The invention discloses a quantitative adulteration detection method for
peanut oil based on multiple-source spectroscopic data fusion. The quantitative adulteration detection method comprises the following steps: oil
sample preparation; spectrum acquisition: respectively acquiring Raman spectrograms and near-
infrared spectrograms of all adulterated oil samples; spectroscopic data fusion: performing
data level fusion on the preprocessed Raman spectrograms and the preprocessed near-
infrared spectrograms to obtain a fusion
spectrogram; quantitative adulteration model establishment: extracting characteristic wavelengths of the fusion
spectrogram, and establishing a quantitative
peanut oil sample adulteration model through a multivariate quantitative calibration method; model
verification: analyzing samples to be detected. The detection method performs data fusion on the
edible oil spectrograms of two spectrums, has good complementarity, can reflect the inner characteristic information of
edible oil more comprehensively, and is quick, convenient, efficient, non-destructive, free from preprocessing, high in accuracy, and strong in applicability.