Lujun Zhang, Nuo Cai, Xiaodong Huang, Xinmin Fan, Juanjuan Gao, Jin Gao, Sensen Li, Yan Wang, Chunyan Wang
Department of Physics and Electronic Information, Weifang University, Weifang, 261061, China.
Science and Technology on Electro-Optical Information Security Control Laboratory, Tianjin, 300308, China.
J Fluoresc. 2025 Mar;35(3):1819-1832. doi: 10.1007/s10895-024-03613-z. Epub 2024 Mar 8.
This research investigates the use of excitation-emission matrix fluorescence (EEMF) in conjunction with chemometric models to rapidly identify and quantify adulteration in olive oil, a critical concern where sample availability is limited. Adulteration is simulated by blending soybean, peanut, and linseed oils into olive oil, creating diverse adulterated samples. Principal component analysis (PCA) was applied to the EEMF spectral data as an initial exploratory measure to cluster and differentiate adulterated samples. Spatial clustering enabled vivid visualization of the variations and trends in the spectra. The novel application of parallel factor analysis (PARAFAC) for data decomposition in this paper focuses on unraveling correlations between the decomposed components and the actual adulterated components, which offers a novel perspective for accurately quantifying adulteration levels. Additionally, a comparative analysis was conducted between the PCA and PARAFAC methodologies. Our study not only unveils a new avenue for the quantitative analysis of adulterants in olive oil through spectral detection but also highlights the potential for applying these insights in practical, real-world scenarios, thereby enhancing detection capabilities for various edible oil samples. This promises to improve the detection of adulteration across a range of edible oil samples, offering significant contributions to food safety and quality assurance.
本研究探讨了激发-发射矩阵荧光（EEMF）结合化学计量模型在快速识别和量化橄榄油掺假方面的应用，在样品可用性有限的情况下，这是一个关键问题。通过将大豆油、花生油和亚麻籽油混入橄榄油中来模拟掺假，从而创建各种掺假样品。主成分分析（PCA）被应用于EEMF光谱数据，作为一种初步的探索性措施，用于对掺假样品进行聚类和区分。空间聚类能够生动地可视化光谱中的变化和趋势。本文中平行因子分析（PARAFAC）在数据分解方面的新应用侧重于揭示分解成分与实际掺假成分之间的相关性，这为准确量化掺假水平提供了新的视角。此外，还对PCA和PARAFAC方法进行了比较分析。我们的研究不仅揭示了通过光谱检测对橄榄油中掺假物进行定量分析的新途径，还强调了将这些见解应用于实际现实场景的潜力，从而提高对各种食用油样品的检测能力。这有望改善对一系列食用油样品掺假的检测，为食品安全和质量保证做出重大贡献。
