(FENXI HUAXUE) Chinese Journal of Analytical Chemistry. Boosting

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1 42 (FENXI HUAXUE) Chinese Journal of Analytical Chemistry 1679~ / j.issn Bagging Boosting * * Near infrared spectroscopy NIR NIR Bagging Bagging-PLS Boosting Boosting-PLS Partial Least Squares PLS Synergy interval partial least squares sipls Competitive adaptive reweighted sampling CARS Bagging-PLS Boosting-PLS 10 PLS sipls 820 ~ nm 1030~ nm 820 ~ ~ nm CARS 5 10 RMSECV Bagging-PLS Boosting-PLS RMSEP 0.02~ 0.04 g /L 4% ~ 5% Baggning-PLS Boosting-PLS NIR Bagging Boosting 1 Near infrared spectroscopy NIR 1 2 MSPC 3 4 PCA-MBRSD NIR Lonicera japonica Partial least squares PLS PLS 5 NIR Bagging-PLS Boosting-PLS NIR Bagging Boosting Bagging Boosting PLS 6 NIR HPLC 216 PLS Kennard-Stone K-S No No * yjqiao@ 263.net wzs@ bucm.edu.cn

2 Bagging-PLS Boosting-PLS K-S 400 ~ 2500 nm HPLC Bagging Boosting 1 HPLC Bagging Bootstrap aggregating Table 1 Two batches statistics of HPLC reference value chlorogenic acid in ethanol precipitation process of Lonicera japonica Batch Minimum Maxium Average SD RSD % Bagging HPLC g /L Notes Concentration unit of HPLC reference value g /L. Boosting Boosting Bagging Boosting Boosting T ω t i = 1 /M i = 1 2 M 1 M t = 1 2 T ⅰ~ ⅵ ⅰ t M ⅱ t M PLS h t ⅲ h t L t l = ^y t i - y i /max ^y t - y i i = 1 2 M 2 ^y t i t ⅳ t 珔 L t = M L t i ω t i 3 i = 1 ⅴ β t = 珔 L t / 1 - 珔 L t 4 0 ~ 1 ⅵ t +1 ωi = ω t i β 1- 珔 L t i 5 T Bagging-PLS Boosting-PLS NIR Bagging-PLS Boosting-PLS NIR 10~ Bagging Bagging 14 Boosting 15 NIR Bagging Boosting 16 Bagging-PLS Boosting-PLS 2.4 Matlab 7.10 MatlabWorks Inc. U.S. PLS PLS_Toolbox Eigenvector Research Inc. U.S. XDS Rapid Liquid Analyzer FOSS Unscrambler9.7 CAMO CARS http / /code.google.com /p /carspls / r RMSEP

3 11 Bagging Boosting NIR ~ 2500 nm PLS r p R 2 cal R 2 val PLS r p 0.92 ~ 0.97 PLS PLS K-S Bagging-PLS Boosting-PLS Bagging-PLS Boosting-PLS 500 Bagging-PLS Boosting-PLS r p RMSEP r p RMSEP Bagging-PLS Boosting-PLS NIR 4 1 Fig.1 A B NIR NIR spectra of ethanol precipitation process of Lonicera japonica A is the first batch and B is the second batch 2 PLS Table 2 Results of PLS models of ethanol precipitation process of Lonicera japonica in the first batch Pretreatment method Later factors Calibration set R 2 cal RMSEC Validation set R 2 cal RMSEC Raw Baseline SNV SNV+Baseline Baseline+SNV+Noise S-G S-G+Noise SNV+Noise ST SNV+ST Baseline+ST MSC Raw S-G Savitaky-Golay ST Kubelka-Munk SNV MSC Noise Raw Original spectrum S-G Savitaky-Golay ST Spectroscopic transformation absorbance to Kubelka-Munk transformation SNV Standard normal variate MSC Multiplicative scatter correction Noise Added noise RMSEC Root mean square error of calibration RMSECV Root mean square error of cross-validation. r p

4 PLS Table 3 Results of PLS models of ethanol precipitation process of Lonicera japonica in the first batch Pretreatment method Later factors Calibration set R 2 cal RMSEC Validation set R 2 cal RMSEC Raw Baseline SNV S-G SNV+Baseline Baseline+SNV+Noise SNV+Noise ST SNV+ST MSC Raw S-G Savitaky-Golay ST Kubelka-Munk SNV MSC Noise Raw Original spectrum S-G Savitaky-Golay ST Spectroscopic transformation absorbance to Kubelka-Munk transformation SNV Standard normal variate MSC Multiplicative scatter correction Noise Added noise. r p 2 Fig.2 NIR r p A B r p values for NIR models of ethanol precipitation process of Lonicera japonica A is the first batch B is the second batch 3 Fig.3 NIR RMSEP A B RMSEP values for NIR models of ethanol precipitation process of Lonicera japonica A is the first batch B is the second batch Bagging-PLS Boosting-PLS r p 0.93 ~ 0.95 RMSEP sipls Synergy interval partial least squares sipls sipls Norgaard ipls 17 PLS 2 3 4

5 11 Bagging Boosting NIR Table 4 Prediction results of NIR models for two batches of ethanol precipitation process of Lonicera japonica Algorithm Bagging-PLS Boosting-PLS Batches of samples Interation number r p r p mean RMSEP RMSEP mean Root mean square error of cross-validation RMSECV NIR RMSECV RMSECV = n i = 1 槡 y NIRi -y Refi 2 n y NIRi PLS y Refi HPLC sipls 4 5 r NIR RMSECV sipls Fig.4 Ethanol precipitation of Lonicera japonica in the first batch using synergy interval PLS sipls to select variables 5 sipls Fig.5 Ethanol precipitation of Lonicera japonica in the friest batch using synergy iterval PLS sipls to select variables

