[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-2147":3},{"id":4,"title":5,"url":6,"summary":7,"summary_zh":8,"content":9,"source_name":10,"source_url":6,"published_at":11,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":15,"score_detail":16,"sources":23,"tags":25,"view_count":32,"doi":33,"paper":34,"created_at":59},2147,"From Farm to Fork: Integrating Smart Farming Data with Isotopic and Spectroscopic Analysis for Food Authentication and Traceability","https:\u002F\u002Fdoi.org\u002F10.3390\u002Fpr14182884","Ensuring food authenticity, traceability, and quality has become a critical challenge in increasingly complex and globalized food supply chains. Conventional post-harvest analytical approaches, while powerful, often operate in isolation and fail to fully capture the influence of pre-harvest conditions on food composition. In parallel, the emergence of smart farming technologies has enabled the collection of high-resolution environmental and agronomic data, offering new opportunities to establish baseline signatures linked to geographical origin and production practices. This review explores the integration of pre-harvest data from precision agriculture with advanced post-harvest analytical techniques, focusing on spectroscopic and isotopic methods for food authentication. Recent advances in vibrational spectroscopy, including near- and mid-infrared, Fourier-transform infrared, and Raman techniques, alongside complementary methods such as nuclear magnetic resonance and fluorescence spectroscopy, have enabled rapid and non-destructive food fingerprinting. In parallel, isotope ratio mass spectrometry and compound-specific isotope analysis provide robust markers of origin, climate conditions, and agricultural inputs through the analysis of stable isotopes of carbon, hydrogen, oxygen, nitrogen, and sulfur. The combination of these analytical approaches with chemometric and machine learning tools facilitates the extraction of meaningful patterns from complex datasets. A central focus of this review is the development of integrated farm-to-fork frameworks that use multi-source data, including field sensor technologies, spectral fingerprints, and isotopic signatures, to enhance traceability and authentication. Applications across a wide range of food systems, including edible oils, beverages, plant-based products, and animal-derived foods, are critically evaluated to highlight the strengths and limitations of current methodologies. Key challenges related to data standardization, system interoperability, cost, portability, miniaturization and regulatory acceptance are discussed, alongside emerging solutions such as artificial intelligence-driven models, digital twins, and blockchain-enabled traceability systems. The review underscores a paradigm shift from reactive testing toward predictive and real-time food authentication systems, driven by the convergence of smart agriculture and advanced analytical chemistry. This integrated approach has the potential to significantly enhance transparency, trust, and sustainability in the global food system.","在日益复杂和全球化的食品供应链中，确保食品真实性、可追溯性和质量已成为一项关键挑战。传统的采后分析方法虽然功能强大，但往往独立运作，无法充分捕捉采前条件对食品成分的影响。与此同时，智慧农业技术的出现使得高分辨率环境和农艺数据的采集成为可能，为建立与地理来源和生产实践相关的基线特征谱提供了新机遇。本综述探讨了精准农业采前数据与先进采后分析技术的整合，重点关注用于食品鉴伪的光谱和同位素方法。振动光谱学的最新进展，包括近红外和中红外、傅里叶变换红外及拉曼技术，以及核磁共振和荧光光谱等互补方法，已使快速、无损的食品指纹分析成为可能。与此同时，同位素比值质谱（IRMS）和化合物特异性同位素分析（CSIA）通过分析碳、氢、氧、氮和硫的稳定同位素，提供了可靠的产地、气候条件和农业投入品标记。将这些分析方法与化学计量学和机器学习工具相结合，有助于从复杂数据集中提取有意义的模式。本综述的核心焦点是开发从农场到餐桌的集成框架，利用多源数据（包括田间传感器技术、光谱指纹和同位素特征）来增强可追溯性和鉴伪能力。本文对食用油、饮料、植物基产品和动物源性食品等广泛食品系统中的应用进行了批判性评估，以突出当前方法的优势和局限性。讨论了与数据标准化、系统互操作性、成本、便携性、小型化和法规接受度相关的关键挑战，以及人工智能驱动模型、数字孪生和区块链赋能可追溯系统等新兴解决方案。本综述强调了由智慧农业与先进分析化学融合所驱动的、从被动检测向预测性和实时食品鉴伪系统的范式转变。这种集成方法有望显著",null,"Processes","2026-09-10T00:00:00Z","论文",10,false,79,{"impact":17,"substance":18,"depth":17,"authority":19,"freshness":20,"relevant":21,"comment":22},18,22,13,8,1,"综述性论文，系统梳理精准农业数据与同位素\u002F光谱分析融合的农场到餐桌溯源框架，方法学视角有参考价值，但属综述而非原创突破，产业落地仍受成本与标准制约。",[24],{"name":10,"url":6},[26,27,28,29,30,31],"智慧农业","机器学习","农产品质量安全","区块链溯源","光谱检测","食品溯源",0,"10.3390\u002Fpr14182884",{"doi":33,"openalex_id":35,"authors":36,"venue":10,"cited_by_count":32,"oa_url":6,"card":52,"direction":56,"ingested_from":58},"W7212176948",[37,40,43,46,49],{"name":38,"orcid":39},"Maria Tarapoulouzi","https:\u002F\u002Forcid.org\u002F0000-0003-0206-4860",{"name":41,"orcid":42},"Jordi Cruz","https:\u002F\u002Forcid.org\u002F0000-0001-8191-8689",{"name":44,"orcid":45},"Yakdiel Rodríguez-Gallo","https:\u002F\u002Forcid.org\u002F0000-0002-5737-6442",{"name":47,"orcid":48},"Guillermo Medina-González","https:\u002F\u002Forcid.org\u002F0000-0002-2630-3400",{"name":50,"orcid":51},"Ioannis Pashalidis","https:\u002F\u002Forcid.org\u002F0000-0002-7587-6395",{"tldr":53,"method":54,"finding":55,"direction":56,"opportunity":57},"综述将精准农业采前数据与光谱、同位素分析结合，构建从农场到餐桌的食品溯源与真伪鉴别框架。","整合田间传感器数据、振动光谱、同位素比质谱及化学计量学与机器学习。","多源数据融合可推动食品鉴别从被动检测转向预测性、实时化体系。","智慧农业 \u002F 农业物联网","可研究田间传感器数据与光谱\u002F同位素指纹的标准化融合模型，并探索数字孪生与区块链在实时溯源中的落地。","openalex","2026-09-11T23:30:10.732050Z"]