Advance in Application of Deep Learning in Food Quality and Safety Detection
With the improvement of people's living standards, consumers' demand for food quality and safety is growing. Traditional methods for detecting food quality and safety can no longer meet the demand for efficient, accurate and reliable detection. Therefore, it becomes imperative to seek a mo...
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| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | zho |
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The editorial department of Science and Technology of Food Industry
2025-03-01
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| Series: | Shipin gongye ke-ji |
| Subjects: | |
| Online Access: | http://www.spgykj.com/cn/article/doi/10.13386/j.issn1002-0306.2024040375 |
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| author | Xing GUO Ying SUN Shuping LIU Xuexin YANG Juyang ZHAO Lianzhou JIANG |
| author_facet | Xing GUO Ying SUN Shuping LIU Xuexin YANG Juyang ZHAO Lianzhou JIANG |
| author_sort | Xing GUO |
| collection | DOAJ |
| description | With the improvement of people's living standards, consumers' demand for food quality and safety is growing. Traditional methods for detecting food quality and safety can no longer meet the demand for efficient, accurate and reliable detection. Therefore, it becomes imperative to seek a more efficient and convenient detection method. On this basis, the rapid development of deep neural network-based machine learning technology, i.e., deep learning, has brought new opportunities for food quality and safety detection. This paper focuses on the application progress of deep learning in food quality and safety detection. It introduces the principles of traditional machine learning and deep learning, and elaborates on the applications of deep learning in food origin tracing and food quality, including the detection of food defects, freshness, adulteration, and pathogens. Furthermore, it looks forward to the development trends of deep learning in the field of food quality and safety detection, aiming to provide theoretical references and research ideas for this field. |
| format | Article |
| id | doaj-art-61afa6a66ea4424188fa1541edb7f78b |
| institution | OA Journals |
| issn | 1002-0306 |
| language | zho |
| publishDate | 2025-03-01 |
| publisher | The editorial department of Science and Technology of Food Industry |
| record_format | Article |
| series | Shipin gongye ke-ji |
| spelling | doaj-art-61afa6a66ea4424188fa1541edb7f78b2025-08-20T01:57:28ZzhoThe editorial department of Science and Technology of Food IndustryShipin gongye ke-ji1002-03062025-03-01466202910.13386/j.issn1002-0306.20240403752024040375-6Advance in Application of Deep Learning in Food Quality and Safety DetectionXing GUO0Ying SUN1Shuping LIU2Xuexin YANG3Juyang ZHAO4Lianzhou JIANG5College of Tourism and Cuisine, Harbin University of Commerce, Harbin 150028, ChinaCollege of Tourism and Cuisine, Harbin University of Commerce, Harbin 150028, ChinaCollege of Tourism and Cuisine, Harbin University of Commerce, Harbin 150028, ChinaCollege of Tourism and Cuisine, Harbin University of Commerce, Harbin 150028, ChinaCollege of Tourism and Cuisine, Harbin University of Commerce, Harbin 150028, ChinaCollege of Food Science, Northeast Agricultural University, Harbin 150030, ChinaWith the improvement of people's living standards, consumers' demand for food quality and safety is growing. Traditional methods for detecting food quality and safety can no longer meet the demand for efficient, accurate and reliable detection. Therefore, it becomes imperative to seek a more efficient and convenient detection method. On this basis, the rapid development of deep neural network-based machine learning technology, i.e., deep learning, has brought new opportunities for food quality and safety detection. This paper focuses on the application progress of deep learning in food quality and safety detection. It introduces the principles of traditional machine learning and deep learning, and elaborates on the applications of deep learning in food origin tracing and food quality, including the detection of food defects, freshness, adulteration, and pathogens. Furthermore, it looks forward to the development trends of deep learning in the field of food quality and safety detection, aiming to provide theoretical references and research ideas for this field.http://www.spgykj.com/cn/article/doi/10.13386/j.issn1002-0306.2024040375deep learningneural networksfood quality and safetyfood traceabilityquality detection |
| spellingShingle | Xing GUO Ying SUN Shuping LIU Xuexin YANG Juyang ZHAO Lianzhou JIANG Advance in Application of Deep Learning in Food Quality and Safety Detection Shipin gongye ke-ji deep learning neural networks food quality and safety food traceability quality detection |
| title | Advance in Application of Deep Learning in Food Quality and Safety Detection |
| title_full | Advance in Application of Deep Learning in Food Quality and Safety Detection |
| title_fullStr | Advance in Application of Deep Learning in Food Quality and Safety Detection |
| title_full_unstemmed | Advance in Application of Deep Learning in Food Quality and Safety Detection |
| title_short | Advance in Application of Deep Learning in Food Quality and Safety Detection |
| title_sort | advance in application of deep learning in food quality and safety detection |
| topic | deep learning neural networks food quality and safety food traceability quality detection |
| url | http://www.spgykj.com/cn/article/doi/10.13386/j.issn1002-0306.2024040375 |
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