Quality parameters and volatile profiles were determined for Longwangmo apricot (Prunus armeniaca L.) kernel oil prepared by cold pressing, heat pressing, and refining of sun-dried and baked apricot kernels. Quality parameters determined included peroxide value, acid value, fatty acids, and UV absorbance. The tested oils showed absorbance values at 232 and 268 nm of 0.70-0.85 and 0.20-0.38, respectively; peroxide values were 2.09-5.62 mmol O 2 /kg and acid values were 0.36-1.40 mg KOH/g. The major fatty acid composition found in the oils was oleic acid (70.29-71.25%) followed by linoleic (22.31-23.00%), palmitic (4.57-4.87%), stearic acids (4.57-4.87%), palmitoleic acid (0.62-0.71%), and a-linolenic acid (0.15-0.18%). The numbers and concentrations of volatile compounds in the oils ranged from 19-52 to 6172-23871 mg/g, respectively. Nine compounds were present, in different amounts, in all apricot kernel oils studied: benzaldehyde, 2-methyl-propanal, 2-methyl-butyl aldehyde, furfural, nonanal, methylpyrazine, 2,5-dimethyl-pyrazine, methoxy pyrazine, and 3-ethyl-2,5-dimethyl-pyrazine. The results of our present investigation revealed that the oil-producing process had significant influence on the volatile profiles of apricot kernel oil. In particular, the bakedpretreatment process may enhance the aroma of apricot kernel oil by increasing the number and quantity of volatile compounds.Practical applications: Apricot kernel oil has recently received attention because of its health and nutritional properties, with favorable fatty acid composition and biologically active compounds. The results showed that Longwangmo apricot kernel oil is suitable as an edible food oil and the bakedpretreatment process with cold pressing may lead to an oil with enhanced flavor.

The determination of molecular species of oligosaccharides and glycoconjugates provided a new direction for the exploration of novel functions and uses of soybean lecithin powder. © 2018 Society of Chemical Industry.

Phytosterol glycosides (PGs), comprising both acylated steryl glycosides (ASGs) and steryl glycosides (SGs), are active ingredients with benefits for human use. Here, we aimed to optimize the silica‐gel adsorption technique for the extraction of PGs from soybean lecithin powder, which contains 5 to 10% of these glycolipids. Both response surface methodology (RSM) and artificial neural networks (ANNs) were applied to optimize the PG extraction parameters (X1 = silica‐gel dosage, X2 = adsorption temperature, and X3 = lecithin concentration) for high‐purity phospholipid and PG production, and their prediction and optimization accuracies were compared. Although both models fitted well with the experimental data, the ANN model demonstrated better accuracy for predicting and optimizing the conditions using four interrelated dependent variables (Y1 = phospholipid yield, Y2 = ASG recovery, Y3 = SG recovery, and Y4 = PG purity) and had a higher coefficient of determination and lower root mean square error and absolute average deviation. After digitally setting the percentages of the four dependent variables for phospholipid and PG production, the ANN‐optimized phospholipid product (Y1 = 88.07%, Y2 = 98.89%, Y3 = 100%, and Y4 = 49.03%) was acquired at X1 = 3.54 g/g, X2 = 26 °C, and X3 = 43 mg/mL, whereas the PG product (Y1 = 83.83%, Y2 = 97.64%, Y3 = 100%, and Y4 = 59.21%) was obtained at X1 = 2.00 g/g, X2 = 28.38 °C, and X3 = 41 mg/mL. In conclusion, the ANN method was better than RSM for the optimization of the silica‐gel adsorption technique for PG extraction from soybean lecithin powder.Practical ApplicationThis paper lays a theoretical foundation for the optimization of the industrial production of phytosterol glycosides and the comprehensive utilization of lecithin resources.