The chemical composition and in vitro antioxidant activity of the essential oil of propolis (EOP) collected from 25 locations in China was investigated. Steam-distillation extraction was used to extract the EOP, and chemical composition was identified by GC/MS. The antioxidant activities of EOP were also measured. The result showed that a total of 406 compounds were detected in EOP. The major compounds of Chinese EOP were cedrol, γ-eudesmol, benzyl alcohol, phenethyl alcohol, 2-methoxy-4-vinylphenol, 3,4-dimethoxystyrene and guaiol. Principal component analysis revealed the significant correlation between EOP compositions and their origins, and certain correlation was detected between EOP and their color. Linear discriminant analysis showed that 88 % and 84 % of the propolis samples were predicted correctly as the groupings identified by climatic zone and the color, respectively. Furthermore, the differences of antioxidant activities of EOP were significant. EOP of Shandong had the strongest antioxidant activities, whereas EOP of Guangdong, Yunnan and Hunan showed the poorest.
We synthesized a series of quinazolinone derivates as tyrosinase inhibitors and evaluated their inhibition constants. We synthesized 2-(2,6-dimethylhepta-1,5-dien-1-yl)quinazolin-4(3H)-one (Q1) from the natural citral. The concentration, which led to 50% activity loss of Q1, was 103 ± 2 μM (IC50 = 103 ± 2 μM). Furthermore, we considered Q1 to be a mixed-type and reversible tyrosinase inhibitor, and determined the KI and KIS inhibition constants to be 117.07 μM and 423.63 μM, respectively. Our fluorescence experiment revealed that Q1 could interact with the substrates of tyrosine and L-DOPA in addition to tyrosinase. Molecular docking studies showed that the binding of Q1 to tyrosinase was driven by hydrogen bonding and hydrophobicity. Briefly, the current study confirmed a new tyrosinase inhibitor, which is expected to be developed into a novel pigmentation drug.
In order to explore a new method to detect the freshness of lotus seeds, the lotus seeds stored for 0, 1, 2, and 3 years, respectively, were used as experimental materials and analyzed by DAPCI-MS (desorption atmospheric pressure chemical ionization-mass spectrometry). The obtained data were processed by principal component analysis (PCA) and backpropagation artificial neural networks (BP-ANNs). The result showed that DAPCI-MS could obtain abundant chemical material information from the slice surface of lotus seeds. The BP-ANNs model could be applied not only to distinguish fresh and aged lotus seeds with the testing set accuracies of 95.0% and 91.7%, respectively, but also to classify lotus seeds with different storage times with the testing set accuracies of 90.0%, 85.0%, 85.0%, and 90.0%, respectively. The paper developed a fast, convenient, and accurate method for the freshness detection of lotus seed and would provide reliable reference value for rapid authentication of food freshness by the rapid mass spectrometry technique.
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