摘要
白酒基酒等级的准确鉴别是白酒分级储藏和勾兑的重要依据,对白酒质量控制至关重要。以浓香型白酒基酒为研究对象,采用超高效液相色谱-高分辨质谱联用(UPLC-Q-Exacive-MS)技术对不同等级浓香型白酒基酒进行分析,经过主成分分析(PCA)降维处理,结合线性判别分析(LDA)、支持向量机(SVM)和反向传播神经网络(BP-ANN)等多种化学计量学方法建立白酒基酒等级判别模型。结果显示,BP-ANN鉴别效果最好,在主成分为8时,训练集和测试集的识别率均达100%,不同等级的白酒样本全部正确识别;其次是SVM(训练集和测试集的识别率分别为96.25%、95.00%)和LDA(训练集和测试集的识别率分别为91.25%、90.00%)。实验证明,UPLC-Q-Exacive-MS结合化学计量学分析方法能实现不同等级浓香型白酒基酒的准确判别。
The accurate identification of the base liquor grade for Baijiu(Chinese liquor)was an important basis for the grade storage and blending of Baijiu,which was crucial for the quality control of Baijiu.Using the strong-flavor Baijiu as research object,different grades of base liquor of strong-flavor Baijiu was analyzed by UPLC and Q-Exactive high resolution mass spectrometry(UPLC-Q-Exacive-MS)based on orbitrap.The grade discrimination model of Baijiu base liquor was established after dimensionality reduction with principal component analysis(PCA)by chemometric methods including linear discriminant analysis(LDA),support vector machine(SVM)and back-propagation artificial neural network(BP-ANN).The results showed that the BP-ANN had the optimal effect.When the principal component was 8,the recognition rate of the training set and the test set was 100%,and different grades of samples were identified correctly.Followed by the SVM and LDA,the recognition rate of training set and test set of SVM was 96.25%and 95.00%,respectively.The recognition rate of training set and test set of LDA was 91.25%and 90.00%.Experiment showed that the UPLC-Q-Exactive-MS combined with chemometrics method could achieve accurate discrimination of different grades of base liquor of strong-flavor Baijiu.
作者
孙宗保
周轩
吴建峰
邹小波
孙莹
唐群勇
霍玲玲
SUN Zongbao;ZHOU Xuan;WU Jianfeng;ZOU Xiaobo;SUN Ying;TANG Qunyong;HUO Lingling(School of Food and Biological Engineering,Jiangsu University,Zhenjiang 212013,China;Jiangsu King's Luck Brewer Co.,Ltd.,Huaian 223411,China;Institute of Agricultural Engineering,Jiangsu University,Zhenjiang 212013,China)
出处
《中国酿造》
CAS
北大核心
2019年第4期42-46,共5页
China Brewing
基金
国家重点研发计划(2016YFD0401104)
国家自然科学基金(31671844)
江苏省博士后基金(1501100C)
江苏高校优势学科建设工程资助项目(PAPD)
镇江市重点研发计划(SH2016021)
江苏大学高级人才基金(15JDG087)
