{"title":"A Comprehensive Review of Advanced Deep Learning Approaches for Food Freshness Detection","authors":"Raj Singh, C. Nickhil, R.Nisha, Konga Upendar, Bhukya Jithender, Sankar Chandra Deka","doi":"10.1007/s12393-024-09385-3","DOIUrl":null,"url":null,"abstract":"<div><p>This comprehensive review highlights the significant strides made in the field of food freshness detection through the integration of deep learning and imaging techniques. By leveraging advanced neural networks, researchers have developed innovative methodologies that enhance the accuracy and efficiency of freshness monitoring. The fusion of various imaging modalities, with sophisticated deep learning algorithms has enabled more precise detection of quality attributes and spoilage indicators. This multidimensional approach not only improves the reliability of freshness assessments but also provides a more holistic view of condition of the food. Additionally, the review underscores the growing potential for these technologies to be applied in real-time monitoring systems, offering valuable insights for both producers and consumers. The advancements discussed pave the way for future research and development, emphasizing the need for continued innovation in integrating these technologies to address the challenges of food safety and quality assurance in an increasingly complex and dynamic market.</p><h3>Graphical Abstract</h3>\n<div><figure><div><div><picture><source><img></source></picture></div></div></figure></div></div>","PeriodicalId":565,"journal":{"name":"Food Engineering Reviews","volume":"17 1","pages":"127 - 160"},"PeriodicalIF":5.3000,"publicationDate":"2024-12-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Food Engineering Reviews","FirstCategoryId":"97","ListUrlMain":"https://link.springer.com/article/10.1007/s12393-024-09385-3","RegionNum":2,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"FOOD SCIENCE & TECHNOLOGY","Score":null,"Total":0}
引用次数: 0
Abstract
This comprehensive review highlights the significant strides made in the field of food freshness detection through the integration of deep learning and imaging techniques. By leveraging advanced neural networks, researchers have developed innovative methodologies that enhance the accuracy and efficiency of freshness monitoring. The fusion of various imaging modalities, with sophisticated deep learning algorithms has enabled more precise detection of quality attributes and spoilage indicators. This multidimensional approach not only improves the reliability of freshness assessments but also provides a more holistic view of condition of the food. Additionally, the review underscores the growing potential for these technologies to be applied in real-time monitoring systems, offering valuable insights for both producers and consumers. The advancements discussed pave the way for future research and development, emphasizing the need for continued innovation in integrating these technologies to address the challenges of food safety and quality assurance in an increasingly complex and dynamic market.
期刊介绍:
Food Engineering Reviews publishes articles encompassing all engineering aspects of today’s scientific food research. The journal focuses on both classic and modern food engineering topics, exploring essential factors such as the health, nutritional, and environmental aspects of food processing. Trends that will drive the discipline over time, from the lab to industrial implementation, are identified and discussed. The scope of topics addressed is broad, including transport phenomena in food processing; food process engineering; physical properties of foods; food nano-science and nano-engineering; food equipment design; food plant design; modeling food processes; microbial inactivation kinetics; preservation technologies; engineering aspects of food packaging; shelf-life, storage and distribution of foods; instrumentation, control and automation in food processing; food engineering, health and nutrition; energy and economic considerations in food engineering; sustainability; and food engineering education.