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    Comparison of Machine Learning Models in Food Authentication Studies


    Singh, Manokamna and Domijan, Katarina (2019) Comparison of Machine Learning Models in Food Authentication Studies. In: 2019 30th Irish Signals and Systems Conference (ISSC). IEEE. ISBN 9781728128009

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    Abstract

    The underlying objective of food authentication studies is to determine whether unknown food samples have been correctly labeled. In this paper, we study three near-infrared (NIR) spectroscopic datasets from food samples of different types: meat samples (labeled by species), olive oil samples (labeled by their geographic origin) and honey samples (labeled as pure or adulterated by different adulterants). We apply and compare a large number of classification, dimension reduction and variable selection approaches to these datasets. NIR data pose specific challenges to classification and variable selection: the datasets are high - dimensional where the number of cases (n) << number of features (p) and the recorded features are highly serially correlated. In this paper, we carry out a comparative analysis of different approaches and find that partial least squares, a classic tool employed for these types of data, outperforms all the other approaches considered.

    Item Type: Book Section
    Additional Information: Cite as: M. Singh and K. Domijan, "Comparison of Machine Learning Models in Food Authentication Studies," 2019 30th Irish Signals and Systems Conference (ISSC), 2019, pp. 1-6, doi: 10.1109/ISSC.2019.8904924.
    Keywords: Principal Component Analysis; PCA; Linear Discriminant Analysis; LDA; Quadratic Discriminant Analysis; QDA; Support Vector Machine; SVM; Marginal Relevance; MR; Feature Selection; Dimension Reduction; Random Forest; RF; Genetic Algorithm; GA; Functional Principal Component Analysis; FPCA; Logit Boost; LB; Bayesian Kernel Projection; Classifier; BKPC; Partial Least Squares; PLS; k-Nearest Neighbours; kNN;
    Academic Unit: Faculty of Science and Engineering > Mathematics and Statistics
    Faculty of Science and Engineering > Research Institutes > Hamilton Institute
    Item ID: 14363
    Identification Number: https://doi.org/10.1109/ISSC.2019.8904924
    Depositing User: Katarina Domijan
    Date Deposited: 21 Apr 2021 14:29
    Publisher: IEEE
    Refereed: Yes
    URI:

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