表面解吸常压化学电离质谱结合人工神经网络鉴别新陈莲子  

Surface desorption atmospheric pressure chemical ionization mass spectrometry for identification of lotus seeds freshness based on PCA and BP-ANN

作　　者：罗丽萍[1] 赵占锋[2] 戴喜末[1,3] 张茜[1,3] 刘亚丽[3,4] 张兴磊[3] 章文军[4] 欧阳永中[3] 

机构地区：[1]南昌大学生命科学与食品工程学院,南昌330047 [2]哈尔滨工业大学电子与信息工程系,哈尔滨150001 [3]东华理工大学,江西省质谱科学与仪器重点实验室,南昌330013 [4]河北工业大学化工学院,天津300130

出　　处：《农业工程学报》2013年第7期261-266,共6页Transactions of the Chinese Society of Agricultural Engineering

基　　金："十二五"农村领域国家科技计划课题资助项目(2012BDA29B01);江西省高等学校科技落地计划项目(KJLD12051);江西省主要学科学术和技术带头人培养对象资助项目(20123BCB22004)

摘　　要：为实现对新陈莲子的快速鉴别,该文采用自行研制的表面解吸常压化学电离质谱(DAPCI-MS),在无需样品预处理的前提下,直接对新鲜和陈年莲子切面进行质谱检测,获得其化学指纹图谱,并通过主成分分析(PCA)和反向传输人工神经网络技术(BP-ANN)对所获指纹谱图信息进行分析,获得新鲜和陈年莲子的质谱信息特征。结果表明,在负离子模式下,DAPCI-MS结合化学计量学方法,实现了新鲜和陈年莲子的快速鉴别,其测试样本准确率分别为95.0%和91.7%;对不同年份莲子也能够有效地分类判别,2012、2011、2010和2009年莲子测试样本准确率分别为90%,85%,85%和90%。该方法具有分析速度快,信息提取准确,识别精度高等优点,为其他粮食谷物品质的鉴定提供参考。In order to realize fast discrimination of lotus seeds freshness, the surface desorption atmospheric pressure chemical ionization mass spectrometry （DAPCI-MS） and principal component analysis （PCA） with back propagation artificial neural network （BP-ANN） were used to distinguish the freshness of lotus seeds produced from 2009 to 2012. Without any sample pretreatments, 60 dried lotus seeds of each year, for a total of 240 individuals were tested and distinguished. The seeds were randomly picked from samples supplied by the Chinese Lotus Seeds Research Academy, which were cultured in the same field in Guangchang County, Jiangxi Province;and were grown with the same standardized method. Each lotus seed was longitudinally sliced to 2 mm for the DAPCI-MS investigation, and tested in the center of the slice with 6 replicates to obtain the averaged results. Experiments were performed using a commercial linear ion trap mass spectrometer （LTQ-XL, Finnigan, San Jose, CA, USA） installed with a homemade DAPCI ion source in negative ion detection mode, and coupled with N2 （0.1 MPa） through a methanol：water （1：1） solution, and a high voltage of 3.0 kV. The mass range m/z was 50–500 and the ion transfer tube temperature was 150 . The mass spectra were rapidly recorded by DAPCI℃ -MS and the data were processed by PCA. Its main components were selected as the input variables for classification mode of BP-ANN. PCA and BP-ANN were performed by Matlab7.0 software. The results showed that DAPCI-MS was a practical, convenient tool for the detection of matrix bases of lotus seeds. The signal peaks occurred increasingly over the storage time, and the observation correlates well with previous studies of aging cereals such as rice and wheat. The PCA’s first 50 components, whose cumulative contribution reached 99.99%and maintained almost all of the original information of the samples, were selected as the input layer of the BP-ANN model which included 50 input layer nodes, 48 hidden layer nodes, and 2 outp

关 键 词：质谱 主成分分析 无损检测 表面解吸常压化学电离 BP人工神经网络 莲子 

分 类 号：S567.2[农业科学—中草药栽培]