Abstract: This study aimed to establish a new method for quick detection of camellia oil purity. Adulterated camellia oil samples were detected by gas chromatography-ion mobility spectrometry (GC-IMS) and the two-dimensional spectral data obtained were analyzed by multi-way principal component analysis (MPCA) and partial least squares (PLS) regression. Mixtures of camellia oil with three other vegetable oils at different concentrations respectively were analyzed by GC-IMS. MPCA was used to reduce the dimensionality of the data and extract a score matrix from the three-dimensional matrix for principal component analysis (PCA), and quantitative predictive models for adulterated oils were established by PLS analysis of the score matrix. The experimental results showed that pure and adulterated samples were clearly distinguished in the PCA plots with MPCA processing and that each adulteration ratio exhibited an obvious region in the plots. PLS regression analysis of the scores matrix extracted by MPCA enabled accurate quantitative analysis of camellia oil adulteration levels. The presented method proved to be a fast, accurate and non-destructive method for the detection of camellia oil adulteration. This method can also be applied to data analysis in other combined instruments, and has a great potential for applications in the quality control and evaluation of edible oils.

Key words: adulterated camellia oil, purity, gas chromatography-ion mobility spectrometry (GC-IMS), multi-way principal component analysis (MPCA), partial least squares (PLS)