Classification of olive oils using high throughput flow ¹H NMR fingerprinting with principal component analysis, linear discriminant analysis and probabilistic neural networks

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• 作者：Serge Rezzi ; David E. Axelson ; K&#xe1 ; roly H&#xe9 ; berger ; Fabiano Reniero ; Carlo Mariani and Claude Guillou
• 关键词：NMR ; Authenticity ; Multivariate methods ; Linear discriminant analysis ; Principal component analysis ; Chemometrics ; Artificial neural networks
• 刊名：Analytica Chimica Acta
• 出版年：2005
• 期刊代码：2_00032670
• 类别：ch
• 出版时间：2005
• 卷：552
• 期：1-2
• 页码：13-24
• 文件大小：536 K

摘要

The combination of ¹H NMR fingerprinting with multivariate analysis provides an original approach to study the profile of olive oil in relation to its geographical origin and processing. The present work aims at illustrating the relevance of ¹H NMR fingerprints for assessing the geographical origin and the year of production for olive oils from various Mediterranean areas. Multivariate (chemometric) techniques are able to filter out the most relevant information from a spectrum, e.g. for a classification. Principal component analysis (PCA) was carried out on the http://www.sciencedirect.com/scidirimg/entities/223c.gif" alt="not, vert, similar" border=0>12,000 variables (chemical shifts) and four data sets were defined prior to PCA. Linear discriminant analysis (LDA) of the first 50 PC's was applied for classification of olive oil samples (97 or 91) according to the geographic origin and year of production. The data analysis has been carried out with and without outliers, as well. Variable selection for LDA was achieved using: (i) the best five variables and (ii) an interactive forward stepwise manner. Using LDA on the external validation sets the correct classification varied between 47 and 75%(random selection), and between 35 and 92%(Kennard–Stone selection (KS)) depending on geographic origin (country) and production years. A similar success rate could be achieved using partial least squares discriminant analysis (PLS DA). The success rate can be considerably improved by using probabilistic neural networks (PNN). Correct classification by PNN varied between 58 and 100%on the external validation sets. Other chemometric techniques, such as multiple linear regression, or generalized pair-wise correlation, did not give better results.