A methodology was developed to distinguish transgenic from non-transgenic soybean oils samples by using FT-MIR spectroscopy coupled with discrimination techniques, including Soft Independent Modeling of Class Analogies (SIMCA), Support Vector Machine-Discriminant Analysis (SVM-DA) and Partial Least SquaresDiscriminant Analysis (PLS-DA). The discrimination success rate of these three methods was compared, and different types of preprocessing were investigated. Based on the results, the best option was PLS-DA with a 100% rate of discrimination, independent of the preprocessing method used.
A methodology was developed for distinguishing different ultra-high temperature (UHT) milk adulterants (water, urea, and formaldehyde) at various levels using NIR spectroscopy (NIRS) coupled with supervision discrimination techniques (SIMCA, SVM-DA, and PLS-DA).
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