Conference Publication Details
Mandatory Fields
Singh, M;Domijan, K
2019 30TH IRISH SIGNALS AND SYSTEMS CONFERENCE (ISSC)
Comparison of Machine Learning Models in Food Authentication Studies
2019
January
Published
1
0 ()
Optional Fields
DISCRIMINANT-ANALYSIS CLASSIFICATION
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.
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