{"title":"在食品分析中整合多种无损光谱传感器(NDSS)的数据融合策略","authors":"","doi":"10.1016/j.trac.2024.117957","DOIUrl":null,"url":null,"abstract":"<div><p>The evolving landscape of agri-food systems, driven by climate change and population growth, necessitates innovative approaches to ensure food integrity, safety, and sustainability. This review explores the role of data fusion strategies, particularly focusing on non-destructive spectroscopic sensors (NDSS) in three key application contexts: in-field monitoring, on/in-line food processing, and food quality authentication. Various data fusion scenarios, including fusing spectra from different spectroscopic platforms, integrating images and spectra, and combining non-spectroscopic sensor data with spectroscopic ones are reviewed. Focus is set on practical considerations, such as selecting the level of data fusion, defining blocks, variable selection, and validation methods, highlighting the importance of tailored approaches based on research aims and data characteristics.</p><p>While combining information from diverse sensors generally enhances information extraction and modelling performance, their implementation in routine applications is still limited and especially studies focused on data fusion models’ performance over time and their maintenance are lacking.</p></div>","PeriodicalId":439,"journal":{"name":"Trends in Analytical Chemistry","volume":null,"pages":null},"PeriodicalIF":11.8000,"publicationDate":"2024-09-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S0165993624004400/pdfft?md5=ba7b79557644151f18b5756a5babbdd0&pid=1-s2.0-S0165993624004400-main.pdf","citationCount":"0","resultStr":"{\"title\":\"Data fusion strategies for the integration of diverse non-destructive spectral sensors (NDSS) in food analysis\",\"authors\":\"\",\"doi\":\"10.1016/j.trac.2024.117957\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><p>The evolving landscape of agri-food systems, driven by climate change and population growth, necessitates innovative approaches to ensure food integrity, safety, and sustainability. This review explores the role of data fusion strategies, particularly focusing on non-destructive spectroscopic sensors (NDSS) in three key application contexts: in-field monitoring, on/in-line food processing, and food quality authentication. Various data fusion scenarios, including fusing spectra from different spectroscopic platforms, integrating images and spectra, and combining non-spectroscopic sensor data with spectroscopic ones are reviewed. Focus is set on practical considerations, such as selecting the level of data fusion, defining blocks, variable selection, and validation methods, highlighting the importance of tailored approaches based on research aims and data characteristics.</p><p>While combining information from diverse sensors generally enhances information extraction and modelling performance, their implementation in routine applications is still limited and especially studies focused on data fusion models’ performance over time and their maintenance are lacking.</p></div>\",\"PeriodicalId\":439,\"journal\":{\"name\":\"Trends in Analytical Chemistry\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":11.8000,\"publicationDate\":\"2024-09-04\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://www.sciencedirect.com/science/article/pii/S0165993624004400/pdfft?md5=ba7b79557644151f18b5756a5babbdd0&pid=1-s2.0-S0165993624004400-main.pdf\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Trends in Analytical Chemistry\",\"FirstCategoryId\":\"1\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S0165993624004400\",\"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/S0165993624004400","RegionNum":1,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"CHEMISTRY, ANALYTICAL","Score":null,"Total":0}
Data fusion strategies for the integration of diverse non-destructive spectral sensors (NDSS) in food analysis
The evolving landscape of agri-food systems, driven by climate change and population growth, necessitates innovative approaches to ensure food integrity, safety, and sustainability. This review explores the role of data fusion strategies, particularly focusing on non-destructive spectroscopic sensors (NDSS) in three key application contexts: in-field monitoring, on/in-line food processing, and food quality authentication. Various data fusion scenarios, including fusing spectra from different spectroscopic platforms, integrating images and spectra, and combining non-spectroscopic sensor data with spectroscopic ones are reviewed. Focus is set on practical considerations, such as selecting the level of data fusion, defining blocks, variable selection, and validation methods, highlighting the importance of tailored approaches based on research aims and data characteristics.
While combining information from diverse sensors generally enhances information extraction and modelling performance, their implementation in routine applications is still limited and especially studies focused on data fusion models’ performance over time and their maintenance are lacking.
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.