基于PCA-Stacking模型的食源性致病菌拉曼光谱识别
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摘要
食源性致病菌的快速识别是一项重要的工作,与传统检测方法相比,拉曼光谱能在无损检测的同时加快鉴别速度。为了提高大肠杆菌O157…H7以及布鲁氏菌S2株拉曼光谱识别的准确性和效率,提出一种基于主成分分析与Stacking算法的集成判别模型,使用网格搜索以及K折交叉验证来提高模型的稳健性。与逻辑回归、K近邻、支持向量机等单一模型进行对比,实验结果证明PCA-Stacking集成模型有最高的准确率,达99.73%,达到了预期效果。
The rapid identification of foodborne pathogenic bacteria is an important task.Compared with the traditional detection methods,Raman spectroscopy is a non-destructive testing method and can simultaneously enhance the identification speed.In order to improve the accuracy and efficiency of Raman spectroscopic identification of Escherichia coil O157…H7 and Brucella suis vaccine strain S2,a integral classification model is proposed based on the principal component analysis and the Stacking algorithm,whose robustness is improved by the grid search and K-fold cross validation.It is experimentally confirmed that compared with the logistic regression,K nearest neighbor,support vector machine and other single models,the integral model based on the Stacking algorithm possesses the highest accuracy rate of 99.73%the expected result is achieved.
The rapid identification of foodborne pathogenic bacteria is an important task.Compared with the traditional detection methods,Raman spectroscopy is a non-destructive testing method and can simultaneously enhance the identification speed.In order to improve the accuracy and efficiency of Raman spectroscopic identification of Escherichia coil O157…H7 and Brucella suis vaccine strain S2,a integral classification model is proposed based on the principal component analysis and the Stacking algorithm,whose robustness is improved by the grid search and K-fold cross validation.It is experimentally confirmed that compared with the logistic regression,K nearest neighbor,support vector machine and other single models,the integral model based on the Stacking algorithm possesses the highest accuracy rate of 99.73%the expected result is achieved.
引文
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[3]Zhang Y,Zhu L Q,Zhang Y T,et al.Simultaneous detection of three foodborne pathogenic bacteria in food samples by microchip capillary electrophoresis in combination with polymerase chain reaction[J].Journal of Chromatography A,2018,1555:100-105.
[4]Gong Q,Li Z L,Niu M F.A pilot study on PCR-based detection of four foodborne pathogenic microorganisms[J].Journal of Food Measurement and Characterization,2018,12(2):675-682.
[5]Gao W C,Li B,Wang X W,et al.Quick detection of five foodborne pathogenic bacteria based on surface enhanced Raman spectroscopy[J].Journal of Jilin Agricultural University,2017,39(6):733-737.高玮村,李博,王习文,等.基于表面增强拉曼技术快速检测5种食源性致病菌[J].吉林农业大学学报,2017,39(6):733-737
[6]Wang Y T,Qu H,Hao L Y,et al.Devising a rapid and efficient method of detecting Escherichia coli O157…H7based on aptamer-mediated surfaceenhanced Raman spectroscopy(SERS)[J].Journal of Pathogen Biology,2018,13(1):16-21.王宇田,曲晗,郝良玉,等.基于核酸适配体SERS技术快速检测大肠埃希菌O157…H7的研究[J].中国病原生物学杂志,2018,13(1):16-21.
[7]He X L,Chen L B,Wang J F,et al.Raman spectroscopy analysis of plastic steel window based on K nearest neighbors algorithm[J].Laser&Optoelectronics Progress,2018,55(5):053001.何欣龙,陈利波,王继芬,等.基于K近邻算法的塑钢窗拉曼光谱分析[J].激光与光电子学进展,2018,55(5):053001.
[8]Guo L B,Chen G N,Liu M Y,Analysis of biological tissue Raman spectroscopy data based on support vector machine algorithm[J].Fu Lighting Technology,2014,25(2):25-27.郭利斌,陈冠楠,刘明宇.基于支持向量机算法的生物组织拉曼光谱数据分析[J].福光技术,2014,25(2):25-27.
[9]Zheng J W,Yang T W.Classification method of biological tissues based on Raman spectrum features[J].Laser&Optoelectronics Progress,2017,54(5):053001.郑家文,杨唐文.基于拉曼光谱特征的生物组织识别方法[J].激光与光电子学进展,2017,54(5):053001.
[10]Zhang H,Wang Q J,Zhu J J,et al.Influence of sample data preprocessing on BP neural networkbased GPS elevation fitting[J].Journal of Geodesy and Geodynamics,2011,31(2):125-128.张昊,王琪洁,朱建军,等.样本数据预处理对基于BP神经网络的GPS高程拟合的影响[J].大地测量与地球动力学,2011,31(2):125-128.
[11]Fang X Q,Peng Y K,Li Y Y,et al.Rapid and quantitative detection method of sodium benzoate in carbonated beverage based on surface-enhanced Raman spectroscopy[J].Acta Optica Sinica,2017,37(9):0930001.房晓倩,彭彦昆,李永玉,等.基于表面增强拉曼光谱快速定量检测碳酸饮料中苯甲酸钠的方法[J].光学学报,2017,37(9):0930001.
[12]Ma R,Wang Q,Chu D Z,et al.Study on a photoelectric signal processing method for the DOConline analyzer[J].Journal of Ocean Technology,2016,35(6):44-49.马然,王茜,褚东志,等.一种DOC在线分析仪光电信号处理方法[J].海洋技术学报,2016,35(6):44-49.
[13]Liu X H,Tan Q P,Zeng P,et al.Comparison and implementation of several MOOC-based text classification[J].Computer Engineering&Software,2016,37(9):27-33.刘鑫昊,谭庆平,曾平,等.几种基于MOOC的文本分类算法的比较与实现[J].软件,2016,37(9):27-33.
[14]Song Limei,Luo J.Pattern recognition[M].Beijing:China Machine Press,2015.宋丽梅,罗菁.模式识别[M].北京:机械工业出版社,2015.
[15]Wei X P,Yu X C,Tan X,et al.A classification algorithm for hyperspectral images based on import vector machine[J].Journal of Geomatics Science and Technology,2015,32(4):379-383.魏祥坡,余旭初,谭熊,等.一种基于输入向量机的高光谱影像分类算法[J].测绘科学技术学报,2015,32(4):379-383.
[16]Wolpert D H.Stacked generalization[J].Neural Networks,1992,5(2):241-259.
[17]Aggarwal C C.Data classification:Algorithms and applications[M].Boca Raton:CRC Press,2014.