Partial Least Squares Discriminant Analysis Model Based on Variable Selection Applied to Identify the Adulterated Olive Oil
作者： Xinhui Li , Sulan Wang , Weimin Shi , Qi Shen
DOI: 10.1007/S12161-015-0355-8
关键词: Mathematics 、 Artificial intelligence 、 Pattern recognition 、 Linear discriminant analysis 、 Set (abstract data type) 、 Feature selection 、 Partial least squares regression 、 Monte Carlo method 、 Variable elimination 、 Cross-validation 、 Olive oil 、 Analytical chemistry
摘要: The identification of the authenticity edible vegetable oils is important from both consumer health and commercial aspect. Fourier transform infrared spectroscopy combined with multivariate statistical analysis methods was used to identify olive oils. Partial least squares discriminant (PLS-DA) based on a reduced subset variables employed build classification models. For purpose variable selection, modified Monte Carlo uninformative elimination (MC-UVE) technique proposed. Comparing other selection techniques, PLS-DA model using selected by MC-UVE provided better results. accuracy obtained cross validation 97.6 %, correct rate prediction set 100 %. results show that successful in inspection
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