Campmajó Guillem, Cayero Laura, Saurina Javier, Núñez Oscar
Department of Chemical Engineering and Analytical Chemistry, University of Barcelona, Martí i Franquès 1-11, E08028 Barcelona, Spain.
Research Institute in Food Nutrition and Food Safety, University of Barcelona, Recinte Torribera, Av. Prat de la Riba 171, Edifici de Recerca (Gaudí), Santa Coloma de Gramenet, E08921 Barcelona, Spain.
Foods. 2019 Aug 1;8(8):310. doi: 10.3390/foods8080310.
Hen eggs are classified into four groups according to their production method: Organic, free-range, barn, or caged. It is known that a fraudulent practice is the misrepresentation of a high-quality egg with a lower one. In this work, high-performance liquid chromatography with ultraviolet detection (HPLC-UV) fingerprints were proposed as a source of potential chemical descriptors to achieve the classification of hen eggs according to their labelled type. A reversed-phase separation was optimized to obtain discriminant enough chromatographic fingerprints, which were subsequently processed by means of principal component analysis (PCA) and partial least squares-discriminant analysis (PLS-DA). Particular trends were observed for organic and caged hen eggs by PCA and, as expected, these groupings were improved by PLS-DA. The applicability of the method to distinguish egg manufacturer and size was also studied by PLS-DA, observing variations in the HPLC-UV fingerprints in both cases. Moreover, the classification of higher class eggs, in front of any other with one lower, and hence cheaper, was studied by building paired PLS-DA models, reaching a classification rate of at least 82.6% (100% for organic vs. non-organic hen eggs) and demonstrating the suitability of the proposed method.
根据生产方式,母鸡所产鸡蛋可分为四类:有机鸡蛋、散养鸡蛋、鸡舍饲养鸡蛋或笼养鸡蛋。众所周知,存在一种欺诈行为,即使用质量较低的鸡蛋冒充高质量鸡蛋。在这项研究中,提出了带有紫外检测的高效液相色谱法(HPLC-UV)指纹图谱作为潜在的化学描述符来源,以根据标签类型对母鸡所产鸡蛋进行分类。优化了反相分离以获得具有足够判别力的色谱指纹图谱,随后通过主成分分析(PCA)和偏最小二乘判别分析(PLS-DA)对其进行处理。通过PCA观察到有机鸡蛋和笼养鸡蛋有特定趋势,正如预期的那样,这些分组通过PLS-DA得到了改善。还通过PLS-DA研究了该方法区分鸡蛋制造商和鸡蛋大小的适用性,观察到在这两种情况下HPLC-UV指纹图谱都存在差异。此外,通过建立配对PLS-DA模型研究了将高级别鸡蛋与其他级别较低(因而价格较便宜)的鸡蛋区分开来的情况,分类准确率至少达到82.6%(有机鸡蛋与非有机鸡蛋的分类准确率为100%),证明了所提方法的适用性。