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

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 output layer nodes for the crusted and fresh lotus seeds with 30 iterations, and 4 output layer nodes for the different years lotus seeds with 37 iterations; and the learning rate, training time and testing time were 0.01, 10 and 10 respectively. This model successfully distinguished the fresh lotus seeds from the aged samples with the training set accuracies of 92.5%and 100%and testing set accuracies of 95.0% and 91.7%. It also provided a classification of production year of the samples with the training set accuracies of 97.5%, 100%, 97.5%, and 100%, and with the testing set accuracies of 90%, 85%, 85%, and 90%. The whole time of one sample injected 6 times did not exceed 2 min with the full spectrum scan time at 100 ms, and the relative standard deviation （RSD） of the sample was 15.4%. Therefore, the method demonstrates that DAPCI-MS is a fast, convenient and accurate tool for detection of the different quality of lotus seeds, and has a reliable reference value for authentication of food with sufficient sensitivity and high throughput.