Departamento de Química Analítica, Facultad de Ciencia y Tecnología, Universidad del País Vasco/Euskal Herriko Unibertsitatea, Bilbao, Spain.
J Agric Food Chem. 2012 Apr 11;60(14):3635-44. doi: 10.1021/jf300022u. Epub 2012 Mar 29.
The data set composed by phenolic compound profiles of 83 Citrus juices (determined by HPLC-DAD-MS/MS) was evaluated by chemometrics to differentiate them according to Citrus species (sweet orange, tangerine, lemon, and grapefruit). Cluster analysis (CA) and principal component analysis (PCA) showed natural sample grouping among Citrus species and even the Citrus subclass. Most of the information contained in the full data set can be captured if only 15 phenolic compounds (concentration ≥10 mg/L), which can be quantified with fast and accurate methods in real samples, are introduced in the models; a good classification which allows the confirmation of the authenticity of juices is achieved by linear discriminant analysis. Using this reduced data set, fast and routine methods have been developed for predicting the percentage of grapefruit in adulterated sweet orange juices using principal component regression (PCR) and partial least-squares regression (PLS). The PLS model has provided suitable estimation errors.
该数据集由 83 种柑橘汁的酚类化合物图谱组成（通过 HPLC-DAD-MS/MS 测定），通过化学计量学进行评估，根据柑橘属（甜橙、橘子、柠檬和葡萄柚）对其进行区分。聚类分析（CA）和主成分分析（PCA）显示出柑橘属之间的自然样本分组，甚至柑橘子类之间也有分组。如果仅引入 15 种酚类化合物（浓度≥10mg/L），这些化合物可以通过快速准确的方法在实际样品中定量，那么可以从全数据集捕捉到大部分信息；线性判别分析实现了良好的分类，可以确认果汁的真实性。使用这个简化数据集，已经开发了快速常规的方法，使用主成分回归（PCR）和偏最小二乘回归（PLS）来预测掺杂甜橙汁中葡萄柚的百分比。PLS 模型提供了合适的估计误差。
