Lunzhao Yi , Wenfu Wang , Yuhua Diao , Sanli Yi , Ying Shang , Dabing Ren , Kun Ge , Ying Gu
{"title":"人工智能在食品质量和安全指标定量分析方面的最新进展：综述","authors":"Lunzhao Yi , Wenfu Wang , Yuhua Diao , Sanli Yi , Ying Shang , Dabing Ren , Kun Ge , Ying Gu","doi":"10.1016/j.trac.2024.117944","DOIUrl":null,"url":null,"abstract":"<div><p>Food quality and safety (FQS) are crucial aspects of everyone's life and health. With the rapidly advancing field of analytical sciences, there is a growing demand for intuitive, accurate, and swift control of FQS. In recent years, artificial intelligence (AI) has emerged as a great opportunity, offering unparalleled opportunities for extracting information and making decisions from complex or large datasets in areas like chromatography, mass spectrometry, and spectroscopy for the identification of FQS indicators. This review provides a comprehensive overview of AI-based technology's general algorithms for FQS indicator analysis. Additionally, it surveys AI-based methods that are at the forefront of analytical techniques and hold significant potential for enhancing the smart control of FQS indicators. Finally, we highlight key challenges and offer recommendations to accelerate progress towards intelligent FQS control.</p></div>","PeriodicalId":439,"journal":{"name":"Trends in Analytical Chemistry","volume":"180 ","pages":"Article 117944"},"PeriodicalIF":11.8000,"publicationDate":"2024-08-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Recent advances of artificial intelligence in quantitative analysis of food quality and safety indicators: A review\",\"authors\":\"Lunzhao Yi , Wenfu Wang , Yuhua Diao , Sanli Yi , Ying Shang , Dabing Ren , Kun Ge , Ying Gu\",\"doi\":\"10.1016/j.trac.2024.117944\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><p>Food quality and safety (FQS) are crucial aspects of everyone's life and health. With the rapidly advancing field of analytical sciences, there is a growing demand for intuitive, accurate, and swift control of FQS. In recent years, artificial intelligence (AI) has emerged as a great opportunity, offering unparalleled opportunities for extracting information and making decisions from complex or large datasets in areas like chromatography, mass spectrometry, and spectroscopy for the identification of FQS indicators. This review provides a comprehensive overview of AI-based technology's general algorithms for FQS indicator analysis. Additionally, it surveys AI-based methods that are at the forefront of analytical techniques and hold significant potential for enhancing the smart control of FQS indicators. Finally, we highlight key challenges and offer recommendations to accelerate progress towards intelligent FQS control.</p></div>\",\"PeriodicalId\":439,\"journal\":{\"name\":\"Trends in Analytical Chemistry\",\"volume\":\"180 \",\"pages\":\"Article 117944\"},\"PeriodicalIF\":11.8000,\"publicationDate\":\"2024-08-29\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Trends in Analytical Chemistry\",\"FirstCategoryId\":\"1\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S0165993624004278\",\"RegionNum\":1,\"RegionCategory\":\"化学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"CHEMISTRY, ANALYTICAL\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Trends in Analytical Chemistry","FirstCategoryId":"1","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0165993624004278","RegionNum":1,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"CHEMISTRY, ANALYTICAL","Score":null,"Total":0}
Recent advances of artificial intelligence in quantitative analysis of food quality and safety indicators: A review
Food quality and safety (FQS) are crucial aspects of everyone's life and health. With the rapidly advancing field of analytical sciences, there is a growing demand for intuitive, accurate, and swift control of FQS. In recent years, artificial intelligence (AI) has emerged as a great opportunity, offering unparalleled opportunities for extracting information and making decisions from complex or large datasets in areas like chromatography, mass spectrometry, and spectroscopy for the identification of FQS indicators. This review provides a comprehensive overview of AI-based technology's general algorithms for FQS indicator analysis. Additionally, it surveys AI-based methods that are at the forefront of analytical techniques and hold significant potential for enhancing the smart control of FQS indicators. Finally, we highlight key challenges and offer recommendations to accelerate progress towards intelligent FQS control.
期刊介绍：
TrAC publishes succinct and critical overviews of recent advancements in analytical chemistry, designed to assist analytical chemists and other users of analytical techniques. These reviews offer excellent, up-to-date, and timely coverage of various topics within analytical chemistry. Encompassing areas such as analytical instrumentation, biomedical analysis, biomolecular analysis, biosensors, chemical analysis, chemometrics, clinical chemistry, drug discovery, environmental analysis and monitoring, food analysis, forensic science, laboratory automation, materials science, metabolomics, pesticide-residue analysis, pharmaceutical analysis, proteomics, surface science, and water analysis and monitoring, these critical reviews provide comprehensive insights for practitioners in the field.
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