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DBN wavelet transform denoising method in soybean straw composition based on near-infrared rapid detection

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Abstract

In Hei Longjiang Province of China, ten million tons of soybean straws are produced every year, most of which are burned or discarded. The comprehensive utilization rate is less than 5 %. However, as a kind of high-quality raw material resources, the soybean straw is the main material when producing fuel ethanol and biodiesel. The traditional chemical detection technology was always applied to test the quality of straws’ internal components, which needs long time and high cost. Therefore, this research adopted near-infrared spectroscopy technology to establish models to achieve rapid detection of straw’s internal components. One hundred and fifty-two samples of straw were collected within Heilongjiang Province, and the main ingredients, lignin and hemicellulose, were chosen as the research objects. K-stone method is used to divide into calibration and validation sets. The correlation coefficient is adopted to select and validate characteristic bands. After removal of the abnormal sample by concentration of residual method, the emphasis is put on the effect of removal of spectrum noise by applying the wavelet transform. The experimental results are that the model has good stability under the full spectrum band 4000–12,000 cm−1. After the removal of 3 lignin and 3 hemicellulose samples, respectively, the R 2 of calibration set of lignin and hemicellulose models increased significantly, from 0.5836436 and 0.4994598 to 0.6994097 and 0.6943559, respectively. However, after the 5-layer decomposition of DB2 wavelet, the R 2 increased to 0.8075574 and 0.8214309 separately. The validation set’s root mean square error prediction (RMSEP) fell from 0.7738772 and 0.3069899 to 0.5979685 and 0.2761462. After the conversion, the relative standard deviations (RSD) are 1.92 and 1.86 %, respectively. Unknown samples can be real-time predicted in short times after building the model, and the prediction process needs only 1960s. The experiments show that it is feasible to employ near-infrared spectroscopy method for rapid detection of straw components, which provides a new method to detect the straw ingredients rapidly in the future.

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References

  1. Yuhong, Z., Hairong, H., Qian, C.: Study on the necessity of energy utilization of crop straw in the low-carbon economy. Environ. Sutainable Dev 4, 104–108 (2012)

    Google Scholar 

  2. Wenjing, H., Bingyi, Z., Liang, H.: Analysis of main components in fast pyrolysis bio-oil from crop straws. Biomass Chem. Eng. 46(4), 5–10 (2012)

    Google Scholar 

  3. Jinzhu, W., Yuanxiu, W., Fengwang, L.: Determination of cellulose, hemicellulose and lignin in corn stalk. Food Ferment. Eng. 3, 44–47 (2010)

    Google Scholar 

  4. Li, H.: Study and APP raise on the assay method of cellulose and hemicellulose in Roughage. Urumqi, Sinkiang, Sinkiang Agricultural University (2008)

  5. Jiang, H., Liu, G., Xiao, X.: Monitoring of solid-state fermentation of wheat straw in a pilot scale using FT-NIR spectroscopy and support vector data description. Microchem. J. 102, 68–74 (2012)

    Article  Google Scholar 

  6. Feng, X., Yu, J., Tesso, T.: Qualitative and quantitative analysis of lignocellulosic biomass using infrared techniques: a mini-review. Appl. Energ. 104(2), 801–809 (2013)

    Google Scholar 

  7. Carina, J., Lomborg, M.H., Thomsen, J.: Power plant intake quantification of wheat straw composition for 2nd generation bioethanol optimization-a near infrared spectroscopy (NIRS) feasibility study. Bioresour. Technol. 101(4), 1199–1205 (2012)

    Google Scholar 

  8. Lidia Esteve Agelet: Measurement of single soybean seed attributes by near-infrared technologies. J. Agric. Food Chem. 60, 8314–8322 (2012)

    Article  Google Scholar 

  9. Hou, S., Li, L.: Rapid characterization of woody biomass digestibility and chemical composition using near-infrared spectroscopy. J. Integr. Plant Biol. 53(2), 166–175 (2011)

