Hyperspectral Identification Model of Cantonese Tangerine Peel Based on BWO-SVM Algorithm
LÜ Shi-lei1, 2, 3, WANG Hong-wei1, LI Zhen1, 2, 3*, ZHOU Xu1, ZHAO Jing1
1. College of Electronic Engineering (College of Artificial Intelligence), South China Agricultural University, Guangzhou 510642, China
2. Guangdong Artificial Intelligence and Digital Economy Laboraotry (Guangzhou), Guangzhou 510330, China
3. Division of Citrus Machinery, China Agriculture Research System of MOF and MARA, Guangzhou 510642, China
Abstract：In response to the problems, including Cantonese tangerine peel appearing on the market with shoddy and year of aging falsification, a hyperspectral identification method based on the Black Widow Optimization (BWO) algorithm that supports vector machine (SVM) was proposed to address these problems. In the current study. The Samples' hyper-spectral image data (385~1 014 nm) were collected with the Cantonese tangerine peel with four aging years (5~20 years) as the experimental object. The average spectral data of the sample region of interest were extracted by lens and reflectance calibration. Firstly, Savitzky-Golay Smoothing (SG), the Multiple Scattering Correction (MSC), and the Detrended Fluctuation Analysis (detrend) algorithm were utilized to perform spectral noise reduction for the data. Furthermore, the successive projections algorithm (SPA) and the competitive adaptive reweighting sampling mixed stepwise regression (CARS_SR) algorithm were used to extract the feature wavelengths. Finally, the root mean square error (RMSE) was proposed as the fitness function. The partial least-regression (PLS), the particle swarm optimization (PSO)-SVM, and the grasshopper optimization algorithm (GOA)-SVM were used to identify the aging year of Cantonese tangerine peel. Additionally, the identification model's optimal parameters were obtained using the BWO algorithm optimized SVM model (BWO-SVM). It was found that the SG_detrend algorithm has a relatively excellent noise reduction ability for the hyperspectral data of Cantonese tangerine peel. The feature wavelengths could be extracted via the CARS_SR algorithm. Compared with PLS, PSO-SVM, and GOA-SVM, more optimal control parameters for the identification model could be gained using BWO-SVM. The accuracy of 97.59%, RMSE of 0.060 2, and R2 of 0.952 9 for the identification of aged vintage Cantonese tangerine peel were achieved with the SVM model. This research provides a novel method to achieve rapid and nondestructive identification of aged vintage Cantonese tangerine peel and also provides a theoretical basis for the development of portable identification instruments and online production equipment.
Key words：Hyperspectral; Cantonese tangerine peel; Storage age; Support vector machine; Black widow optimization algorithm
[1] Li Xinqi，Yang Yahui，Zhu Yitian，et al. Food Chemistry，2022，384：1352519.
[2] Choi Moon-Young，Chai Chuan，Park Jeong Hill，et al. Journal of Pharmaceutical and Biomedical Analysis，2011，54(4)：638.
[3] CHAI Li-ping(柴利萍). China Synthetic Resin and Plastics (合成树脂及塑料)，2019，36(4)：115.
[4] YANG Fang，ZHONG Ying，ZHOU Hong-kun，et al(杨 方，钟 莹，周鸿昆，等). Laser & Optoelectronics Progress(激光与光电子学进展)，2017，54(3)：302.
[5] YU Mei，LI Jia-yi，FAN Wei，et al(余 梅，李嘉仪，范 伟，等). Food Research and Development(食品研究与开发)，2021，49(19)：171.
[6] Jaiswal G，Sharma A，Yadav S K. Wiley Interdisciplinary Reviews Data Mining and Knowledge Discovery，2021，11(6)：e1426.
[7] Zhang Xiaodong，Wang Pei，Wang Yafei，et al. Frontiers in Plant Science，2022，13：1035731.
[8] Liu Weiwei，Li Mengjie，Zhang Manyin，et al. Ecosystem Health and Sustainability，2020，6(1)：1726211.
[9] Cui Jiarui，Li Kenken，Hao Jie，et al. Food，2022，11(13)：1936.
[10] Mário C U A，Teresa C B S，Roberto K H G，et al. Chemometrics and Intelligent Laboratory Systems，2001，57(2)：65.
[11] Li Hongdong，Liang Yizeng，Xu Qingsong，et al. Analytica Chimica Acta，2009，648(1)：77.
[12] Seyedeh Z M，Seyedali M，Shahrzad S，et al. Applied Intelligence，2018，48：805.
[13] Adrián F P，Hernán P，Juan H A，et al. Mathematical Problems in Engineering，2020，2020：8856040.
[14] LIU Mei-chen，XUE He-ru，LIU Jiang-ping，et al(刘美辰，薛河儒，刘江平，等). Spectroscopy and Spectral Analysis(光谱学与光谱分析)，2022，42(5)：1601.
