Identification of peanut oil origins based on Raman spectroscopy combined with multivariate data analysis methods

Abstract

This study aimed to use Raman spectroscopy to identify the producing areas of peanut oil and build a robust discriminant model to further screen out the characteristic spectra closely related to the origin. Raman spectra of 159 peanut oil samples from different provinces and different cities of the same province were collected. The obtained data were analyzed by stepwise linear discriminant analysis (SLDA), k-nearest neighbor analysis (k-NN), support vector machine (SVM) and multi-way analysis of variance. The results showed that the overall recognition rate of samples based on full spectra was higher than 90%. The producing origin, variety and their interaction influenced Raman spectra of peanut oil significantly, and 1 400–1 500 cm−1 and 1 600–1 700 cm−1 were selected as the characteristic spectra of origin and less affected by variety. The best classification model established by SLDA combined with characteristic spectra could rapidly and accurately identify peanut oil's origin.

Keywords

• Raman spectroscopy
• peanut oil
• large-scale district
• small-scale district
• variety
• chemometrics