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作 者:李俊毅 黄忻 吴漫晔[2] 黄乾峰 倪少义 陈丽玲 吴道伟 王伟斌 林昭亮 LI Junyi;HUANG Xin;WU Manye;HUANG Qianfeng;NI Shaoyi;CHEN Liling;WU Daowei;WANG Weibin;LIN Zhaoliang(Jieyang City Food Inspection Institute,Jieyang 522000,China;Jieyang Polytechnic,Jieyang 522000,China)
机构地区:[1]揭阳市食品检验所,广东揭阳522000 [2]揭阳职业技术学院,广东揭阳522000
出 处:《现代食品》2023年第4期214-219,共6页Modern Food
摘 要:广东是白木香的主要产区,每年有大量的白木香叶资源,据报道,白木香叶含有较为丰富的黄酮类物质,本文采用星点设计响应面法优选白木香叶黄酮类化合物提取工艺,为白木香叶的资源开发提供依据。以硝酸铝显色法检测白木香叶黄酮类物质得率并以其为评价指标,考察提取方法及主要影响因素对得率的影响,在此基础上,采用星点设计响应面法(CCD)优化白木香叶黄酮类化合物提取工艺,通过软件Design-Expert 12.0对试验数据回归拟合建立多项式数学模型用于预测分析并对结果进行验证。采用醇溶液回流提取法对白木香叶黄酮类化合物进行提取,经优选后工艺条件为乙醇体积分数70%,料液比1:40,提取时间3 h,验证试验得率为3.10%,与回归模型预测值相符。优化后的工艺用于提取白木香叶黄酮类化合物,稳定可靠,重现性好,所建立回归模型预测性强。Guangdong is the main production area of Aquilaria Sinensis,and there are a lot of resources of Aquilaria Sinensis leaves every year.It is reported that Aquilaria Sinensis leaves contain relatively rich flavonoids.This study uses the central design response surface methodology to optimize the extraction process of flavonoids from Aquilaria Sinensis leaves,providing a basis for the resource development of Aquilaria Sinensis leaves.The extraction rate of flavonoids from the leaves of Aquilaria Sinensis was detected by the aluminum nitrate color method and used it as the evaluation index,examining the impact of extraction methods and main influencing factors on yield.On this basis,the extraction process of flavonoids from the leaves of Aquilaria Sinensis was optimized by using the central composite design response surface methodology(CCD).A polynomial mathematical model was established by regression fitting of the experimental data through the software Design Expert 12.0 to predict and analyze the results.Flavonoids from the leaves of Aquilaria Sinensis were extracted by the method of alcohol solution reflux extraction.The optimized extraction conditions were 70%ethanol volume fraction,1:40 material to liquid ratio,and 3 hours of extraction time.The yield of the validation experiment was 3.10%,which was consistent with the prediction value of the regression model.The optimized process is stable,reliable,reproducible,and the regression model is highly predictive.
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