Identifying camellia oil adulteration with selected vegetable oils by characteristic near-infrared spectral regions

In this paper, a methodology based on characteristic spectral bands of near infrared spectroscopy (1000–2500nm) and multivariate analysis was proposed to identify camellia oil adulteration with vegetable oils. Sunflower, peanut and corn oils were selected to conduct the test. Pure camellia oil and that adulterated with varying concentrations (1–10% with the gradient of 1%, 10–40% with the gradient of 5%, 40–100% with the gradient of 10%) of each type of the three vegetable oils were prepared, respectively. For each type of adulterated oil, full-spectrum partial least squares partial least squares (PLS) models and synergy interval partial least squares (SI-PLS) models were developed. Parameters of these models were optimized simultaneously by cross-validation. The SI-PLS models were proved to be better than the full-spectrum PLS models. In SI-PLS models, the correlation coefficients of predition set (Rp) were 0.9992, 0.9998 and 0.9999 for adulteration with sunflower oil, peanut oil and corn oil seperately; the corresponding root mean square errors of prediction set (RMSEP) were 1.23, 0.66 and 0.37. Furthermore, a new generic PLS model was built based on the characteristic spectral regions selected from the intervals of the three SI-PLS models to identify the oil adulterants, regardless of the adultrated oil types. The model achieved with Rp= 0.9988 and RMSEP = 1.52. These results indicated that the characteristic near infrared spectral regions could determine the level of adulteration in the camellia oil