提取时间条件对橄榄糊酚类物质含量影响的研究。利用实验室研制的电位电子舌进行检测和鉴别

Ítala M. G. Marx, Nuno F. Rodrigues, A. C. Veloso, J. Pereira, A. M. Peres
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引用次数: 0

摘要

本研究考察了不同挤压时间(Mt)(0、15、30、45和60 min)在工业提取过程中的影响。阿贝奎纳油在25°C时对橄榄糊总酚含量的影响。此外,还评估了应用实验室制造的电位测量电子舌(e -舌)的可能性,该电子舌由40个具有交叉灵敏度的脂质/聚合物传感器膜组成,根据Mt区分橄榄糊。结果表明,随着Mt的增加,橄榄糊总酚含量显著降低(p值< 0.001,单因素方差分析)(从2.21±0.02 g没食子酸当量/kg降至1.99±0.03 g没食子酸当量/kg),呈线性下降趋势(R-Pearson = - 0.910)。这些发现可能暂时归因于在软化过程中酚类化合物从橄榄糊迁移到提取的油和水相。最后,在分析橄榄酱的甲醇提取物(甲醇:水,80:20 v/v)过程中获得的e舌信号,以及线性判别分析(LDA)和模拟退火(SA)算法,使我们能够建立一个成功的分类模型。E-tongue-LDA-SA模型基于11个选定的非冗余传感器,使我们能够根据Mt(训练和留一交叉验证的灵敏度为100%)正确区分所有研究的橄榄酱。脂质/聚合物传感器膜通过静电相互作用和/或氢键与酚类化合物相互作用的能力初步解释了电子舌令人满意的性能,其总含量取决于Mt。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Evaluation of the Effect of Extracted Time Conditions on the Phenolic Content of Olive Pastes from cv. Arbequina and Discrimination Using a Lab-Made Potentiometric Electronic Tongue
The present study investigated the effect of malaxation times (Mt) (0, 15, 30, 45 and 60 min), during the industrial extraction of cv. Arbequina oils at 25 °C on total phenolic content of olive pastes. Additionally, the possibility of applying a lab-made potentiometric electronic tongue (E-tongue), comprising 40 lipid/polymer sensor membranes with cross sensitivity, to discriminate the olive pastes according to the Mt, was evaluated. The results pointed out that the olive pastes’ total phenolic contents significantly decreased (p-value < 0.001, one-way ANOVA) with the increase of the Mt (from 2.21 ± 0.02 to 1.99 ± 0.03 g gallic acid equivalents/kg olive paste), there being observed a linear decreasing trend (R-Pearson = −0.910). These findings may be tentatively attributed to the migration of the phenolic compounds from the olive pastes to the extracted oil and water phases, during the malaxation process. Finally, the E-tongue signals, acquired during the analysis of the olive pastes’ methanolic extracts (methanol:water, 80:20 v/v), together with a linear discriminant analysis (LDA), coupled with a simulated annealing (SA) algorithm, allowed us to establish a successful classification model. The E-tongue-LDA-SA model, based on 11 selected non-redundant sensors, allowed us to correctly discriminate all the studied olive pastes according to the Mt (sensitivities of 100% for training and leave-one-out cross-validation). The satisfactory performance of the E-tongue could be tentatively explained by the known capability of lipid/polymeric sensor membranes to interact with phenolic compounds, through electrostatic interactions and/or hydrogen bonds, which total content depended on the Mt.
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