基于自适应信号分解的农产品太赫兹光谱去噪方法研究

doi:10.3964/j.issn.1000-0593(2025)12-3575-10

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摘要：针对农产品太赫兹光谱信号中复杂基质导致的峰重叠问题及环境与系统噪声引起的动态非线性干扰，该研究探究了基于自适应信号分解的去噪方法提升太赫兹光谱质量的可行性。采用太赫兹时域光谱（THz-TDS）结合衰减全反射（ATR）附件采集了48个花生样品的吸收光谱，以花生水分含量的THz-ATR定量预测模型为研究对象，分别应用小波变换（WT）、经验模态分解（EMD）、局部均值分解（LMD）及其改进方法——分段局部均值分解（SLMD）和分段镜像延拓局部均值分解（PME-LMD）进行光谱去噪，并通过支持向量回归（SVR）模型评估各方法的适用性。实验结果表明：经PME-LMD去噪后构建的花生水分预测模型性能最优，其均方根误差（RMSE）、决定系数（R²）和平均绝对百分比误差（MAPE）分别达到0.010、0.912和0.040。相较于传统方法，PME-LMD显著提升了光谱质量与模型预测精度。研究提出的PME-LMD去噪策略有效抑制了太赫兹光谱信号中的非均匀噪声干扰，为农产品太赫兹光谱分析提供了高效精准的信号预处理方法，对太赫兹技术在农产品品质检测中的应用具有重要理论价值与工程指导意义。

关键词：太赫兹光谱；去噪方法；农产品；支持向量回归；分段镜像延拓局部均值分解

Abstract：To address the issues of peak overlap caused by complex matrices in agricultural product terahertz (THz) spectral signals and the dynamic, nonlinear interference induced by environmental and system noise, this study explores the feasibility of adaptive-signal-decomposition-based denoising methods to improve THz spectral quality. THz time-domain spectroscopy (THz-TDS) combined with an attenuated total reflection (ATR) accessory was used to collect THz absorbance spectra from 48 peanut samples. Taking the quantitative prediction model of peanut moisture content based on THz-ATR as an example, wavelet transform (WT), empirical mode decomposition (EMD), local mean decomposition (LMD), and its improved methods—segmented local mean decomposition (SLMD) and piecewise mirror extension local mean decomposition (PME-LMD)—were employed for spectral denoising. The applicability of different denoising methods was evaluated using a support vector regression (SVR) model. Experimental results show that the peanut moisture content prediction model constructed after PME-LMD denoising achieved the best performance, with a root mean square error (RMSE), coefficient of determination (R²), and mean absolute percentage error (MAPE) of 0.010, 0.912, and 0.040, respectively. Compared with traditional methods, PME-LMD significantly improved spectral quality and model prediction performance. The PME-LMD denoising strategy proposed in this study effectively suppresses non-uniform noise interference in THz spectral signals, providing an efficient and accurate preprocessing method for THz spectral analysis of agricultural products. This research provides theoretical support and technical guidance for the application of THz technology for detecting agricultural product quality.

Key words：Terahertz spectroscopy; Denoising method; Agricultural products; Support vector regression; Piecewise mirror extension local mean decomposition

收稿日期: 2025-06-28 修订日期: 2025-09-10

中图分类号:

O657.31

基金资助: Supported by the National Key R&D Program of China (2023YFD2101001), National Natural Science Foundation of China (32202144,61807001)

通讯作者: 杨一 E-mail: yangyi@btbu.edu.cn

作者简介: WU Jing-zhu, female, (1979—), Professor, Beijing Key Laboratory of Big Data Technology for Food Safety, Beijing Technology and Business University e-mail: pubwu@163.com

引用本文:

吴静珠，刘宇昊，杨一，谢传銮，吕钟鸣，李沂灿. 基于自适应信号分解的农产品太赫兹光谱去噪方法研究[J]. 光谱学与光谱分析, 2025, 45(12): 3575-3584.
WU Jing-zhu, LIU Yu-hao, YANG Yi, XIE Chuan-luan, LÜ Zhong-ming, LI Yi-can. Research on Denoising Method of Agricultural Product Terahertz Spectroscopy Based on Adaptive Signal Decomposition. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2025, 45(12): 3575-3584.

