Abstract
Considering Fuji apple, the relationship between the near infrared spectrum and the soluble solids content (SSC), which is one of the important indexes to measure the internal quality of apple, is studied in this paper. In order to reduce the computational complexity and to improve the accuracy of modeling, this paper adopts the wavelet packet threshold denoising method for spectral spectrum processing, and uses the method of wavelet packet analysis (WPA) to filter the characteristic wavelength of the spectrum. Moreover, a prediction model of SSC is proposed based on BP neural network due to its characteristics of anti-noise, anti-interference, strong nonlinear conversion ability and the good capacity in handling nonlinear measured data with uncertain causality. Finally, the simulation results show that wavelet packet analysis can not only reduce the calculation of modeling variables, but also Improve modeling accuracy of the BP neural network model. The proposed method can make a better prediction of the SSC of apple.
This work is supported by the National Natural Science Foundation of China No. 61473135, Shandong Agricultural Machinery Research and Development Innovation Project Grant No. 2018YF011, and Shandong Provincial Key Research and Development Project 2017GGX10116, and Shandong Provincial Natural Science Foundation ZR2018PF009.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Shu, H., Chen, X.: The current task of the development of fruits industry in China. Chin. Fruit Tree 02, 1–3 (2018)
Chao, C., Qi, L., Fan, L., et al.: Research and application of infrared spectroscopy technology in food safety testing. Food Res. Dev. 40(14), 219–224 (2019)
Jing, S., Yan, Z., Qinglong, M.: Nondestructive identification of apple varieties by VIS/NIR spectroscopy. Preserv. Process. 19(03), 8–14 (2019)
Guo, Z., Zhao, C., Huang, W., et al.: Intensity correction of visualized prediction for sugar content in apple using hyperspectral imaging. Agric. Mach. 46(07), 227–232 (2015)
Sanaz, J., Carlos, Z.E., Singdhuja, S.: Near infrared spectroscopy to predict bitter pit development in different varieties of apples. Food Meas. 11(3), 987–993 (2017)
Xiong, Y., Wen, Z., Chen, G., et al.: Study on spectral identification based on wavelet packet analysis. Spectrosc. Spectr. Anal. 25(8), 1332–1335 (2005)
Li, J., Liu, Y., Liu, Z.: Soil total iron content hyperspectral inversion based on BP neural network. Technol. Econ. Guide 27(22), 93–94 (2019)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering
About this paper
Cite this paper
Yan, X., Bi, S., Shen, T., Ma, L. (2020). Prediction Analysis of Soluble Solids Content in Apples Based on Wavelet Packet Analysis and BP Neural Network. In: Zhang, YD., Wang, SH., Liu, S. (eds) Multimedia Technology and Enhanced Learning. ICMTEL 2020. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 327. Springer, Cham. https://doi.org/10.1007/978-3-030-51103-6_31
Download citation
DOI: https://doi.org/10.1007/978-3-030-51103-6_31
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-51102-9
Online ISBN: 978-3-030-51103-6
eBook Packages: Computer ScienceComputer Science (R0)