Rapid detection and separation of olive oil and Camellia oil based on ion mobility spectrometry fingerprints and chemometric models

A simple and rapid classification model for olive and Camellia oil was proposed based on ion mobility spectrometry (IMS) fingerprints and chemometric model (peak detection and random forest algorithm). Results indicated that IMS fingerprint spectra by second‐derivative algorithm could completely separate 64 olive oil and 79 Camellia oil samples used in this study by simply calculating the peak area. Random forest algorithm was employed to establish discriminant model for olive oil adulterated by Camellia oil. Simulated adulteration detection showed that the accuracy rate of discriminant model is 96.4% as two of 55 samples were identified as blending olive oil. All these results suggested that IMS could be an effective method to detect the adulterated olive oils by Camellia oil. Practical applications: Camellia oil is much similar to olive oil no matter in the physicochemical properties and fatty acid profiles. Thereby, olive oil has been one of the most frequent targets for the adulteration by Camellia oil. This study aimed to provide a rapid method to detect and separate olive oil and Camellia oil by a portable IMS device, by using fingerprints spectra, peak detection (first‐ and second‐derivative algorithm), and random forest algorithm. Results indicated that the classification and discriminant model established in this work was doable for the adulteration detection in the industry. Based on fingerprints spectra, peak detection (first‐ and second‐derivative algorithm) and random forest algorithm, IMS detection is doable for the rapid detection and separation of olive and Camellia oil.