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[1] Ferguson B, Zhang X C. Nature Materials, 2002, 1(1): 26
[2] Wang K, Sun D W, Pu H. Trends in Food Science & Technology, 2017, 67: 93.
[3] Salén P, Basini M, Bonetti S, et al. Physics Reports, 2019, 836-837: 1.
[4] Guo H, Shiraga K, Kondo N, et al. Food Chemistry, 2023, 425: 136237.
[5] Lei T, Li Q, Sun D W. Food Chemistry, 2022, 380: 131971.
[6] Yang Z, Tang D, Hu J, et al. Small, 2021, 17(3): 2005814.
[7] Ge H, Lü M, Lu X, et al. Photonics, 2021, 8(11): 518.
[8] Fu Y, Ren Y, Sun D W. Trends in Food Science & Technology, 2024, 147: 104463.
[9] Funaki C, Yamamoto S, Hoshina H, et al. Polymer, 2018, 137: 245.
[10] Cao C, Serita K, Kitagishi K, et al. Biophysical Journal, 2020, 119(12): 2469.
[11] Wang Z, Hu M, Kuruoglu E E, et al. IEEE Access, 2018, 6: 41087.
[12] Zhang X, Lu S, Liao Y, et al. Chemometrics and Intelligent Laboratory Systems, 2017, 164: 8.
[13] Afsah-Hejri L, Akbari E, Toudeshki A, et al. Computers and Electronics in Agriculture, 2020, 177: 105628.
[14] Hui L, Jingzhu W, Cuiling L, et al. IFAC-PapersOnLine, 2018, 51(17): 206.
[15] Ge H, Ji X, Lu X, et al. Spectrochimica Acta Part A: Molecular and Biomolecular Spectroscopy, 2023, 303: 123206.
[16] Jiang Y, Chen X, Ge H, et al. Foods, 2023, 12(15): 2819.
[17] Zhang Z, Lu Y, LÜ C, et al. Optics and Lasers in Engineering, 2021, 138: 106413.
[18] Klionskiy D, Kupriyanov M, Kaplun D. Journal of Vibroengineering, 2017, 19(7): 5560.
[19] Zhang L, Chang J, Li H, et al. IEEE Access, 2020, 8: 113943.
[20] Yan R, Gao R X, Chen X. Signal Processing, 2014, 96: 1.
[21] Li C, Liang M. Signal Processing, 2012, 92(9): 2264.
[22] Smith J S. Journal of the Royal Society Interface, 2005, 2(5): 443.
[23] Wang Y, He Z, Zi Y. Journal of Vibration and Acoustics, 2010, 132(021010)[2025-02-27]. https://doi.org/10.1115/1.4000770.
[24] Guo W, Huang L, Chen C, et al. Digital Signal Processing, 2016, 55: 52.
[25] Liang J, Wang L, Wu J, et al. Digital Signal Processing, 2020, 107: 102881.
[26] Fan X, Zhang Y, Krehbiel P R, et al. IEEE Transactions on Geoscience and Remote Sensing, 2021, 59(1): 86.
[27] Rilling G, Flandrin P, Gonçalves P. On Empirical Mode Decomposition and Its Algorithms[C]. Processing of the IEEE EURASIP Workshop on Nonlinear Signal and Image Processing (NSIP-03), New York: IEEE, 2003.
[28] Ling M, Bian X, Wang S, et al. Chemometrics and Intelligent Laboratory Systems, 2022, 230: 104655.
[29] Gao Y, Villecco F, Li M, et al. Entropy, 2017, 19(4): 176.