Deep learning-based detection of wine bottle capsules for contamination prevention
Quality control of wine bottle packaging is crucial for preventing wine contamination and safety hazards, ensuring product integrity, and maintaining the overall quality of the product. The presence of metallic capsules has been demonstrated to significantly reduce airborne contamination, particular...
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| Format: | Article |
| Language: | English |
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Elsevier
2025-09-01
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| Series: | Results in Engineering |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2590123025024582 |
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| author | Diego Caballero-Ramírez Guilherme Tortorella Jorge García-Alcaraz Emilio Jiménez-Macías Jorge Limon-Romero Yolanda Baez-Lopez Diego Tlapa |
| author_facet | Diego Caballero-Ramírez Guilherme Tortorella Jorge García-Alcaraz Emilio Jiménez-Macías Jorge Limon-Romero Yolanda Baez-Lopez Diego Tlapa |
| author_sort | Diego Caballero-Ramírez |
| collection | DOAJ |
| description | Quality control of wine bottle packaging is crucial for preventing wine contamination and safety hazards, ensuring product integrity, and maintaining the overall quality of the product. The presence of metallic capsules has been demonstrated to significantly reduce airborne contamination, particularly from 2,4,6-trichloroanisol (TCA), a major contributor to cork taint. Traditional capsule detection in the wine industry primarily relies on visual inspection, thus prone to human error. Despite the progress of Industry 4.0, the application of artificial intelligence (AI) for automated inspection in the wine industry remains limited. This study evaluates the suitability of deep learning (DL) for inspecting capsules in wine bottles. A dataset of 12,050 images was used to train, test, and validate the performance of YOLOv8, YOLOv9, and YOLOv11 models. All three models demonstrated similar performance, achieving high precision (>96 %), recall (>97 %), and mAP (>98 %). Notably, YOLOv11 exhibited the fastest inference speed, making it a strong candidate for real-time detection. These results demonstrated the feasibility of deep learning (DL) in assisting humans with wine packaging inspection to prevent further airborne contamination and enhance real-time quality control. The proposed approach provides a practical and affordable solution to reduce reliance on manual inspection, particularly for small and medium producers, making inspection processes smarter and more efficient. |
| format | Article |
| id | doaj-art-38d3f346377545e19f1bb1b562871f0e |
| institution | DOAJ |
| issn | 2590-1230 |
| language | English |
| publishDate | 2025-09-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Results in Engineering |
| spelling | doaj-art-38d3f346377545e19f1bb1b562871f0e2025-08-20T02:47:13ZengElsevierResults in Engineering2590-12302025-09-012710638810.1016/j.rineng.2025.106388Deep learning-based detection of wine bottle capsules for contamination preventionDiego Caballero-Ramírez0Guilherme Tortorella1Jorge García-Alcaraz2Emilio Jiménez-Macías3Jorge Limon-Romero4Yolanda Baez-Lopez5Diego Tlapa6Facultad de Ingeniería, Arquitectura y Diseño; Universidad Autónoma de Baja California, Ensenada 21100, MexicoDepartment of Mechanical Engineering, The University of Melbourne, Melbourne 3052, Australia; IAE Business School, Universidad Austral, Buenos Aires 1611, Argentina; Fundacao Dom Cabral, Belo Horizonte, BrazilDepartment of Industrial Engineering and Manufacturing, Institute of Engineering and Technology, Universidad Autónoma de Ciudad Juárez, Juárez 32310, Chihuahua, MexicoDepartment of Electric Engineering, Universidad De La Rioja, Logroño 26004, EspañaFacultad de Ingeniería, Arquitectura y Diseño; Universidad Autónoma de Baja California, Ensenada 21100, MexicoFacultad de Ingeniería, Arquitectura y Diseño; Universidad Autónoma de Baja California, Ensenada 21100, MexicoFacultad de Ingeniería, Arquitectura y Diseño; Universidad Autónoma de Baja California, Ensenada 21100, Mexico; Corresponding author.Quality control of wine bottle packaging is crucial for preventing wine contamination and safety hazards, ensuring product integrity, and maintaining the overall quality of the product. The presence of metallic capsules has been demonstrated to significantly reduce airborne contamination, particularly from 2,4,6-trichloroanisol (TCA), a major contributor to cork taint. Traditional capsule detection in the wine industry primarily relies on visual inspection, thus prone to human error. Despite the progress of Industry 4.0, the application of artificial intelligence (AI) for automated inspection in the wine industry remains limited. This study evaluates the suitability of deep learning (DL) for inspecting capsules in wine bottles. A dataset of 12,050 images was used to train, test, and validate the performance of YOLOv8, YOLOv9, and YOLOv11 models. All three models demonstrated similar performance, achieving high precision (>96 %), recall (>97 %), and mAP (>98 %). Notably, YOLOv11 exhibited the fastest inference speed, making it a strong candidate for real-time detection. These results demonstrated the feasibility of deep learning (DL) in assisting humans with wine packaging inspection to prevent further airborne contamination and enhance real-time quality control. The proposed approach provides a practical and affordable solution to reduce reliance on manual inspection, particularly for small and medium producers, making inspection processes smarter and more efficient.http://www.sciencedirect.com/science/article/pii/S2590123025024582Wine industryQuality controlArtificial intelligenceDeep learningYOLO models |
| spellingShingle | Diego Caballero-Ramírez Guilherme Tortorella Jorge García-Alcaraz Emilio Jiménez-Macías Jorge Limon-Romero Yolanda Baez-Lopez Diego Tlapa Deep learning-based detection of wine bottle capsules for contamination prevention Results in Engineering Wine industry Quality control Artificial intelligence Deep learning YOLO models |
| title | Deep learning-based detection of wine bottle capsules for contamination prevention |
| title_full | Deep learning-based detection of wine bottle capsules for contamination prevention |
| title_fullStr | Deep learning-based detection of wine bottle capsules for contamination prevention |
| title_full_unstemmed | Deep learning-based detection of wine bottle capsules for contamination prevention |
| title_short | Deep learning-based detection of wine bottle capsules for contamination prevention |
| title_sort | deep learning based detection of wine bottle capsules for contamination prevention |
| topic | Wine industry Quality control Artificial intelligence Deep learning YOLO models |
| url | http://www.sciencedirect.com/science/article/pii/S2590123025024582 |
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