    Article  MathSciNet  Google Scholar 

  10. Hacisalihoglu, G., Larbi, B., Mark Settles, A.: Near-infrared reflectance spectroscopy predicts protein, starch, and seed weight in intact seeds of common bean. J. Agric. Food Chem. 58, 702–706 (2010)

    Article  Google Scholar 

  11. Jiang, F., Zhang, S., Wu, S., Yang, G., Zhao, D.: Multi-layered hand gesture recognition with kinect. J. Mach. Learn. Res 16(2), 227–254 (2015)

    MathSciNet  MATH  Google Scholar 

  12. Jiang, F., Gao, W., Yao, H., Zhao, D., Chen, X.: Synthetic data generation technique in signer-independent sign language recognition. Pattern Recogn. Lett. 30(5), 513–529 (2009)

    Article  Google Scholar 

  13. Jiang, F., Gao, Y., Liu, S., Zhao, D.: Discriminating features learning in gesture classification. IET Comput. Vision 9(5), 673–680 (2015)

    Article  Google Scholar 

  14. Chen, B.W., Chen, C.Y., Wang, J.F.: Smart homecare surveillance system: Behavior identification based on state transition support vector machines and sound directivity pattern analysis. IEEE Trans. Syst. Man Cybern. 43(6), 1279–1289 (2013)

    Article  Google Scholar 

  15. Chen, B.W., Wang, J.C., Wang, J.F.: A novel video summarization based on mining the story-structure and semantic relations among concept entities. IEEE Trans. Multimedia 11(2), 295–312 (2009)

    Article  Google Scholar 

  16. Jiang, F., Rho, S., Chen, B.-W., Du, X., Zhao, D.: Face hallucination and recognition in social network services. J. Supercomput. 71(6), 2035–2049 (2015)

    Article  Google Scholar 

  17. Jiang, F., Lin, C., Wang, H., Zhao, D.: Game theory based no-reference perceptual quality assessment for stereoscopic images. J. Supercomput. 71(9), 3337–3352 (2015)

    Article  Google Scholar 

  18. Jiang, F., Chen, B.W., Rho, S., Ji, W., Pan, L., Guo, H., Zhao, D.: Optimal filter based on scale-invariance generation of natural images. J. Supercomput. 72(1), 5–23 (2015)

    Article  Google Scholar 

  19. Jiang, F., Gao, Y., Liu, S., Zhao, D.: Discriminating features learning in gesture classification. IET Comput. Vision 9(5), 673–680 (2015)

    Article  Google Scholar 

  20. Lu Liu, X., Ye, P., Womac, A.R.: Variability of biomass chemical composition and rapid analysis using FT-NIR techniques. Carbohydr. Polym. 81(4), 820–829 (2010)

    Article  Google Scholar 

  21. Wang, D., Dowell, F., Chung, D.P.: Assessment of heat-damaged wheat kernels using near-infrared spectroscopy. Cereal Chem. 78(5), 625–628 (2010)

    Article  Google Scholar 

  22. Dowell, F.E., Wang, D., Wu, X., Dowell, K.M.: Detecting the antimalarial artemisinin in plant extracts using near-infrared spectroscopy. Am. J. Agric. Sci. Technol. 2(1), 1–7 (2013)

    Google Scholar 

  23. Xu, F., Wang, D.: Rapid determination of sugar content in corn stover hydrolysates using near infrared spectroscopy. Bioresour. Technol. 9, 293–298 (2013)

    Article  Google Scholar 

  24. He, C., Chen, L., Yang, Z.: A rapid and accurate method for on-line measurement of straw-coal blends using near infrared spectroscopy. Bioresour. Technol. 4(10), 314–320 (2012)

    Article  Google Scholar 

  25. Bruun, S., Jensen, J.W., Magid, J.: Prediction of the degradability and ash content of wheat straw from different cultivars using near infrared spectroscopy. Ind. Crops Prod. 31(2), 321–326 (2010)

