The Use of Digital Color Imaging and Machine Learning for the Evaluation of the Effects of Shade Drying and Open-Air Sun Drying on Mint Leaf Quality

Ropelewska, Ewa; Sabancı, Kadir; Aslan, Muhammet Fatih

doi:10.3390/app13010206

Cited by 3 publications

(2 citation statements)

References 30 publications

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“…Drying under the direct sun can lead to losses in the components of the plants (Nurhaslina et al, 2022). Ropelewska et al (2023) reported that when plant leaves are exposed to direct solar radiation, their colours become lighter and their aromas decrease. At the same time, the drying time can make a difference in the amount and variety of the components of the plants.…”

Section: Chemical Compositionmentioning

confidence: 99%

Comparison of different drying methods for phytochemical quality of stevia (Stevia rebaudiana Bert.)

ÖZYİĞİT,

UÇAR,

ERUYGUR

et al. 2023

Not Sci Biol

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This study examined the effects of different drying methods on the chemical content, antioxidant, antibacterial, enzymatic and anticancer activity values of stevia (Stevia rebaudiana Bert.) extract. Stevia leaves were dried using four different methods (in the sun, shade, air conditioner, and oven), and extracts were collected and analyzed. Based on extract content, 2-tetradecyl acrylate was the major ingredient in air-conditioned and oven-dried applications (25.00% and 21.47%, respectively). The same compound was detected in both sun-drying and shade-drying methods, but the content was low. The antioxidant activity values of the samples were evaluated with 1,1-Diphenyl-2-picrylhydrazyl (DPPH) and 2,2′-azino-bis(3-ethylbenzothiazoline-6-sulfonic acid) (ABTS) radical scavenging tests. While the best result for DPPH (IC50 value 57.94 ± 0.63 µg/mL) was found in the shade-dried stevia samples, the best result in terms of ABTS test (IC50 value 44.03 ± 1.22 µg/mL) ml) was detected in oven-dried. When the samples were examined in terms of antimicrobial activity, it was seen that extracts of all drying methods were effective against Staphylococcus aureus. However, it was determined that the extracts obtained from plants dried in air conditioning and oven had a stronger effect. The cell viability assay was utilized to assess the antiproliferative effects of extract on L929 and MDA-MB-231 cell lines. The extracts did not significantly affect the L929 cell viability, while MDA-MB-231 remarkably reduced cell viability (sun drying, IC50 = 0.9 mg/mL; oven drying, IC50 = 0.65 mg/mL; shade drying, IC50 = 0.74 mg/mL; air conditioner drying, IC50 = 0.47 mg/mL). In particular, the extract obtained by the air conditioner drying method showed the most prominent cytotoxic effect. The results showed that drying using different methods had an impact on the quality standards of the stevia leaves.

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Section: Chemical Compositionmentioning

confidence: 99%

Comparison of different drying methods for phytochemical quality of stevia (Stevia rebaudiana Bert.)

ÖZYİĞİT,

UÇAR,

ERUYGUR

et al. 2023

Not Sci Biol

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“…Image analysis and machine learning were successfully applied in previous studies to classify fruits and vegetables [17][18][19] and detect changes in the product quality as a result of different processing, such as drying and fermentation [20][21][22][23]. Furthermore, image features were used to estimate and predict the chemical properties of food products [24,25].…”

Section: Introductionmentioning

confidence: 99%

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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Effect of Different Drying Methods on the Phytochemical and Antioxidant Properties of Soursop Leaves at Two Stages of Maturity

Lekjing,

Venkatachalam,

Charoenphun

et al. 2024

ACS Omega

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The Use of Digital Color Imaging and Machine Learning for the Evaluation of the Effects of Shade Drying and Open-Air Sun Drying on Mint Leaf Quality

Cited by 3 publications

References 30 publications

Comparison of different drying methods for phytochemical quality of stevia (Stevia rebaudiana Bert.)

Comparison of different drying methods for phytochemical quality of stevia (Stevia rebaudiana Bert.)

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

Effect of Different Drying Methods on the Phytochemical and Antioxidant Properties of Soursop Leaves at Two Stages of Maturity

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