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• W2512708623 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." @default.
• W2512708623 created "2016-09-16" @default.
• W2512708623 creator A5014600847 @default.
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• W2512708623 date "2017-02-01" @default.
• W2512708623 modified "2023-10-10" @default.
• W2512708623 title "Continuous statistical modelling for rapid detection of adulteration of extra virgin olive oil using mid infrared and Raman spectroscopic data" @default.
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• W2512708623 doi "https://doi.org/10.1016/j.foodchem.2016.09.011" @default.
• W2512708623 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/27664692" @default.
• W2512708623 hasPublicationYear "2017" @default.
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