摘要: 大數(shù)據(jù)是一種新興的信息技術，廣泛應用于各行各業(yè)。大數(shù)據(jù)技術在膳食營養(yǎng)健康中的應用主要是結合營養(yǎng)學、醫(yī)學等相關學科，利用大數(shù)據(jù)技術挖掘、分析數(shù)據(jù)，再根據(jù)個人生理指標提供個性化的信息服務。本文以Web of Science核心合集和中國知網(wǎng)中文數(shù)據(jù)庫為數(shù)據(jù)來源，利用VOSviewer，對近10年大數(shù)據(jù)與膳食營養(yǎng)健康領域的相關文獻進行年度發(fā)文量和關鍵詞分析?？偨Y分析了大數(shù)據(jù)技術在膳食營養(yǎng)健康領域中的應用以及其所面臨的挑戰(zhàn)。最后，對大數(shù)據(jù)在膳食營養(yǎng)健康領域的未來研究趨勢做出了展望，以期為大數(shù)據(jù)技術在該領域的深入研究及應用提供幫助。

Abstract: As an emerging information technology, big data has been widely used in all aspects of life. As for the application of big data technology in dietary nutrition and health, it revolves mainly around the combination of nutrition, medicine and other relevant disciplines, during which big data technology is adopted to mine and analyze data for the provision of personalized information services tailored to personal physiological indicators. With the core collection of Web of Science and CNKI Chinese database as data sources, and VOSviewer is applied in this paper to analyze the annual volume of publication and the keywords of relevant literature on big data as well as dietary nutrition and health published over the past 10 years. Then, the application of big data technology in the field of dietary nutrition and health and the challenges arising from this field are summarized and analyzed. Finally, the future trend of big data research in the field of dietary nutrition and health is indicated, which would provide guidance for the in-depth research and application of big data technology in this field.

圖  1   2011~2021年大數(shù)據(jù)在膳食營養(yǎng)健康領域研究的文獻數(shù)量圖

Figure  1.   Distribution of the number of literatures on big data in the field of dietary nutrition and health from 2011 to 2021

圖  2   CNKI關鍵詞共現(xiàn)網(wǎng)絡圖

Figure  2.   CNKI keyword co-occurrence network diagram

圖  3   WOS關鍵詞共現(xiàn)網(wǎng)絡圖

Figure  3.   WOS keyword co-occurrence network diagram

表  1   2011~2021年間大數(shù)據(jù)在膳食營養(yǎng)健康相關研究關鍵詞共現(xiàn)聚類簇

Table  1   Co-occurrence clusters of big data in dietary nutrition and health related research from 2011 to 2021

聚類編號 CNKI關鍵詞聚類簇
聚類標簽WOS關鍵詞聚類簇
聚類標簽 1hadoop、應用研究、數(shù)據(jù)挖掘、
智慧醫(yī)療、糖尿病、飲食管理genetic testing, high-fat diet, metabotying, nutrition,
personalized nutrition, precision nutrition, vitamins
2亞健康、健康管理、大數(shù)據(jù)技術、
大數(shù)據(jù)時代、慢性病3 d food printing, 3 d printing, customized fabrication,
muti-material, platform design, printing precision
3個性化營養(yǎng)、慢性病預防和管理、
精準營養(yǎng)、營養(yǎng)基因competitive sports, dietary nutrition, nutrition supplement,
nutrition status, reasonable diet4個性化推薦、推薦系統(tǒng)、
機器學習、深度學習allergic diseases,
metabolic intervention,
pregnancy5云計算、慢性病管理、物聯(lián)網(wǎng)body mass index, enterotype,
gut microbiota, short-chain fatty acids6云平臺、互聯(lián)網(wǎng)+、營養(yǎng)健康diet, metabolomics,
obesity, proteomics7信息化平臺、大數(shù)據(jù)、慢病管理fatty acids, nutrigenomics8體質(zhì)健康、大數(shù)據(jù)平臺、學生9營養(yǎng)、營養(yǎng)管理、食品安全10健康領域、大數(shù)據(jù)背景、食品營養(yǎng) 11隱私保護12管理系統(tǒng)

