School of Food Science and Engineering, Hefei University of Technology, Hefei 230009, China; Intelligent Control and Compute Vision Lab, Hefei University, Hefei 230601, China.
School of Food Science and Engineering, Hefei University of Technology, Hefei 230009, China.
Food Chem. 2018 Jun 15;251:86-92. doi: 10.1016/j.foodchem.2018.01.081. Epub 2018 Jan 12.
Discrimination of geographical origin of extra-virgin olive oils (EVOOs) is of great importance for legislation and consumers worldwide. The feasibility of a rapid discrimination of four different geographical origins of EVOOs with terahertz spectroscopy system was examined. Different chemometrics including least squares-support vector machines (LS-SVM), back propagation neural network (BPNN) and random forest (RF) combined with principal component analysis (PCA), genetic algorithm (GA) were compared to obtain the best discrimination model. The results demonstrated that there were apparent differences among the four different geographical origins of EVOOs in fatty acid compositions and the absorbance spectra, and an excellent classification (accuracy was 96.25% in prediction set) could be achieved using the LS-SVM method combine with GA. It can be concluded that THz spectroscopy together with chemometrics would be a promising technique to rapid discriminate the geographical origin of EVOOs with high efficiency.
鉴别特级初榨橄榄油（EVOO）的产地对于全球立法和消费者来说都非常重要。本研究采用太赫兹光谱系统，对快速鉴别四种不同产地的特级初榨橄榄油的可行性进行了检验。分别采用偏最小二乘支持向量机（LS-SVM）、反向传播神经网络（BPNN）和随机森林（RF）结合主成分分析（PCA）、遗传算法（GA）等不同化学计量学方法进行比较，以获得最佳的判别模型。结果表明，四种不同产地的特级初榨橄榄油在脂肪酸组成和吸光度谱上存在明显差异，采用 LS-SVM 结合 GA 的方法可以实现极好的分类（预测集准确率为 96.25%）。可以得出结论，太赫兹光谱结合化学计量学是一种快速、高效鉴别特级初榨橄榄油产地的有前途的技术。
