Article

Temperature-Dependent Near-Infrared Spectroscopy for Sensitive Detection of Glucose

  • Wang Mingyuan ,
  • Cui Xiaoyu ,
  • Cai Wensheng ,
  • Shao Xueguang
Expand
  • College of Chemistry, Nankai University, Tianjin Key Laboratory of Biosensing and Molecular Recognition, Tianjin 300071, China

Received date: 2019-12-12

  Online published: 2020-02-13

Supported by

Project supported by the National Natural Science Foundation of China (No. 21775076) and the Fundamental Research Funds for the Central Universities (No. 63191743).

Abstract

Temperature-dependent near-infrared (NIR) spectroscopy has been proposed and used in the quantitative analysis of multi-component mixtures and the understanding of the interactions in solutions. Mutual factor analysis (MFA) was developed, in our previous work, to detect glucose content in aqueous solutions and serum samples using the NIR spectra measured at different temperatures. The essence of the algorithm is to extract and compare the spectral component, named as standardized signal (SS), mutually contained in the spectral data of different samples. The relative quantity of SS can be used to build the calibration model for quantitative analysis. Furthermore, the spectral information of water can be used for the analysis, because the change of the water spectrum with temperature is a reflection of the change in glucose content. In this work, serum samples with low glucose concentration were prepared and measured at the temperature range of 30~60℃ with a step of 5℃. The feasibility of MFA in the quantitative determination of low concentration samples was further studied. Serum solutions with glucose content of 1.0~15.0 mmol/L and 0.0~1.0 mmol/L were prepared, respectively. Before calculation of MFA, continuous wavelet transform (CWT) was used to improve the resolution of the spectra. The results show that MFA can achieve an accurate quantification of the glucose content. The linear correlation coefficients (R) of the calibration models between the relative quantity of SS and the concentration of glucose are 0.9923 and 0.9895, respectively, and the root-mean-squared error of prediction (RMSEP) are 0.35 and 0.07 mmol/L, respectively. The relative error of predicted concentration of samples in the validation set obtained from the calibration model of samples with a concentration of 1.0~15.0 mmol/L are in the range of -12.00%~5.64%, which are in a reasonable level for clinical uses. Temperature-dependent NIR spectroscopy combined with MFA may be a potential way for detecting the micro-content components in complex aqueous systems.

Cite this article

Wang Mingyuan , Cui Xiaoyu , Cai Wensheng , Shao Xueguang . Temperature-Dependent Near-Infrared Spectroscopy for Sensitive Detection of Glucose[J]. Acta Chimica Sinica, 2020 , 78(2) : 125 -129 . DOI: 10.6023/A19120424

References

[1] Sharma, S.; Goodarzi, M.; Delanghe, J.; Ramon, H.; Saeys, W. Appl. Spectrosc. 2014, 68, 398.
[2] Chu, M. X.; Miyajima, K.; Takahashi, D.; Arakawa, T.; Sano, K.; Sawada, S.; Kudo, H.; Iwasaki, Y.; Akiyoshi, K.; Mochizuki, M.; Mitsubayashi, K. Talanta 2011, 83, 960.
[3] Hazen, K. H.; Arnold, M. A.; Small, G. W. Appl. Spectrosc. 1994, 48, 477.
[4] Chen, J.; Arnold, M. A.; Small, G. W. Anal. Chem. 2004, 76, 5405.
[5] Liu, L. Z.; Arnold, M. A. Anal. Bioanal. Chem. 2009, 393, 669.
[6] Cui, X. Y.; Liu, X. W.; Yu, X. M.; Cai, W. S.; Shao, X. G. Anal. Chim. Acta 2017, 957, 47.
[7] Shao, X. G.; Cui, X. Y.; Yu, X. M.; Cai, W. S. Talanta 2018, 183, 142.
[8] Marquardt, L. A.; Arnold, M. A.; Small, G. W. Anal. Chem. 1993, 65, 3271.
[9] Sharma, S.; Goodarzi, M.; Delanghe, J.; Ramon, H.; Saeys, W. Appl. Spectrosc. 2014, 68, 398.
[10] Goodarzi, M.; Saeys, W. Talanta 2016, 146, 155.
[11] Hazen, K. H.; Arnold, M. A.; Small, G. W. Anal. Chim. Acta 1998, 371, 255.
[12] Amerov, A. K.; Chen, J.; Small, G. W.; Arnold, M. A. Anal. Chem. 2005, 77, 4587.
[13] Kang, N.; Kasemsumran, S.; Woo, Y.; Kim, H.; Ozaki, Y. Chemom. Intell. Lab. Syst. 2006, 82, 90.
[14] Tsenkova, R. NIR News 2008, 19, 12.
[15] Tsenkova, R. J. Near Infrared Spectrosc. 2009, 17, 303.
[16] Tarumi, M.; Shimada, M.; Murakami, T.; Tamura, M.; Shimada, M.; Arimoto, H.; Yamada, Y. Phys. Med. Biol. 2003, 48, 2373.
[17] Cui, X. Y.; Cai, W. S.; Shao, X. G. RSC Adv. 2016, 6, 105729.
[18] Cheng, D.; Cai, W. S.; Shao, X. G. Appl. Spectrosc. 2018, 72, 1354.
[19] Wang, L.; Zhu, X. W.; Cai, W. S.; Shao, X. G. Phys. Chem. Chem. Phys. 2019, 21, 5780.
[20] Zhu, X. W.; Cui, X. Y.; Cai, W. S.; Shao, X. G. Acta Chim. Sinica 2018, 76, 298. (in Chinese). (朱雪薇, 崔晓宇, 蔡文生, 邵学广, 化学学报, 2018, 76, 298.)
[21] Fan, M. L.; Cai, W. S.; Shao, X. G. Appl. Spectrosc. 2016, 71, 472.
[22] Liu, X. W.; Cui, X. Y.; Yu, X. M.; Cai, W. S.; Shao, X. G. Chin. Chem. Lett. 2017, 28, 1447.
[23] Ma, L.; Cui, X. Y.; Cai, W. S.; Shao, X. G. Phys. Chem. Chem. Phys. 2018, 20, 20132.
[24] Qi, L. H.; Cai, W. S.; Shao, X. G. Acta Chim. Sinica 2016, 74, 172. (in Chinese). (祁丽华, 蔡文生, 邵学广, 化学学报, 2016, 74, 172.)
[25] Shao, X. G.; Kang, J.; Cai, W. S. Talanta 2010, 82, 1017.
[26] Kang, J.; Cai, W. S.; Shao, X. G. Talanta 2011, 85, 420.
[27] Shao, X. G.; Cui, X. Y.; Wang, M.; Cai, W. S. Spectrochim. Acta Part A 2019, 213, 83.
[28] Czarnecki, M. A.; Morisawa, Y.; Futami, Y.; Ozaki, Y. Chem. Rev. 2015, 115, 9707.
[29] Šašić, S.; Segtnan, V. H.; Ozaki, Y. J. Phys. Chem. A 2002, 106, 760.
[30] Segtnan, V. H.; Šašić, S.; Isaksson, T.; Ozaki, Y. Anal. Chem. 2001, 73, 3153.
[31] Rajendran, R.; Rayman, G. J. Diabetes Sci. Technol. 2014, 8, 1081.
[32] Shao, X. G.; Cai, W. S. Rev. Anal. Chem. 1998, 17, 235.
[33] Shao, X. G.; Leung, A. K. M.; Chau, F. T. Acc. Chem. Res. 2003, 36, 276.
[34] Shao, X. G.; Ma, C. X. Chemom. Intell. Lab. Syst. 2003, 69, 157.
Outlines

/