Stored Black Currant (Ribes nigrum L.)

Ropelewska

2022

Foods

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Evaluating the Classification of Freeze-Dried Slices and Cubes of Red-Fleshed Apple Genotypes Using Image Textures, Color Parameters, and Machine Learning

Ropelewska

Kruczyńska²,

Rady

et al. 2023

Agriculture

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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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Prediction of tannin, protein, and total phenolic content of grain sorghum using image analysis and machine learning

Cited by 2 publications

References 17 publications

Application of Imaging and Artificial Intelligence for Quality Monitoring of Stored Black Currant (Ribes nigrum L.)

Application of Imaging and Artificial Intelligence for Quality Monitoring of Stored Black Currant (Ribes nigrum L.)

Evaluating the Classification of Freeze-Dried Slices and Cubes of Red-Fleshed Apple Genotypes Using Image Textures, Color Parameters, and Machine Learning

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