Lia Frederick, Formosa Jean Paul, Zammit-Mangion Marion, Farrugia Claude
Department of Chemistry, University of Malta, 2080 Msida MSD, Malta.
Department of Physiology and Biochemistry, University of Malta, 2080 Msida MSD, Malta.
Foods. 2020 Apr 15;9(4):498. doi: 10.3390/foods9040498.
The potential application of multivariate three-way data analysis techniques, namely parallel factor analysis (PARAFAC) and discriminant multi-way partial least squares regression (DN-PLSR), on three-dimensional excitation emission matrix (3D-EEM) fluorescent data were used to identify the uniqueness and authenticity of Maltese extra virgin olive oil (EVOO). A non-negativity constrained PARAFAC model revealed that a four-component model provided the most appropriate solution. Examination of the extracted components in mode 2 and 3 showed that these belonged to different fluorophores present in extra virgin olive oil. Application of linear discriminate analysis (LDA) and binary logistic regression analysis on the concentration of the four extracted fluorophores, showed that it is possible to discriminate Maltese EVOOs from non-Maltese EVOOs. The application of DN-PLSR provided superior means for discrimination of Maltese EVOOs. Further inspection of the extracted latent variables and their variable importance plots (VIPs) provided strong proof of the existence of four types of fluorophores present in EVOOs and their potential application for the discrimination of Maltese EVOOs.
将多元三向数据分析技术，即平行因子分析（PARAFAC）和判别式多向偏最小二乘回归（DN-PLSR），应用于三维激发发射矩阵（3D-EEM）荧光数据，以鉴定马耳他特级初榨橄榄油（EVOO）的独特性和真实性。一个非负约束PARAFAC模型表明，四组分模型提供了最合适的解决方案。对模式2和模式3中提取的组分进行检查表明，这些组分属于特级初榨橄榄油中存在的不同荧光团。对四种提取荧光团的浓度进行线性判别分析（LDA）和二元逻辑回归分析表明，有可能区分马耳他EVOO和非马耳他EVOO。DN-PLSR的应用为区分马耳他EVOO提供了更好的方法。对提取的潜在变量及其变量重要性图（VIP）的进一步检查有力地证明了EVOO中存在四种荧光团类型及其在区分马耳他EVOO方面的潜在应用。
