Departamento de Química Analítica, Nutrición y Bromatología, Facultad de Ciencias Químicas, C/Plaza de la Merced s/n, 37008 Salamanca, Spain.
Talanta. 2013 Nov 15;116:50-5. doi: 10.1016/j.talanta.2013.04.043. Epub 2013 May 3.
The present study addresses the prediction of the time of ripening and type of mixtures of milk (cow's, ewe's and goat's) in cheeses of varying composition using artificial neural networks (ANN). To accomplish this aim, neural networks were designed using as input data the content of 19 fatty acids obtained with GC-FID of the cheese fat and scores obtained from principal component analysis (PCA) of NIR spectra. The best model of neuronal networks for the identification of the type of mixtures of milk was obtained using the information concerning the fatty acid concentration (80% of correct results in the training phase and 75% in the validation phase). Regarding the information of the near-infrared (NIR) spectra a neural network was designed. The aforesaid neural network predicted the ripening of cheeses with 100% accuracy in both training and in validation.
本研究旨在使用人工神经网络（ANN）预测不同组成的奶酪中牛奶（牛、羊和山羊）的成熟时间和混合物类型。为了实现这一目标，使用 GC-FID 获得的奶酪脂肪中 19 种脂肪酸含量和近红外（NIR）光谱主成分分析（PCA）获得的得分作为输入数据设计神经网络。使用有关脂肪酸浓度的信息（在训练阶段和验证阶段分别有 80%和 75%的正确结果），获得了用于识别牛奶混合物类型的最佳神经元网络模型。关于近红外（NIR）光谱的信息，设计了一个神经网络。上述神经网络在训练和验证阶段均能以 100%的准确率预测奶酪的成熟度。
