Marini Federico, Magrì Antonio L, Bucci Remo, Magrì Andrea D
Dipartimento di Chimica, Università di Roma La Sapienza, P.le Aldo Moro 5, 00185 Rome, Italy.
Anal Chim Acta. 2007 Sep 19;599(2):232-40. doi: 10.1016/j.aca.2007.08.006. Epub 2007 Aug 6.
The problem of authenticating extra virgin olive oil varieties is particularly important from the standpoint of quality control. After having shown in our previous works the possibility of discriminating oils from a single variety using chemometrics, in this study a combination of two different neural networks architectures was employed for the resolution of simulated binary blends of oils from different cultivars. In particular, a Kohonen self-organizing map was used to select the samples to include in the training, test and validation sets, needed to operate the successive calibration stage, which has been carried out by means of several multilayer feed-forward neural networks. The optimal model resulted in a validation Q2 in the range 0.91-0.96 (10 data sets), corresponding to an average prediction error of about 5-7.5%, which appeared significantly better than in the case of random or Kennard-Stone selection.
从质量控制的角度来看，鉴别特级初榨橄榄油品种的问题尤为重要。在我们之前的研究中已经表明，使用化学计量学可以区分单一品种的橄榄油，在本研究中，采用了两种不同神经网络架构的组合来解析来自不同品种的橄榄油模拟二元混合物。具体而言，使用了一个Kohonen自组织映射来选择样本，以纳入连续校准阶段所需的训练集、测试集和验证集，该校准阶段是通过几个多层前馈神经网络进行的。最优模型的验证Q2在0.91 - 0.96范围内（10个数据集），对应的平均预测误差约为5 - 7.5%，这明显优于随机选择或Kennard - Stone选择的情况。
