Continuous statistical modelling for rapid detection of adulteration of extra virgin olive oil using mid infrared and Raman spectroscopic data. (15th February 2017)

• Record Type: Journal Article
• Authors: Georgouli, Konstantia; Martinez Del Rincon, Jesus; Koidis, Anastasios
• Is Part Of: Food chemistry. Volume 217(2017)
• Page Start: 735
• Page End: 742
• Publication Date: 2017-02-15
• Subjects: Continuous statistical modelling -- Dimensionality reduction -- Rapid detection -- Adulteration -- Extra virgin olive oil -- FT-IR -- RAMAN -- Spectroscopy; Food -- Analysis -- Periodicals; Food -- Composition -- Periodicals; 664
• DOI: 10.1016/j.foodchem.2016.09.011
• Languages: English
• ISSNs: 0308-8146

Abstract

Highlights: A novel dimensionality reduction pattern recognition technique is proposed. Continuous Locality Preserving Projections is based on continuous statistical modelling. The technique is showcased in an oil adulteration problem using FTIR/Raman data. Our approach proved better in classification rate than the state-of-the-art methods. Results prove the potential of this novel technique to be used for screening purposes. Abstract: The main objective of this work was to develop a novel dimensionality reduction technique as a part of an integrated pattern recognition solution capable of identifying adulterants such as hazelnut oil in extra virgin olive oil at low percentages based on spectroscopic chemical fingerprints. A novel Continuous Locality Preserving Projections (CLPP) technique is proposed which allows the modelling of the continuous nature of the produced in-house admixtures as data series instead of discrete points. The maintenance of the continuous structure of the data manifold enables the better visualisation of this examined classification problem and facilitates the more accurate utilisation of the manifold for detecting the adulterants. The performance of the proposed technique is validated with two different spectroscopic techniques (Raman and Fourier transform infrared, FT-IR). In all cases studied, CLPP accompanied by k-Nearest Neighbors (kNN) algorithm was found to outperform any other state-of-the-art pattern recognition techniques.