    Article  Google Scholar 

  26. Templeton, D.W., Sluiter, A.D., Thomas, T.K.: Assessing corn stover composition and sources of variability via NIRS. Springer Science + Business Media. 16(4), 621–639 (2009)

  27. Hua, L., Xinggang, K., Guolian, W.: Study on the structural layer of crude fiber in roughag. Chin. Agric. Sci. 23(6), 32–36 (2007)

    Google Scholar 

  28. Huiying, L.: The production of microbial lipid using corn stover hydrolysate by mortierella isabehlinal. Changchun University of Technology, Changchun (2013)

    Google Scholar 

  29. Di, W., Chen, X., Shi, P.: Determination of linolenic acid and linoleic acid in edible oils using near-infrared spectroscopy improved by wavelet transform and uninformative variable elimination. Anal. Chim. Acta 634, 166–171 (2009)

    Article  Google Scholar 

  30. Belanche, A., Weisbjerg, M.R., Allison, G.G., Newbold, C.J., Moorby, J.M.: Estimation of feed crude protein concentration and rumen degradability by Fourier-transform infrared spectroscopy. J. Dairy Sci. 96(12), 7867–7880 (2013)

    Article  Google Scholar 

  31. Liu, Y., Sun, X., Zhang, H., Aiguo, O.: Nondestructive measurement of internal quality of Nanfeng mandarin fruit by charge coupled device near infrared spectroscopy. Comput. Electron. Agric. 71, 10–14 (2012)

    Article  Google Scholar 

  32. Blanke, M.M.: Non-invasive assessment of firmness and NIR sugar (TSS) measurement in apple, pear and kiwi fruit. Springer-Verlag Berlin Heidelberg. 18(10),19–24 (2013)

  33. Chen, B-W., He, X., Ji, W., Rho, S., Kung, S-Y.: Support vector analysis of large-scale data based on kernels with iteratively increasing order. J Supercomput. 72(9), 3297–3311 (2016)

    Article  Google Scholar 

  34. Chen, BW., Tsai, AC., Wang, JF.: Structuralized context-aware content and scalable resolution support for wireless VoD services. IEEE. Trans. Consum. Electron. 55(2), 713–720 (2009)

    Article  Google Scholar 

Download references

Acknowledgments

We would like to acknowledge the editors and reviewers, whose valuable comments greatly improved the manuscript. This work was supported in part by the National High Technology Research and Development Program of China (863 Program) (2013AA102303), the Natural Science Foundation of Heilongjiang Province of China (F201402) and Key Technologies R&D Program of Heilongjiang Province of China (GA15B107).

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Authors and Affiliations

  1. Northeast Agricultural University, Agrostology Postdoctoral Programme, Harbin, 150030, China

    Qingming Kong & Guowen Cui

  2. School of Animal Science and Technology, Northeast Agricultural University, Harbin, 150030, China

    Qingming Kong & Guowen Cui

  3. Division of Computer Engineering, Mokwon University, Daejeon, 302-729, Korea

    Sang-Soo Yeo

  4. School of Electronic Engineering and Information, Northeast Agricultural University, Harbin, 150030, China

    Zhongbin Su, Jingjing Wang, Fengzhu Hu & Weizheng Shen

  5. Department of Computer Science and Engineering, South Eastern University of Sri Lanka, Oluvil, 32360, Sri Lanka

    Varshinee Anu Padigala

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  1. Qingming Kong
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  2. Guowen Cui
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Correspondence to Guowen Cui or Weizheng Shen.

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Kong, Q., Cui, G., Yeo, SS. et al. DBN wavelet transform denoising method in soybean straw composition based on near-infrared rapid detection. J Real-Time Image Proc 13, 613–626 (2017). https://doi.org/10.1007/s11554-016-0642-7

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  • DOI: https://doi.org/10.1007/s11554-016-0642-7

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