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Mid-Infrared Spectral Combined Vectorial Angle Method for Direct Quantifying the Content of Sucralose |
SU Hui1, 2, PAN Hao-ran1, 2, YAO Zhi-xiang1, 2, 3*, HUANG Xiao-cheng1, 2, LIU Liu1, 2, LIU Chun-shui1, 2 |
1. College of Biological and Chemical Engineering, Guangxi University of Science and Technology, Liuzhou 545006, China
2. Guangxi Key Laboratory of Green Processing of Sugar Resources, Guangxi University of Science and Technology, Liuzhou 545006, China
3. The Coordination Innovative Center of Sugar Industry of Guangxi, Nanning 530004, China |
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Abstract The diversity of components and interferences of spectral overlapping highly and background reduce the features of the mid-infrared of the complex mixtures. Furthermore, the mid-infrared spectrum of a multi-component complex sample is generally not a simple sum of spectra of the components. Chemometrics techniques are needed for quantitative interpretation of the mid-infrared spectra. The problems of serious overlap and nonlinearity in infrared spectra were solved by data accumulation, feature extraction and correction modeling from chemometrics methods such as PLS and ANN, etc. And bi-measurement quantitative analysis was applied preliminarily based on Lambert Bill’s law and the popularization of the sampling technology of ATR. Then how to reduce these disturbances and ensure the accuracy of the results effectively was the main direction of research. Based on evaluating the spectral intensity of optical channel and background, this study proposed a new approximate linear quantitation method combined with vector angle transformation. Through the preliminary theoretical calculation, it was found that there was a certain relationship between the multi-component mixture and the vector angle value of the relative content in the mixture, and the relationship was not influenced by the batch sampling. Further, the Gauss curve was used to simulate the mixed signal, which fully illustrated a linear correlation within a certain range if suitable wavelength range was selected, and the correlation was not affected by the change of the measurement conditions. The KBr compression method was used to get the mixed samples, into which the components were added step by step, and the spectral and mixed-sample spectral signals were obtained. The first order derivation was carried out to eliminate the additive error signals which were converted into the space vector angle to the elimination of light path difference in batch sampling, and then they converted the content into a percentage. The angle values of the mixed samples have simple linear relationships with the contents of the reference materials. Then the quantitative analysis was made by using the standard curve of the calculated result angle values and the contents. This method was utilized to determine the contents in samples of the mixing of TGS and TGS-6-A (two-component systems), and the deacylated products of TGS-6-A (multi-component systems) with satisfactory results. The established correlation coefficients (r) of the standard curves were all above 0.995 0 and the relative errors were less than 8%. The result shows the established analytical method could meet the content analysis of multi-component samples. It has great reference value for the study of multi-component samples by mid-infrared transmission spectroscopy.
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Received: 2018-05-07
Accepted: 2018-10-10
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Corresponding Authors:
YAO Zhi-xiang
E-mail: zxyao@gxust.edu.cn
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[1] De C E S, Cassella R J. Talanta, 2016, 152: 33.
[2] Lee J, Nam Y S, Min J, et al. Journal of Forensic Sciences, 2016, 61(3): 815.
[3] Sato S, Kawaguchi T, Kaneko F. Macromolecular Symposia, 2016, 369(1): 114.
[4] Carlon H R. Applied Optics, 1980, 19(12): 3610.
[5] Pollard M J, Griffiths P R, Nishikida K. Applied Spectroscopy, 2007, 61(8): 860.
[6] Strasfeld D B, Ling Y L, Gupta R, et al. Journal of Physical Chemistry B, 2011, 113(47): 15679.
[7] Orlov A S, Mashukov V I, Rakitin A R, et al. Journal of Applied Spectroscopy, 2012, 79(3): 484.
[8] LIANG Qi-feng, LI Shan(梁奇峰,李 珊). The Food Industry(食品工业), 2015, (4): 283.
[9] ZHANG Xiu-ping, HE Shu-mei(张秀萍,何书美). Journal of Analytical Science(分析科学学报), 2007, 23(4): 484.
[10] HUANG Li, LI Ling, FENG Ling, et al(黄 丽,李 玲,冯 玲,等). Food Science(食品科学), 2015, 36(12): 205.
[11] Eugenio Sanchez, Bruce R Kowalski. Journal of Chemometrics, 2010, 2(4): 247.
[12] Eugenio Sanchez, Bruce R Kowalski. Journal of Chemometrics, 1988, 2(4): 265.
[13] YAO Zhi-xiang, SU Hui(姚志湘,粟 晖). SCIENTIA SINICA Chimica(中国科学: 化学), 2010,(10): 1564. |
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