表  2   國外食物成分數(shù)據(jù)庫相關信息

Table  2   Relevant information of foreign food composition databases

數(shù)據(jù)庫名稱數(shù)據(jù)庫內(nèi)容用途 阿拉伯食物成分數(shù)據(jù)庫[35]數(shù)據(jù)庫開發(fā)為在線膳食評估工具myfood24；包含2016種食物及其大量營養(yǎng)素、微量營養(yǎng)素數(shù)據(jù)
旨在促進營養(yǎng)流行病學研究，幫助測量阿拉伯人口的
飲食攝入
愛爾蘭食物成分數(shù)據(jù)庫[36]共編入938種食品，包括459種復合菜肴、92種營養(yǎng)補充劑促進數(shù)據(jù)共享；為研究人員和決策者提供愛爾蘭特定
食品成分數(shù)據(jù)
DietSys[37]包含9851種食品，其中5901種被分配為1至27份或者部分大小的選擇，41% 的食品被歸類為超加工食品用于高效、準確的飲食分析；用于評估巴西、阿根廷和
北美居民的飲食攝入量myfood24 FCDB[38]
開發(fā)為在線膳食評估工myfood24；包含40274常用食品和品牌食品，包含120個大量營養(yǎng)素、微量營養(yǎng)素數(shù)據(jù)，5669個食物部分圖像
反映英國消費者可獲得的各種食品，并潛在地
提高飲食評估的準確性喀里多尼亞食物成分
數(shù)據(jù)庫[39]數(shù)據(jù)庫集成到基于移動應用程序的24 h召回工具iRecall中，共有972種食品，涵蓋40個食品類別和25種營養(yǎng)價值，包括飽和脂肪和總糖對食物和營養(yǎng)數(shù)據(jù)進行更改提供了靈活性；為新喀里多尼亞的居民制定有針對性的公共衛(wèi)生政策和個人及社區(qū)一級的營養(yǎng)干預，以改善飲食，減少與飲食有關的健康風險

表  3   營養(yǎng)健康服務平臺信息

Table  3   Information on nutrition and health service platform

平臺名稱結構框架服務對象目的 食品安全與營養(yǎng)云服務平臺架構方案[41]數(shù)據(jù)匯聚層、數(shù)據(jù)處理層、信息分析層、用戶應用層
服務于政府、企業(yè)、
消費者、社會媒體實現(xiàn)數(shù)據(jù)收集、數(shù)據(jù)處理以及信息服務膳食營養(yǎng)云服務
平臺[42]基礎架構即服務、平臺即服務和軟件即服務，不同云層
提供不同的服務
一般人群、餐飲企業(yè)、專業(yè)營養(yǎng)師以及公共衛(wèi)生政府部門傳播營養(yǎng)學及慢性病預防知識；獲取人群膳食信息將相關統(tǒng)計數(shù)據(jù)快速傳遞給政府后指導民眾營養(yǎng)均衡、科學膳食；為政府制定
相關營養(yǎng)政策提供參考依據(jù)
營養(yǎng)健康知識服務
系統(tǒng)平臺[43]知識資源倉儲層、知識服務支撐層、知識服務用戶層專家、一般人群打造符合國家營養(yǎng)健康產(chǎn)業(yè)發(fā)展需要的營養(yǎng)健康知識資源匯聚中心和綜合性知識服務平臺；探索和構建營養(yǎng)健康領域知識服務模式

表  4   三種疾病監(jiān)測系統(tǒng)信息

Table  4   Information of three disease surveillance systems

名稱框架用途 高血壓早期預警和
健康管理平臺[64]數(shù)據(jù)采集和存儲模塊、健康風險分析模塊、健康指導模塊、健康管理
效果評價模塊
利用機器學習的數(shù)學模型進行健康風險評估，采用大數(shù)據(jù)的Hadoop平臺。對與影響
高血壓的有關因素（攝鹽量、攝油量等）進行監(jiān)測后，由專家給出健康指導意見，
提高患者的知曉率、治療率智能體重管控系統(tǒng)[65]攝入量模塊、輸出量模塊、個人體重數(shù)據(jù)、群體體重數(shù)據(jù)、預警模塊依托大數(shù)據(jù)提供專業(yè)、實時、高效的智能系統(tǒng)。對個人數(shù)據(jù)進行長期收集，將個人數(shù)據(jù)與群體數(shù)據(jù)進行比對，對用戶在使用過程中的營養(yǎng)素攝入量、熱量、運動量等直接與
健康情況相關的各項指標進行監(jiān)測，實時發(fā)出預警
糖尿病患者膳食
管理系統(tǒng)[66]用戶層、應用層、數(shù)據(jù)層和支持層4層能有效輔助和優(yōu)化糖尿病患者臨床飲食治療工作；利用食物互換法編制飲食方案，
能科學有效地滿足患者膳食的個性化需求；為患者、醫(yī)護人員及管理者之間交流互動
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