Department of Physics, University of Patras, 26504 Patras, Greece.
Institute of Chemical Engineering Sciences (ICE-HT), Foundation for Research and Technology-Hellas (FORTH), 26504 Patras, Greece.
Molecules. 2021 Feb 25;26(5):1241. doi: 10.3390/molecules26051241.
In the present work, the emission and the absorption spectra of numerous Greek olive oil samples and mixtures of them, obtained by two spectroscopic techniques, namely Laser-Induced Breakdown Spectroscopy (LIBS) and Absorption Spectroscopy, and aided by machine learning algorithms, were employed for the discrimination/classification of olive oils regarding their geographical origin. Both emission and absorption spectra were initially preprocessed by means of Principal Component Analysis (PCA) and were subsequently used for the construction of predictive models, employing Linear Discriminant Analysis (LDA) and Support Vector Machines (SVM). All data analysis methodologies were validated by both "k-fold" cross-validation and external validation methods. In all cases, very high classification accuracies were found, up to 100%. The present results demonstrate the advantages of machine learning implementation for improving the capabilities of these spectroscopic techniques as tools for efficient olive oil quality monitoring and control.
在本工作中，采用激光诱导击穿光谱（LIBS）和吸收光谱两种光谱技术，以及机器学习算法，获得了大量希腊橄榄油样品及其混合物的发射光谱和吸收光谱，并利用这些光谱对橄榄油的地理来源进行了鉴别/分类。发射光谱和吸收光谱最初都经过主成分分析（PCA）预处理，然后用于构建预测模型，采用线性判别分析（LDA）和支持向量机（SVM）。所有数据分析方法均通过“k 折”交叉验证和外部验证方法进行验证。在所有情况下，均发现了非常高的分类准确率，高达 100%。本结果表明，机器学习的实施为提高这些光谱技术作为有效橄榄油质量监测和控制工具的性能提供了优势。
