紫苏中紫苏醛含量的高光谱预测方法

doi:10.3964/j.issn.1000-0593(2024)09-2667-08

Abstract
References
Related Citation (15)

Download: PDF (14670 KB)
Export: BibTeX | EndNote (RIS)

Abstract To rapidly detect Perilla aldehyde (PAE) content in Perilla Frutescens, hyperspectral imaging technology was employed, and the hyperspectral images of Perilla Frutescens were acquired from four distinct producing regions. Based on obtaining effective wavelength images of Perilla Frutescens, its texture features and energy values obtained by wavelet transform were fused with spectral values in various ways to create different characterization vectors. These vectors were then employed to construct corresponding rapid detection models for PAE content. The models' prediction capabilities were thoroughly compared and analyzed to determine the optimal prediction strategy for PAE content. The specific methods are as follows. (1) Four methods were employed to preprocess the raw spectral values. After evaluating the predictive performance of the constructed models, it was determined that Local Weighted Scatterplot Smoothing (LOWESS) emerged as the optimal preprocessing method. (2) The Competitive Adaptive Reweighted Sampling (CARS) and Successive Projections Algorithm (SPA) were employed to extract the characteristic wavelengths of the preprocessed spectral information, and the corresponding spectral values were then computed to facilitate their integration with other mentioned features in the paper. (3) Principal Component Analysis (PCA) was utilized to get effective wavelength images from the hyperspectral images. The grayscale co-occurrence matrix (GLCM) was then applied to the effective wavelength images to extract four texture features: Energy (ASM), Contrast (CON), Correlation (COR), and Entropy (ENT); simultaneously, the Daubechies wavelet was employed to conduct three-level decomposition of the effective wavelength image, and the energy of the low-frequency component derived from the decomposition was also considered as a characterization feature of the effective wavelength image. (4) The extracted features of wavelength spectral values, wavelet energy values, and texture features were utilized to construct feature input vectors in different ways, and based on the mentioned vectors, four detection models were then constructed: Partial Least Squares Regression (PLSR), Backpropagation Neural Network (BPNN), Random Forest (RF), and Extreme Gradient Boosting (XGBoost); and then these models were evaluated and compared according to their prediction capabilities to identify the optimal input vectors and prediction models. The research results indicate that the prediction capabilities of the four prediction models using single-class feature input vectors are all inferior to that of the input vectors fused with multi-class features; the optimal input vector is the feature fusion input vector, which incorporates the texture feature values and wavelet energy values from effective wavelength images as well as the spectral values corresponding to the feature wavelengths selected by CARS after LOWESS preprocessing. Among these models, the XGBoost model demonstrates the strongest prediction capabilities. The R²_c and RMSEC of the training set are respectively 0.998 08 and 0.022 49, and the R²_p and RMSEP of the testing set are 0.989 44 and 0.036 40, respectively. This research finding introduces a novel approach for the rapid detection of PAE content in Perilla Frutescens, and it also serves as a valuable reference for developing detection strategies for other components.

Key words：Hyperspectral; Perilla frutescens; Perilla aldehyde; Feature fusion; Wavelet transform; Prediction model

Received: 2023-06-28 Accepted: 2023-10-17

ZTFLH:

TS205.9

Corresponding Authors: YIN Yong E-mail: yinyong@haust.edu.cn

Cite this article:

SUN Jia-qi,YIN Yong,YU Hui-chun, et al. Hyperspectral Prediction Method for Perilla Aldehyde Content in Perilla Frutescens[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2024, 44(09): 2667-2674.

URL:

https://www.gpxygpfx.com/EN/10.3964/j.issn.1000-0593(2024)09-2667-08 OR https://www.gpxygpfx.com/EN/Y2024/V44/I09/2667

[1] Chinese Pharmacopoeia Commission（国家药典委员会）. Pharmacopoeia of the People's Republic of China (中华人民共和国药典）. Beijing: China Medical Science and Technology Press （北京：中国医药科技出版社）, 2020. 354.
[2] SUN Zong-bao, WANG Tian-zhen, ZOU Xiao-bo, et al（孙宗保, 王天真, 邹小波，等）. Food Science（食品科学）, 2021, 42(8): 257.
[3] HUANG Yi-qi, LIU Xiang-huan, HUANG Zhen-yu, et al（黄亦其，刘祥焕，黄震宇，等）. Transactions of the Chinese Society for Agricultural Machinery（农业机械学报）, 2023, 54(4): 259.
[4] Wan Guoling, Fan Shuxiang, Liu Guishan, et al. Food Control, 2023, 144: 109332.
[5] YANG Dong, WANG Shu-hui, WU Jian-hua, et al（杨东，王舒卉，吴建华，等）. Journal of the Chinese Cereals and Oils Association（中国粮油学报）, 2022, 37(11): 46.
[6] JIA Meng-meng, YIN Yong, YU Hui-chun, et al（贾梦梦，殷勇，于慧春，等）. Spectroscopy and Spectral Analysis（光谱学与光谱分析）, 2023, 43(3): 969.
[7] CHEN Jia-bao, GUO Long, WEN Chun-xiu, et al（陈家宝，郭龙，温春秀，等）. China Pharmacy（中国药房）, 2021, 32(8): 945.
[8] LIU Yun-hong, WANG Qing-qing, SHI Xiao-wei, et al（刘云宏，王庆庆，石晓微，等）. Transactions of the Chinese Society of Agricultural Engineering（农业工程学报）, 2019, 35(13): 291.
[9] ElMasry Gamal M, Nakauchi Shigeki. Biosystems Engineering, 2016, 142: 53.
[10] SONG Yan, ZHAO Lei, NING Jing-ming, et al（宋彦，赵磊，宁井铭，等）. Transactions of the Chinese Society of Agricultural Engineering（农业工程学报）, 2023, 39(2): 260.
[11] Li Jiangbo, Huang Wenqian, Tian Xi, et al. Computers and Electronics in Agriculture, 2016, 127: 582.
[12] CHEN Yan, XU Hai-li, XING Qiang, et al（陈妍，徐海黎，邢强，等）. Electronic Measurement Technology（电子测量技术）, 2022, 45(4): 114.
[13] Tao Feifei, Peng Yankun, Gomes Carmen L, et al. Journal of Food Engineering, 2015, 162: 38.
[14] Feng Luwei, Zhang Zhou, Ma Yuchi, et al. Remote Sensing, 2020, 12(12): 2028.
[15] LIU Qing-ru, MENG Lian-jun, ZHANG Xiao-juan, et al（刘青茹，孟连君，张晓娟，等）. Food Science（食品科学）, 2022, 43(24): 310.