6 ~ nm 1030 ~ nm ~ nm 960 ~ nm NIR r p 2% ~ 4% RMSEP 0.02 ~ 0.04 g /L sipls 6 Fig.6 sipls NIR models results of ethanol precipitation of Lonicera japonica in the first batch after variables selection using sipls 7 Fig.7 sipls NIR models results of ethanol precipitation of Lonicera japonica in the second batch after variables selection using sipls CARS CARS competitive adaptive reweighted sampling RMSEP root mean square error of prediction Table 5 The models for two batches of ethanol precipitation of Lonicera japonica CARS-Bagging-PLS RMSEP r p CARS-Boosting-PLS Algorithms Batches Variable selection RMSEP mean r p mean 5 CARS Without selected CARS Bagging-PLS Without selected CARS r p Without selected ~ 5 CARS Boosting-PLS Without selected CARS CARS Competitive adaptive reweighted sampling RMSEP Root mean square error of prediction.

7 11 Bagging Boosting CARS r p RMSEP Bagging Boosting CARS r p RMSEP 4 Bagging-PLS Boosting-PLS PLS NIR Bagging-PLS Boosting-PLS Bagging- PLS Boosting-PLS NIR NIR References 1 LIU Shu-Hua ZHANG Xue-Gong ZHOU Qun SUN Su-Qin. Spectroscopy and Spectral Analysis WU Zhi-Sheng TAO Ou CHENG Wei YU Lu SHI Xin-Yuan QIAO Yan-Jiang. Chinese J. Anal. Chem XU Bing SHI Xin-Yuan QIAO Yan-Jiang DU Min SUI Cheng-Lin LIU Qian. China Journal of Traditional Chinese Medicine and Pharmacy XU Bing LUO Gan LIN Zhao-Zhou AI Lu SHI Xin-Yuan QIAO Yan-Jiang. Chemical Journal of Chinese Universities Wu Z S Xu B Du M Sui C L Shi X Y Qiao Y J. Journal of Pharmaceutical and Biomedical Analysis CHENG Long WANG Gui-Zeng. Journal of Tsinghua University Science and Technology s s Wu Z S Du M Sui C L Shi X Y Qiao Y J. Analytical Methods Drucker H. Proceedings of the Fourteenth International Conference on Machine Learning Shao X Bian X Cai W. Analy. Chim. Acta ZHU Hong-Bin. Computer Applications and Software HE Ming LI Guo-Zheng YUAN Jie WU Geng-Feng. Journal of Shanghai University Natural Science Edition WANG Li ZHU Xue-Feng. Control Engineering of China Breiman L. Machine Learning Zhang H Ishikawa M. International Congress Series YU Ling WU Tie-Jun. Pattern Recognition and Artificial Intelligence CHU Xiao-Li XU Yu-Peng LU Wan-Zhen. Chinese J. Anal. Chem Norgaard L Saudland A Wagner J Nielsen J P Munck L Engelsen S B. Applied Spectroscopy

8 Li H Liang Y Xu Q Cao D S. Anal. Chim. Acta LIN Zhao-Zhou SHI Xin-Yuan QIAO Yan-Jiang. World Science and Technology /Modernization of Traditional Chinese Medicine and MateriaMedica A Study on Model Performance for Ethanol Precipitation Process of Lonicera japonica by NIR Based on Bagging-PLS and Boosting-PLS algorithm CHEN Zhao 1 2 WU Zhi-Sheng * 2 SHI Xin-Yuan 2 XU Bing 2 ZHAO Na 2 QIAO Yan-Jiang * 2 1 Fujian University of Traditional Chinese Medicine Fuzhou China 2 Research Center of TCM Information Engineering Beijing University of Chinese Medicine Beijing China Abstract To provide the methodology for rapid quality evaluation of Lonicera japonica we have established the stable quantitative model of near infrared spectroscopy NIR. The performance of Bagging partial least squares Bagging-PLS model and Boosting partial least squares Boosting-PLS model was compared with that partial least squares PLS model based on the NIR data of ethanol precipitation process of Lonicera japonica. On this basis the performance of these two models after variables selection was also studied by the methods of sipls synergy interval partial least squares and CARS competitive adaptive reweighted sampling. The experimental results showed that the prediction performance of Bagging-PLS and Boosting-PLS models was superior to PLS model with the latent factor of 10. The band of nm and nm for the first batch was selected by the method of sipls. In addition the band of nm and nm was selected for the second batch sample in the same method.furthermore the method of CARS was taken to select variables for the two batches samples with 5-fold cross-validation and 10-fold cross-validation.and the lowest RMSECV root mean square error of cross-validation values were used to take subset.compared to the model performance without the method of CARS the RMSEP value of the Bagging- PLS model and Boosting-PLS model for the concentration of chlorogenic acid reduced by g /L and r p correlation coefficient of prediction value increased by 4%-5%. Generally Bagging-PLS and Boosting-PLS could be regarded as rapid prediction methodsfor NIR quantitative models of ethanol precipitation process of Lonicera japonica. Keywords Process analysis technology Lonicera japonica Ethanol precipitation Bagging-partial least squares model Boosting-partial least squares moldel Received 29 May 2014 accepted 24 July 2014 This work was supported by the National Natural Science Foundation of China No and the Special Research Foundation for the Doctoral Program of Higher Education No