Yuan Libo, Meng Xiangru, Xin Kehui, Ju Ying, Zhang Yan, Yin Chunling, Hu Leqian
School of Chemistry and Chemical Engineering, Henan University of Technology, Zhengzhou 450001, China.
School of Chemistry and Chemical Engineering, Henan University of Technology, Zhengzhou 450001, China.
Spectrochim Acta A Mol Biomol Spectrosc. 2023 Mar 5;288:122120. doi: 10.1016/j.saa.2022.122120. Epub 2022 Nov 17.
Driven by economic benefits like any other foods, vegetable oil has long been plagued by mislabeling and adulteration. Many studies have addressed the field of classification and identification of vegetable oils by various analysis techniques, especially spectral analysis. A comparative study was performed using Fourier transform infrared spectroscopy (FTIR), visible near-infrared spectroscopy (Vis-NIR) and excitation-emission matrix fluorescence spectroscopy (EEMs) combined with chemometrics to distinguish different types of edible vegetable oils. FTIR, Vis-NIR and EEMs datasets of 147 samples of five vegetable oils from different brands were analyzed. Two types of pattern recognition methods, principal component analysis (PCA)/multi-way principal component analysis (M-PCA) and partial least squares discriminant analysis (PLS-DA)/multilinear partial least squares discriminant analysis (N-PLS-DA), were used to resolve these data and distinguish vegetable oil types, respectively. PCA/M-PCA analysis exhibited that three spectral data of five vegetable oils showed a clustering trend. The total correct recognition rate of the training set and prediction set of FTIR spectra of vegetable oil based on PLS-DA method are 100%. The total recognition rate of Vis-NIR based on PLS-DA are 100% and 97.96%. However, the total correct recognition rate of training set and prediction set of EEMs data based on N-PLS-DA method is 69.39% and 75.51%, respectively. The comparative study showed that FTIR and Vis-NIR combined with chemometrics were more suitable for vegetable oil species identification than EEMs technique. The reason may be concluded that almost all chemical components in vegetable oil can produce FTIR and NIR absorption, while only a small amount of fluorophores can produce fluorescence. That is, FTIR and NIR can provide more spectral information than EEMs. Analysis of EEMs data using self-weighted alternating trilinear decomposition (SWATLD) also showed that fluorophores were a few and irregularly distributed in vegetable oils.
与其他食品一样,受经济利益驱使,植物油长期以来一直存在标签错误和掺假问题。许多研究通过各种分析技术,特别是光谱分析,来研究植物油的分类和鉴定领域。进行了一项比较研究,使用傅里叶变换红外光谱(FTIR)、可见近红外光谱(Vis-NIR)和激发-发射矩阵荧光光谱(EEMs)并结合化学计量学来区分不同类型的食用植物油。分析了来自不同品牌的五种植物油的147个样品的FTIR、Vis-NIR和EEMs数据集。分别使用两种模式识别方法,主成分分析(PCA)/多向主成分分析(M-PCA)和偏最小二乘判别分析(PLS-DA)/多线性偏最小二乘判别分析(N-PLS-DA)来处理这些数据并区分植物油类型。PCA/M-PCA分析表明,五种植物油的三种光谱数据呈现出聚类趋势。基于PLS-DA方法的植物油FTIR光谱训练集和预测集的总正确识别率均为100%。基于PLS-DA的Vis-NIR总识别率分别为100%和97.96%。然而,基于N-PLS-DA方法的EEMs数据训练集和预测集的总正确识别率分别为69.39%和75.51%。比较研究表明,FTIR和Vis-NIR结合化学计量学比EEMs技术更适合植物油种类鉴定。原因可能是植物油中几乎所有化学成分都能产生FTIR和近红外吸收,而只有少量荧光团能产生荧光。也就是说,FTIR和近红外能比EEMs提供更多光谱信息。使用自加权交替三线性分解(SWATLD)对EEMs数据进行分析也表明,荧光团在植物油中数量较少且分布不规则。