Cheng Ding

Bio: Cheng Ding is an academic researcher from Hubei University of Technology. The author has contributed to research in topics: Adipocyte & Pungency. The author has an hindex of 3, co-authored 3 publications receiving 14 citations.

Genetic Algorithm–Artificial Neural Network Modeling of Capsaicin and Capsorubin Content of Chinese Chili Oil

Abstract: Chili oil, which contains large amounts of capsaicin and capsorubin, is one of the most consumed seasonings in China. These compounds significantly affect the quality, antioxidant activity, pungency, and color of chili oil. This study aimed to investigate the effect of stewing temperature, stewing time, and amount of oil on the capsaicin and capsorubin contents of Chinese chili oil. The partial least squares (PLS) regression and genetic algorithm–artificial neural network models were established and used to predict capsaicin and capsorubin contents. The genetic algorithm was applied to optimize the parameters of the network. The developed genetic algorithm–artificial neural network, which included ten hidden neurons, predicted capsaicin and capsorubin contents with correlation coefficients of 0.995 and 0.986, respectively. The neural network exhibited more accurate prediction and practicability compared with the PLS regression model.

Fermented crystal allium chinense and preparation method thereof

Abstract: The invention discloses fermented crystal allium chinense and a preparation method thereof. The preparation method is mainly characterized in that a certain proportion of mixedlactic acid bacteria are used for fermenting processed allium chinense, and a product is obtained after seasoning, packaging and sterilizing. Quantitative mixed viable bacteria such as lactobacillus plantarum, lactococcuslact is and lactobacillus brev is are added to allium chinense so as to ferment allium chinense, so that the preparation period of a fermented allium chinense product is shortened, the quality is improved, the salt use amount is reduced simultaneously, a desalination process is omitted, and the cost is reduced.

Rapid identification of gutter oil by detecting the capsaicin using surface enhanced Raman spectroscopy

Abstract: Accurate determination of the gutter oil represents a major food safety challenge. Here, we propose to use the surface enhanced Raman spectroscopy (SERS) with silver nanorod array substrates to detect the capsaicin, a marker of the gutter oil that is difficult to remove. The capsaicin has several characteristic SERS peaks at Δv = 807 cm−1, Δv = 1, 264 cm−1, and so forth, which correspond to the C39-C37-C43 stretching vibrational mode at the alkyl chain, the ring stretching vibrational mode, and so forth, confirmed by our density functional theory calculation. This SERS-based method enabled the rapid and highly sensitive detection of the gutter oil with minimum pretreatment. The concentration dependent SERS investigation shows that the SERS intensity of these characteristic peaks has a linear relationship with the capsaicin concentration when 1 mg/L < C < 60 mg/L. By combining a liquid phase extraction method and the SERS detection strategy, the capsaicin concentration as low as 30 mg/L can be detected from inoculated corn oil samples.