How big data analytics can strengthen large-scale food fortification and biofortification decision-making: A scoping review.

IF 4.8 3区 综合性期刊 Q1 MULTIDISCIPLINARY SCIENCES
Fiona Walsh,Anna Zhenchuk,Corey Luthringer,Christoph Kratz,Florian Schweigert
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引用次数: 0

Abstract

Big data analytics have shown great potential to improve decision-making in health, including disease surveillance and healthcare delivery. This scoping review explores how big data supports decision-making in large-scale food fortification (LSFF) and biofortification across the food value chain. Following PRISMA guidelines, we analyzed open-access peer-reviewed literature and gray literature from 2012 to 2022. Given the limited literature, we broadened our search to include big data applications in agriculture and nutrition, aiming to draw relevant insights for LSFF and biofortification. Of 1678 records, 28 mentioned LSFF or biofortification, all published between 2018 and 2022. Overall, most records focused on production (60%) and inputs (19.5%). Notably, 16.7% (n = 7) of records mentioning LSFF or biofortification addressed public health monitoring, compared to 2.3% (n = 45) of those without a mention. Use case examples include blockchain and Internet of Things (IoT) for fortified product traceability, machine learning to predict fortification gaps, and artificial intelligence to analyze anemia prevalence, highlighting opportunities to enhance both production and public health monitoring. Despite this potential, big data use in LSFF and biofortification remains limited. Expanding its use in underexplored areas, such as distribution and regulation, could enhance decision-making, efficiency, and sustainability in LSFF and biofortification.
大数据分析如何加强大规模食品强化和生物强化决策:范围审查。
大数据分析在改善卫生决策方面显示出巨大潜力,包括疾病监测和医疗保健服务。本综述探讨了大数据如何支持整个食品价值链中大规模食品强化(LSFF)和生物强化的决策。根据PRISMA的指导方针,我们分析了2012年至2022年的开放获取同行评议文献和灰色文献。鉴于文献有限,我们扩大了搜索范围,包括农业和营养领域的大数据应用,旨在为LSFF和生物强化提供相关见解。在1678项记录中,有28项提到了LSFF或生物强化,这些记录都是在2018年至2022年之间发布的。总的来说,大多数记录集中在产量(60%)和投入(19.5%)上。值得注意的是,16.7% (n = 7)提到LSFF或生物强化的记录涉及公共卫生监测,相比之下,没有提及的记录只有2.3% (n = 45)涉及公共卫生监测。用例示例包括用于强化产品可追溯性的区块链和物联网(IoT),用于预测强化差距的机器学习,以及用于分析贫血患病率的人工智能,突出了加强生产和公共卫生监测的机会。尽管有这种潜力,大数据在LSFF和生物强化中的应用仍然有限。扩大其在未开发领域的应用,如分布和监管,可以提高LSFF和生物强化的决策、效率和可持续性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Annals of the New York Academy of Sciences
Annals of the New York Academy of Sciences 综合性期刊-综合性期刊
CiteScore
11.00
自引率
1.90%
发文量
193
审稿时长
2-4 weeks
期刊介绍: Published on behalf of the New York Academy of Sciences, Annals of the New York Academy of Sciences provides multidisciplinary perspectives on research of current scientific interest with far-reaching implications for the wider scientific community and society at large. Each special issue assembles the best thinking of key contributors to a field of investigation at a time when emerging developments offer the promise of new insight. Individually themed, Annals special issues stimulate new ways to think about science by providing a neutral forum for discourse—within and across many institutions and fields.
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