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| Determination of Active Ingredient in Emamectin Benzoate Formulation by Data Fusion Strategy Based on Near/Mid Infrared Spectra and Competitive Adaptive Reweighted Sampling |
| HU Jing, TANG Guo, DENG Hai-yan, XIONG Yan-mei* |
| College of Science, China Agricultural University，Beijing 100193, China |
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Abstract Rapid determination of pesticide active ingredient has been a trend in pesticide quality control. In this paper, we aimed to use data fusion strategy to develop a rapid and reliable method to determine the active ingredient in the emamectin benzoate formulation by fusing the information of NIR and MIR. Data fusion strategy combined with partial least squares (PLS) regression was applied. Competitive adaptive reweighted sampling was engaged to investigate effective variables in the PLS regression. Compared with the models established by independent NIR or MIR, there was a significant improvement provided by data fusion strategy, which benefited from the synergistic effect of complementary information obtained from NIR and MIR spectra. In the meantime, CARS was proved to be an effective variable selection technique in the modeling process that makes the model simpler and more efficient. The results in this work showed that data fusion is an effective modeling strategy that improves the model performance by utilizing more information from different sources. The feasibility of data fusion strategy can obtain better results in determination of low concentration samples (0.1%～1.0%), and data fusion of NIR and MIR spectra combined with a variable selection algorithm could be a promising strategy to determine the active ingredient in commercial pesticide formulation. Eventually, a data fusion method based on near infrared (NIR) spectra and mid infrared (MIR) spectra for determination the active ingredient of emamectin benzoate in the commercial formulation was developed.
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Received: 2016-03-25
Accepted: 2016-06-16
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Corresponding Authors:
XIONG Yan-mei
E-mail: xiongym@cau.edu.cn
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[1] Salguero-Chaparro L, Gaitán-Jurado A J, Ortiz-Somovilla V, et al. J. Food Control, 2013, 30: 504.
[2] Souza L M, Mitsutake H, Gontijo L C, et al. Fuel, 2014, 30: 257.
[3] Xiong Y M, Song X Z, Chou C Z, et al. Chinese Chem. Lett., 2012, 23: 1047.
[4] Xiong Y M, Tang G, Duan J, et al. Chinese J. Anal. Chem., 2012, 40: 1434.
[5] Xiong Y M, Duan Y Q, Wang D, et al. Spectroscopy and Spectral Analysis, 2010, 30(6): 1488.
[6] Anibal C V D, Callao M P, Ruisánchez I. Talanta，2011，84：829.
[7] Alamprese C, Casale M, Sinelli N, et al. LWT- Food Sci. Technol., 2013, 53: 225.
[8] Casale M, Oliveri P, Casolino C, et al. Anal. Chim. Acta, 2012, 712: 56.
[9] Jiang C, Liu Y, Qu H B. Anal. Methods，2013，5：4467.
[10] Acar E, Lawaetz A J, Rasmussen M A, et al. 35th Annual International Conference of the IEEE EMBS. Osaka, Japan, 3-7 July, 2013. 6023.
[11] Fan W, Shan Y, Li G Y, et al. Food Anal. Method，2012，5：585. |
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