Application of random forests to select premium quality vegetable oils by their fatty acid composition.
In order to discriminate premium quality from inexpensive edible oils, the fatty acid profiles of tea, rapeseed, corn, sunflower and sesame oil were compared with the ones from extra virgin olive oil (EVOO). Fatty acid methyl esters were quantified by GC/MS. Principal component analysis (PCA) and random forests (RF) were applied to cluster the samples. RF showed a better ability of discrimination and also revealed the contribution of each variable to the clustering model. The multidimensional scaling (MDS) plot of the RF proximity matrix demonstrated that tea oil was similar to EVOO. Meanwhile, it was observed that the total content of cis-monounsaturated fatty acids (79.48%) in tea oil was close to EVOO (80.71%), especially the oleic acid (77.38% and 77.45%, respectively). The results suggest that tea oil might be a good edible oil choice, considering the high oleic acid content and similar fatty acid profiles compared to those of EVOO.
Research Center of Modernization of Chinese Medicines, College of Chemistry and Chemical Engineering, Central South University, Changsha 410083, China.
Fang-Fang
FF
Ai
Jun
J
Bin
Zhi-min
ZM
Zhang
Jian-hua
JH
Huang
Jian-bing
JB
Wang
Yi-zeng
YZ
Liang
Ling
L
Yu
Zhen-yu
ZY
Yang
D000465:Algorithms; D002849:Chromatography, Gas; D003205:Computing Methodologies; D005227:Fatty Acids / Q000737:chemistry*; D010938:Plant Oils / Q000737:chemistry*; D025341:Principal Component Analysis; D011786:Quality Control
D005227:Fatty Acids; D010938:Plant Oils
Fatty acids; GC/MS; Random forests; Vegetable oils