JOURNAL OF LIGHT INDUSTRY

CN 41-1437/TS  ISSN 2096-1553

数据融合策略在食用油真实性鉴别中的研究与应用进展

李艳坤 ,  张伟 ,  刘彦伶

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李艳坤, 张伟, 刘彦伶. 数据融合策略在食用油真实性鉴别中的研究与应用进展[J]. 轻工学报, 2024, 39(5): 50-59. doi: 10.12187/2024.05.006
引用本文: 李艳坤, 张伟, 刘彦伶. 数据融合策略在食用油真实性鉴别中的研究与应用进展[J]. 轻工学报, 2024, 39(5): 50-59. doi: 10.12187/2024.05.006
shu
LI Yankun, ZHANG Wei and LIU Yanling. Research and application progress of data fusion strategy in authenticity identification of edible oil[J]. Journal of Light Industry, 2024, 39(5): 50-59. doi: 10.12187/2024.05.006
Citation: LI Yankun, ZHANG Wei and LIU Yanling. Research and application progress of data fusion strategy in authenticity identification of edible oil[J]. Journal of Light Industry, 2024, 39(5): 50-59. doi: 10.12187/2024.05.006
shu

数据融合策略在食用油真实性鉴别中的研究与应用进展

  • 1. 华北电力大学 环境科学与工程系, 河北 保定 071003;

  • 2. 华北电力大学 河北省燃煤电站烟气多污染物协同控制重点实验室, 河北 保定 071003

    • 作者简介: 李艳坤(1977—),女,河北省邯郸市人,华北电力大学副教授,博士,主要研究方向为化学计量学、环境分析化学。E-mail:liyankun_ncepu@foxmail.com;

  • 基金项目: 国创计划资助项目(S202310079127)
    中央高校基本科研业务费项目(2017MS135)

  • 中图分类号: O657.3;TS227

Research and application progress of data fusion strategy in authenticity identification of edible oil

  • 1. Department of Environmental Science and Engineering, North China Electric Power University, Baoding 071003, China;

  • 2. Hebei Key Laboratory of Power Plant Flue Gas Multi-Pollutants Control, North China Electric Power University, Baoding 071003, China

  • Received Date: 2023-11-15
    Accepted Date: 2024-01-18
    Available Online: 2024-10-15

    CLC number: O657.3;TS227

  • 摘要: 对基于光谱、质谱、色谱等检测技术的数据融合策略及其在食用油真实性鉴别中的研究及应用现状进行综述,指出:目前,广泛应用于食用油真实性鉴别的检测技术包括光谱、色谱、质谱、电子传感器等。然而,单一检测技术往往只关注某一特定的数据或指标,当食用油所含成分较复杂时,无法充分消除叠加效应、基线漂移、噪声等问题。数据融合策略分为数据层融合、特征层融合和决策层融合三类,结合化学计量学方法可以综合不同检测技术获取的数据,提取更丰富的数据特征信息,从而提高食用油真实性的鉴别效果。不同的新型检测技术之间,或将其与传统光谱、质谱、色谱等检测技术之间进行数据融合,可以快速、准确地实现食用油掺伪鉴别、品种分类和产地溯源,未来可就改进现有分析方法、结合深度学习算法开发新型融合算法、引入云计算提高食用油鉴别实时性等方面进行深入研究,以推动数据融合策略在食用油真实性鉴别领域的发展与创新。
    Abstract: An overview of data fusion strategies based on spectroscopy, mass spectrometry, chromatography and other detection technologies and their current research and application in authenticity identification of edible oils was presented, pointing out that: at present, detection technologies widely used for authenticity identification of edible oils including spectroscopy, chromatography, mass spectrometry and electronic sensors. However, a single detection technique often focused only on a specific data or index, which could not fully eliminate the superposition effect, baseline drift and noise when the ingredients contained in edible oils were more complex. Data fusion strategies were categorized into three types: data layer fusion, feature layer fusion and decision layer fusion. Combined with chemometrics methods, the data obtained by different detection technologies could be integrated to obtain and extract richer data feature information, thus improving the authenticity identification of edible oils. Data fusion between various novel detection technologies, or between new and traditional spectroscopy, mass spectrometry, chromatography and other detection technologies, which could quickly and accurately achieved the identification of adulteration of edible oils, variety classification and origin traceability. In the future, in-depth research could be carried out on the improvement of the existing analytical methods, the development of new fusion algorithms combined with deep learning algorithms, and the introduction of cloud computing to improve real-time edible oil identification, so as to promote the development of data fusion strategy in the field of edible oil authenticity identification.
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李艳坤, 张伟, 刘彦伶. 数据融合策略在食用油真实性鉴别中的研究与应用进展[J]. 轻工学报, 2024, 39(5): 50-59. doi: 10.12187/2024.05.006
引用本文: 李艳坤, 张伟, 刘彦伶. 数据融合策略在食用油真实性鉴别中的研究与应用进展[J]. 轻工学报, 2024, 39(5): 50-59. doi: 10.12187/2024.05.006
shu
LI Yankun, ZHANG Wei and LIU Yanling. Research and application progress of data fusion strategy in authenticity identification of edible oil[J]. Journal of Light Industry, 2024, 39(5): 50-59. doi: 10.12187/2024.05.006
Citation: LI Yankun, ZHANG Wei and LIU Yanling. Research and application progress of data fusion strategy in authenticity identification of edible oil[J]. Journal of Light Industry, 2024, 39(5): 50-59. doi: 10.12187/2024.05.006
shu

数据融合策略在食用油真实性鉴别中的研究与应用进展

    作者简介:李艳坤(1977—),女,河北省邯郸市人,华北电力大学副教授,博士,主要研究方向为化学计量学、环境分析化学。E-mail:liyankun_ncepu@foxmail.com
  • 1. 华北电力大学 环境科学与工程系, 河北 保定 071003;
  • 2. 华北电力大学 河北省燃煤电站烟气多污染物协同控制重点实验室, 河北 保定 071003
  • 收稿日期: 2023-11-15
  • 录用日期: 2024-01-18
  • 网络出版日期: 2024-10-15
基金项目:  国创计划资助项目(S202310079127)中央高校基本科研业务费项目(2017MS135)

摘要: 对基于光谱、质谱、色谱等检测技术的数据融合策略及其在食用油真实性鉴别中的研究及应用现状进行综述,指出:目前,广泛应用于食用油真实性鉴别的检测技术包括光谱、色谱、质谱、电子传感器等。然而,单一检测技术往往只关注某一特定的数据或指标,当食用油所含成分较复杂时,无法充分消除叠加效应、基线漂移、噪声等问题。数据融合策略分为数据层融合、特征层融合和决策层融合三类,结合化学计量学方法可以综合不同检测技术获取的数据,提取更丰富的数据特征信息,从而提高食用油真实性的鉴别效果。不同的新型检测技术之间,或将其与传统光谱、质谱、色谱等检测技术之间进行数据融合,可以快速、准确地实现食用油掺伪鉴别、品种分类和产地溯源,未来可就改进现有分析方法、结合深度学习算法开发新型融合算法、引入云计算提高食用油鉴别实时性等方面进行深入研究,以推动数据融合策略在食用油真实性鉴别领域的发展与创新。

English Abstract

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