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Compact Near-Infrared Spectrometer for Quantitative Determination of Wood Composition
Abstract
A compact desktop near-infrared spectrometer with near-infrared spectrum acquisition software was developed based on fixed grating type fiber optic spectrometer with MEMS technology. The light source stability, baseline stability, instrument signal-to-noise ratio, and dark current drift are four important indicators for the performance test and evaluation of the spectral system. The test results show that the light source reaches a stable state after being warmed up for 2 s. The standard deviation of 100 %T line of1200-1550 nm instrument is less than 0.0003, and the signal-to-noise ratio is 3000:1. The dark current relative standard deviation fluctuates between 0.0019 and 0.0035. Based on 88 samples of the crushed material of lumber for wood cellulose and lignin contents, the quantitative calibration model was established using multiple scatter correction spectra pretreatment method set up after the correction model of cellulose and lignin. Root mean square errors of calibration set of cellulose and lignin are 0.6096 and 0.9572%, respectively. Root mean square errors of prediction set of cellulose and lignin are 1.2884 and 1.7712% respectively. The experimental results show that the developed small NIR spectrometer has a stable working state, and the prediction results for cellulose and lignin content based on the calibration model for powdery wood samples verify that the small NIR spectrometer has a high detection accuracy and can be used in the rapid detection of common materials.
Supporting agencies: This research was supported in part by National Key R&D program of China (SQ2018YFE011812)
About the Authors
W. Qi
School of Food and Biological Engineering at Jiangsu University
China
China
Zhenjiang 212013
Zh. Xiong
College of Light Industry and Food Engineering at Nanjing Forestry University
Russian Federation
Russian Federation
Nanjing 210037
H. Tang
Tianjin Jiuguang Technology Development Co. Ltd.
China
China
Tianjin 300450
D. Lu
School of Food and Biological Engineering at Jiangsu University
China
China
Zhenjiang 212013
B. Chen
School of Food and Biological Engineering at Jiangsu University
China
China
Zhenjiang 212013
References
1. D. Eisenstecken, A. Panarese, P. Robatscher, C. W. Huck, A. Zanella, M. Oberhuber, Molecules, 20, No. 8, 13603-13619 (2015).
2. Y. Liu, Z. Xia, L. Yao, Y. Wu, Y. Li, S. Zeng, H. Li, J. Food Comp. Anal., 84 (2019).
3. Z. H. Zhang, X. X. Muo, Y. J. Guo, W. Wang, Spectrosc. Spectr. Anal., 31, No. 7, 1975-1979 (2011).
4. J. H. Qu, D. Liu, J. H. Cheng, D. W. Sun, J. Ma, H. B. Pu, X. A. Zeng, Crit. Rev. Food Sci., 55, No. 13, 1939-1954 (2015).
5. B. Baca-Bocanegra, J. M. Hernandez-Hierro, J. Nogales-Bueno, F. J. Heredia, Talanta, 192, 353-359 (2019).
6. K. He, H. Cheng, W. Du, F. Qian, Chem. Intel. Lab. Syst, 134, 79-88 (2014).
7. L. J. Xie, A. C. Wang, H. R. Xu, X. P. Fu, Y. B. Ying, T. Asab., 59, No. 2, 399-419 (2016).
8. Q. Wen, H. J. Lei, J. Huang, F. Yu, L. K. Huang, Z. Zhang, D. L. Li, Y. C. Peng, Z. Y. Wen, Appl. Opt., 58, No. 17, 4642-4646 (2019).
9. M. Rani, C. Marchesi, S. Federici, G. Rovelli, I. Alessandri, I. Vassalini, S. Ducoli, L. Borgese, A. Zacco, F. Bilo, E. Bontempi, L. E. Depero, Materials (Basel), 12, No. 17 (2019).
10. M. Blanco, I. Villarroya, Trac-TrendAnal. Chem., 21, No. 4, 240-250 (2002).
11. A. Sakudo, Clin. Chim. Acta, 455, 181-188 (2016).
12. X. Y. Xiang, Z. Y. Wen, Z. C. Long, M. J. Hong, Y. Q. Liang, Y. Xu, Spectrosc. Spectr. Anal., 29, No. 8, 2286-2290 (2009).
13. N. Odisio, M. Calabrese, A. Idone, N. Seris, L. Appolonia, J. M. Christille, Eur. Phys. J. Plus, 134, No. 2, 16 (2019).
14. Z. Xiong, F. Pfeifer, H. W. Siesler, Appl. Spectrosc., 70, No. 4, 635-644 (2016).
15. C. F. Zhou, G. T. Han, S. W. Gao, M. Y. Xing, Y. Song, W. Jiang, BioResources, 13, No. 3, 6122-6132 (2018).
16. Y. Horikawa, S. Hirano, A. Mihashi, Y. Kobayashi, S. C. Zhai, J. Sugiyama, Appl. Biochem. Biotechnol., 188, No. 4, 1066-1076 (2019).
17. D. Mauruschat, B. Plinke, J. Aderhold, J. Gunschera, P. Meinlschmidt, T. Salthammer, Wood Sci. Technol, 50, No. 2, 313-331 (2016).
18. O. Elle, R. Richter, M. Vohland, A. Weigelt, Sci. Rep, 9, 11 (2019).
19. X. L. Jin, X. L. Chen, C. H. Shi, M. Li, Y. J. Guan, C. Y. Yu, T. Yamada, E. J. Sacks, J. H. Peng, Bioresour. Technol., 241, 603-609 (2017).
20. Y. H. Yun, H. D. Li, B. C. Deng, D.-S. Cao, TrAC Trend. Anal. Chem, 113, 102-115 (2019).
21. H. Li, J. X. Wang, Z. N. Xing, G. Shen, Spectrosc. Spectr. Anal., 31, No. 2, 362-365 (2011).
22. A. Rinnan, F. V. D. Berg, S. B. Engelsen, TrAC Trend. Anal. Chem., 28, No. 10, 1201-1222 (2009).
23. P. A. M. Nascimento, L. C. D. Carvalho, L. C. C. Jrnior, F. M. V. Pereira, G. H. D. A. Teixeira, Postharvest Biol. Technol., 111, 345-351 (2016).
Review
For citations:
Qi W., Xiong Zh., Tang H., Lu D., Chen B. Compact Near-Infrared Spectrometer for Quantitative Determination of Wood Composition. Zhurnal Prikladnoii Spektroskopii. 2021;88(2):340(1)-340(8).
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