Visual identification of material attributes in wakame: exploring thickness, strength, and chlorophyll content
This study investigates the potential of using visual features to predict key material attributes in wakame, focusing on thickness, strength, and chlorophyll content (SPAD values). We compared frozen and salted wakame samples to understand how different processing methods affect these predictions. U...
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| Format: | Article |
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Frontiers Media S.A.
2024-12-01
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| Series: | Frontiers in Sustainable Food Systems |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fsufs.2024.1493220/full |
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| author | Xin Lu Tomoya Suzuki Natsumi Shimoyama Zhuolin Wang Chunhong Yuan Chunhong Yuan |
| author_facet | Xin Lu Tomoya Suzuki Natsumi Shimoyama Zhuolin Wang Chunhong Yuan Chunhong Yuan |
| author_sort | Xin Lu |
| collection | DOAJ |
| description | This study investigates the potential of using visual features to predict key material attributes in wakame, focusing on thickness, strength, and chlorophyll content (SPAD values). We compared frozen and salted wakame samples to understand how different processing methods affect these predictions. Using a combination of RGB, L*a*b*, and HSV color features, we developed and evaluated various regression models, including simple linear regression, quadratic regression, and random forests. Our results indicate that color features can effectively predict SPAD values, particularly in frozen samples, with the best models achieving an R2 of 0.900. However, predicting thickness and strength proved more challenging, with models showing limited predictive power. Interestingly, strength predictions were more accurate for salted samples, suggesting that salt curing may enhance the relationship between visual features and physical strength. We found that processing methods significantly impact the effectiveness of prediction models. Freezing appears to better preserve the original optical properties of wakame, while salt curing introduces greater complexity, necessitating more sophisticated modeling approaches. This study contributes to the development of rapid, non-destructive methods for assessing wakame quality, which is crucial for the growing wakame industry. Our findings highlight the potential of visual analysis in wakame quality assessment while also emphasizing the need for tailored approaches based on processing methods. Future work should focus on refining these models and exploring additional factors that influence wakame properties. |
| format | Article |
| id | doaj-art-a7a355f4936d4d6db27f02dd75b81460 |
| institution | OA Journals |
| issn | 2571-581X |
| language | English |
| publishDate | 2024-12-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Sustainable Food Systems |
| spelling | doaj-art-a7a355f4936d4d6db27f02dd75b814602025-08-20T01:57:55ZengFrontiers Media S.A.Frontiers in Sustainable Food Systems2571-581X2024-12-01810.3389/fsufs.2024.14932201493220Visual identification of material attributes in wakame: exploring thickness, strength, and chlorophyll contentXin Lu0Tomoya Suzuki1Natsumi Shimoyama2Zhuolin Wang3Chunhong Yuan4Chunhong Yuan5Faculty of Science and Engineering, Iwate University, Morioka, JapanGraduate School of Arts and Sciences, Iwate University, Morioka, JapanGraduate School of Arts and Sciences, Iwate University, Morioka, JapanFaculty of Agriculture, Iwate University, Morioka, JapanFaculty of Agriculture, Iwate University, Morioka, JapanAgri-Innovation Center, Iwate University, Morioka, JapanThis study investigates the potential of using visual features to predict key material attributes in wakame, focusing on thickness, strength, and chlorophyll content (SPAD values). We compared frozen and salted wakame samples to understand how different processing methods affect these predictions. Using a combination of RGB, L*a*b*, and HSV color features, we developed and evaluated various regression models, including simple linear regression, quadratic regression, and random forests. Our results indicate that color features can effectively predict SPAD values, particularly in frozen samples, with the best models achieving an R2 of 0.900. However, predicting thickness and strength proved more challenging, with models showing limited predictive power. Interestingly, strength predictions were more accurate for salted samples, suggesting that salt curing may enhance the relationship between visual features and physical strength. We found that processing methods significantly impact the effectiveness of prediction models. Freezing appears to better preserve the original optical properties of wakame, while salt curing introduces greater complexity, necessitating more sophisticated modeling approaches. This study contributes to the development of rapid, non-destructive methods for assessing wakame quality, which is crucial for the growing wakame industry. Our findings highlight the potential of visual analysis in wakame quality assessment while also emphasizing the need for tailored approaches based on processing methods. Future work should focus on refining these models and exploring additional factors that influence wakame properties.https://www.frontiersin.org/articles/10.3389/fsufs.2024.1493220/fullwakamevisual analysisSPADthicknessstrengthmachine learning |
| spellingShingle | Xin Lu Tomoya Suzuki Natsumi Shimoyama Zhuolin Wang Chunhong Yuan Chunhong Yuan Visual identification of material attributes in wakame: exploring thickness, strength, and chlorophyll content Frontiers in Sustainable Food Systems wakame visual analysis SPAD thickness strength machine learning |
| title | Visual identification of material attributes in wakame: exploring thickness, strength, and chlorophyll content |
| title_full | Visual identification of material attributes in wakame: exploring thickness, strength, and chlorophyll content |
| title_fullStr | Visual identification of material attributes in wakame: exploring thickness, strength, and chlorophyll content |
| title_full_unstemmed | Visual identification of material attributes in wakame: exploring thickness, strength, and chlorophyll content |
| title_short | Visual identification of material attributes in wakame: exploring thickness, strength, and chlorophyll content |
| title_sort | visual identification of material attributes in wakame exploring thickness strength and chlorophyll content |
| topic | wakame visual analysis SPAD thickness strength machine learning |
| url | https://www.frontiersin.org/articles/10.3389/fsufs.2024.1493220/full |
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