A novel method for detection of camellia oil adulteration based on time-resolved emission fluorescence

Flavor classification and year prediction of Chinese Baijiu by time-resolved fluorescence

Baijiu is a traditional and popular Chinese liquor with enormous sale potential, which is affected by factors such as flavor and storage time. Chinese Baijiu is a complex and transparent mixture that makes analyzing difficult. The utility of time-resolved fluorescence helped to develop a new method to analyze Baijiu. Forty-two Baijiu samples among six brands with three flavors were prepared, and their fluorescence spectra were analyzed with an excitation light of 374.2 nm. Hexanoic acid and ethyl butyrate were found to have an impact on Baijiu fluorescence. The properties of lifetimes in Baijiu were investigated, and its mechanism was studied by calculations through density function theory. Using parameters of fluorescence lifetimes, Baijiu samples were classified according to their flavors. Additionally, the correlations between fluorescence lifetimes and storage time of Baijiu in Luzhou flavor were obtained, leading to a reliable and efficient method to establish the year forecast model of Chinese Baijiu with a mean error of 2.79 months. It also provides an important reference of the utility of time-resolved fluorescence for quantitative research of multi-component systems.

Detection of camellia oil adulteration using chemometrics based on fatty acids GC fingerprints and phytosterols GC-MS fingerprints

The fatty acid, squalene, and phytosterols, coupled to chemometrics were utilized to detect the adulteration of camellia oil (CAO) with palm superolein (PAO), refined olive oil (ROO), high oleic- sunflower oil (HO-SUO), sunflower oil (SUO), corn oil (COO), rice bran oil (RBO), rice oil (RIO), peanut oil (PEO), sesame oil (SEO), soybean oil (SOO), and rapeseed oil (RAO). CAO was characterized with higher triterpene alcohols, thus differentiated from other vegetable oils in principle component analysis (PCA). Using partial least squares-discriminant analysis (PLS-DA), CAO adulterated with PAO, ROO, HO-SUO, SUO, COO, RBO, RIO, PEO, SEO, SOO, RAO (5%-100%, w/w), could be classified, especially higher than 92.31% of the total discrimination accuracy, at an adulterated ratio above 30%. With less than 22 potential key markers selected by the variable importance in projection (VIP), the optimized PLS models were confirmed to be accurate for the adulterated level prediction in CAO.