Department of Chemistry, Faculty of Chemistry and Chemical Engineering, Babeş-Bolyai University, Arany Janos Str., No. 11, RO-400028 Cluj-Napoca, Romania.
Department of Chemistry, Faculty of Chemistry and Chemical Engineering, Babeş-Bolyai University, Arany Janos Str., No. 11, RO-400028 Cluj-Napoca, Romania.
Spectrochim Acta A Mol Biomol Spectrosc. 2019 Apr 15;213:204-209. doi: 10.1016/j.saa.2019.01.065. Epub 2019 Jan 22.
A comprehensive study concerning the characterization and classification of 30 cold-pressed edible oils according to their UV-Vis spectra and radical scavenging profiles using the 2,2-diphenyl-1-picrylhydrazyl (DPPH) assay is presented. Considering the principal component analysis (PCA) and fuzzy-principal component analysis (FPCA) loadings profiles, the characteristic spectral regions with a significant influence in oil samples classification were identified and associated with characteristic factors in each group. Much more, the oils with high antiradical capacity were revealed. The scores corresponding to the first principal component and the canonical scores corresponding to the first discriminant function derived from radical scavenging spectral profiles allowed a relevant classification of oils in well-defined groups associated with their high, medium and low radical scavenging capacity. The FPCA-LDA method applied on DPPH radical scavenging spectral profiles of edible oils appeared to be the most efficient method with a correct classification rate of 96.7%.
本文全面研究了 30 种冷榨食用油的特征和分类，根据其紫外-可见光谱和自由基清除特性，使用 2,2-二苯基-1-苦基肼（DPPH）法进行分析。考虑到主成分分析（PCA）和模糊主成分分析（FPCA）的加载谱，确定了对油样分类有显著影响的特征光谱区域，并将其与每个组的特征因子相关联。此外，还揭示了具有高抗氧化能力的油。与自由基清除光谱特征相关的第一主成分得分和第一判别函数的典型得分允许对油进行相关分类，分为高、中、低自由基清除能力的明确组。应用于食用油 DPPH 自由基清除光谱的 FPCA-LDA 方法似乎是最有效的方法，其正确分类率为 96.7%。
