|
|
|
|
|
|
| Research on Denoising Method of Agricultural Product Terahertz Spectroscopy Based on Adaptive Signal Decomposition |
| WU Jing-zhu1, LIU Yu-hao1, YANG Yi1*, XIE Chuan-luan2, LÜ Zhong-ming1, LI Yi-can1 |
1. Beijing Key Laboratory of Big Data Technology for Food Safety, Beijing Technology and Business University, Beijing 100048, China
2. Beijing Research Institute of Automation for Machinery Industry Co., Ltd., Beijing 100120, China |
|
|
|
|
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.
|
|
Received: 2025-06-28
Accepted: 2025-09-10
|
|
|
|
Corresponding Authors:
YANG Yi
E-mail: yangyi@btbu.edu.cn
|
|
[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. |
| [1] |
ZHAO Xiao-yan1*, ZHENG Shao-wen1, WU Xian-hao1, SUN Zhi-yan2, 3, TAO Rui2, 3, ZHANG Tian-yao1, YUAN Yuan1, LIU Xing4, ZHOU Da-biao2, 3, ZHANG Zhao-hui1, YANG Pei2, 3*. Prediction of EGFR Amplification Status of Glioma Based on Terahertz Spectral Data With Convolutional Neural Networks[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2025, 45(10): 2856-2862. |
| [2] |
DENG Yun1, 2, WANG Jun1, 2, CHEN Shou-xue2*, SHI Yuan-yuan3. Hyperspectral Inversion Model of Forest Soil Organic Matter Based on PCA-DBO-SVR[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2025, 45(02): 569-583. |
| [3] |
LI Xin-yu1, LIU Cai-qin1, HUANG Hao-chong1*, ZHENG Zhi-yuan1*, ZHANG Zi-li1, QIU Kun-feng2. Characterization of Pyrolytic Properties of Clay Minerals Based on Terahertz-Thermogravimetric Spectroscopy[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2025, 45(01): 179-182. |
| [4] |
KE Zhi-lin1, DONG Bing2, LING Dong-xiong2*, WEI Dong-shan2, 3*. Progress on Terahertz Spectroscopy Detection of Glass Transition of
Polymers[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2024, 44(10): 2709-2716. |
| [5] |
HE Yu-xin1, YANG Li-jun1*, YU Hua2, CHEN Qi-yu1, CHENG Li1, LIAO Rui-jin1. New Nondestructive Method of Methanol Detection in Insulating Oil Based on Terahertz Spectrum[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2024, 44(05): 1405-1411. |
| [6] |
LI Cheng-ju1, 3, LIU Yin-du1, 3, QIN Tian-yuan1, 3, WANG Yi-hao1, 3, FAN You-fang1, 3, YAO Pan-feng2, 3, SUN Chao1, 3, BI Zhen-zhen1, 3*, BAI Jiang-ping1, 3*. Estimation of Chlorophyll Content in Potato Leaves Based on
Machine Learning[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2024, 44(04): 1117-1127. |
| [7] |
LI Xin1, LIU Jiang-ping1, 2*, HUANG Qing1, HU Peng-wei1, 2. Optimization of Prediction Model for Milk Fat Content Based on Improved Whale Optimization Algorithm[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(09): 2779-2784. |
| [8] |
YU Yang1, ZHANG Zhao-hui1, 2*, ZHAO Xiao-yan1, ZHANG Tian-yao1, LI Ying1, LI Xing-yue1, WU Xian-hao1. Effects of Concave Surface Morphology on the Terahertz Transmission Spectra[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(09): 2843-2848. |
| [9] |
CHU Zhi-hong1, 2, ZHANG Yi-zhu2, QU Qiu-hong3, ZHAO Jin-wu1, 2, HE Ming-xia1, 2*. Terahertz Spectral Imaging With High Spatial Resolution and High
Visibility[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(02): 356-362. |
| [10] |
LU Xue-jing1, 2, GE Hong-yi2, 3, JIANG Yu-ying2, 3, ZHANG Yuan3*. Application Progress of Terahertz Technology in Agriculture Detection[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2022, 42(11): 3330-3335. |
| [11] |
LI Ming1*, HAN Dong-hai2*, LU Ding-qiang1, LU Xiao-xiang1, CHAI Chun-xiang1, LIU Wen3, SUN Ke-xuan1. Research Progress of Universal Model of Near-Infrared Spectroscopy in Agricultural Products and Foods Detection[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2022, 42(11): 3355-3360. |
| [12] |
TANG Xin, ZHOU Sheng-ling*, ZHU Shi-ping*, MA Ling-kai, ZHENG Quan, PU Jing. Analysis and Identification of Terahertz Tartaric Acid Spectral
Characteristic Region Based on Density Functional Theory and
Bootstrapping Soft Shrinkage Method[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2022, 42(09): 2740-2745. |
| [13] |
LI Yan1, LIU Qi-hang2, 3, HUANG Wei1, DUAN Tao1, CHEN Zhao-xia1, HE Ming-xia2, 3, XIONG Yu1*. Terahertz Imaging Study of Dentin Caries[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2022, 42(08): 2374-2379. |
| [14] |
JING Xia1, ZHANG Jie1, 2, WANG Jiao-jiao2, MING Shi-kang2, FU You-qiang3, FENG Hai-kuan2, SONG Xiao-yu2*. Comparison of Machine Learning Algorithms for Remote Sensing
Monitoring of Rice Yields[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2022, 42(05): 1620-1627. |
| [15] |
WU Jing-zhu1, LI Xiao-qi1, SUN Li-juan2, LIU Cui-ling1, SUN Xiao-rong1, YU Le1. Advances in the Application of Terahertz Time-Domain Spectroscopy and Imaging Technology in Crop Quality Detection[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2022, 42(02): 358-367. |
|
|
|
|
