Department of Food Technology, Faculty of Food Engineering, University of Campinas, Rua Monteiro Lobato, 80, CEP: 13083-862 Campinas, SP, Brazil.
Department of Food Technology, Faculty of Food Engineering, University of Campinas, Rua Monteiro Lobato, 80, CEP: 13083-862 Campinas, SP, Brazil.
Food Chem. 2015 Jul 15;179:35-43. doi: 10.1016/j.foodchem.2015.01.100. Epub 2015 Jan 28.
The stability of extra virgin olive oil in polyethylene terephthalate bottles and tinplate cans stored for 6 months under dark and light conditions was evaluated. The following analyses were carried out: free fatty acids, peroxide value, specific extinction at 232 and 270 nm, chlorophyll, L(∗)C(∗)h color, total phenolic compounds, tocopherols and squalene. The physicochemical changes were evaluated by artificial neural network (ANN) modeling with respect to light exposure conditions and packaging material. The optimized ANN structure consists of 11 input neurons, 18 hidden neurons and 5 output neurons using hyperbolic tangent and softmax activation functions in hidden and output layers, respectively. The five output neurons correspond to five possible classifications according to packaging material (PET amber, PET transparent and tinplate can) and light exposure (dark and light storage). The predicted physicochemical changes agreed very well with the experimental data showing high classification accuracy for test (>90%) and training set (>85). Sensitivity analysis showed that free fatty acid content, peroxide value, L(∗)Cab(∗)hab(∗) color parameters, tocopherol and chlorophyll contents were the physicochemical attributes with the most discriminative power.
研究了在避光和光照条件下储存在聚对苯二甲酸乙二醇酯瓶和马口铁罐中 6 个月的特级初榨橄榄油的稳定性。进行了以下分析：游离脂肪酸、过氧化物值、在 232nm 和 270nm 处的特定消光值、叶绿素、L(∗)C(∗)h 颜色、总酚类化合物、生育酚和角鲨烯。根据光照条件和包装材料，通过人工神经网络（ANN）模型评估了理化变化。优化的 ANN 结构由 11 个输入神经元、18 个隐藏神经元和 5 个输出神经元组成，在隐藏层和输出层中分别使用双曲正切和 softmax 激活函数。这 5 个输出神经元对应于根据包装材料（琥珀色 PET、透明 PET 和马口铁罐）和光照条件（避光和光照储存）的 5 种可能分类。预测的理化变化与实验数据非常吻合，表明测试集（>90%）和训练集（>85%）的分类准确率很高。敏感性分析表明，游离脂肪酸含量、过氧化物值、L(∗)Cab(∗)hab(∗)颜色参数、生育酚和叶绿素含量是具有最强判别力的理化属性。
