Detection And Identification Of Biogenic Amines In Rice Wine

Biogenic amines (BAs) are low molecular weight nitrogenous organic compounds.The appropriate biogenic amines are beneficial to normal physiology function of human body, but over absorption of which would have undesirable physiological effects on human beings and in severe cases, death. As a specific traditional Chinese wines, because of its unique open fermentation technology with the multi-microorganisms,.in which the flavour,mellow taste and deliciousness were intensified. While the fermentation process is complex and difficult to control, may result in such a number of potentially harmful substances that biogenic amines is one of them. Compared with other alcoholic beverages, the level of biogenic amines in rice wine is greatest amounts. There was synergism effect between ethanol and toxicity of biogenic amines. Therefore, excessive drinking may be a potential hazard to health. Although, the limit standards for biogenic amines in rice wine had not yet been established, the consensus is that efforts should be made to reduce the content of BAs in foods, thereby reducing the risk of foods intake. The contents of biogenic amines in rice wine samples should be accurately determined that is foundation for carrying out relevant research. Furthermore, rice wine from different origin should be sorted according to biogenic amines and amino acids taht is the precursors of biogenic amines, not only help understand the effect of biogenic amine formation in different environmental conditions,but also can provide reference for detection the geographical origin and has heoretical and practical value.It may not be an option for complex matrix rice wine samples. In view of this,derivatization conditions of dansyl chloride were optimized and compared different types of solid-phase extraction cartridge to select the appropriate cartridge to examine biogenic amines in rice wine and then established a new method for the determination of seven biogenic amines (tryptamine, ?-phenylethylamine, putrescine, cadaverine, histamine,tyramine, spermidine) by high performance liquid chromatography-ultraviolet detection(HPLC-UV) in this paper. The developed method demonstrated good linearity with correlation coefficient of more than 0.9989 over a concentration range of 0.1-100 mg/L.The limit of detection (LOD) for BAs ranged from 2.07 to 5.56 ?g/L. The intra-day and inter-day relative standard deviations (RSD) were from 0.86% to 3.81% and from 2.13% to 3.82%, respectively. Spiking experiments showed that the overall recoveries ranged between 85 to 113 %. The sensitivity of the method was better than previous reports of HPLC-UV, and the effect of matrix effect on the detection results was reduced significantly.The standard curve can be used to accurately, detect the biogenic amines in the rice wine samples.Content of biogenic amines in 48 samples of rice wine in four producing regions(Zhejiang, Shanghai, Shandong and Hubei) were analysed by this method. The amount of biogenic amines varies greatly (Except tryptamine). Considering the possible connection between biogenic amines and amino acids, the content of amino acid in 48 rice wine samples were also determined. In one-way ANOVA analysis result manifestation, biogenic amines and amino acids of rice wine in four producing regions has significant influences.Correlation analysis showed that biogenic amines is closely related to amino acids. In the end of paper, the discriminative model of rice wine-producing region, which be based on biogenic amines and amino acids was established by discriminant analysis (DA), partial least squares regression (PLSR) and artificial neural network analysis (ANN).Discrimination analysis show that the rice wine from four producing origin is completely separated and located in their respective regions. The correct rate on back-substitution check is 100 % and cross check is 96 %, which indicated that these index had a positive effect of discriminant on rice wine-producing region. Partial least squares regression model has low misjudgment ratio, but the relative error is higher when compared with the real value. So the regression equation is not suitable for'the identification of the origin. The artificial neural network model is good in simulation results and suitable for region identification.