Classification of Qianxi Tomatoes by Visible/Near Infrared Spectroscopy Combined With GMO-SVM
ZHANG Fu1, 2, 3, WANG Xin-yue2, CUI Xia-hua2, CAO Wei-hua2, ZHANG Xiao-dong1*, ZHANG Ya-kun2
1. Key Laboratory of Modern Agricultural Equipment and Technology of Ministry of Education, Jiangsu University, Zhenjiang
212013, China
2. College of Agricultural Equipment Engineering, Henan University of Science and Technology, Luoyang 471003, China
3. Collaborative Innovation Center of Advanced Manufacturing of Machinery and Equipment of Henan Province, Luoyang 471003, China
Abstract：Qianxi tomatoes are rich in nutrition, tasting sweet, sour and delicious, different varieties of qianxi tomato’s flavor and nutritional value is obviously different, especially lycopene, citric acid, vitamin C and amino acid content varies greatly and the traditional artificial classification method of low efficiency, strong subjectivity, high rate of error detection and other issues are pressing to be solved. Therefore, in order to screen the high comprehensive nutritional value and good flavor of the qianxi tomatoes to achieve the rapid and accurate classification of the qianxi tomatoes, a classification model based on qianxi tomatoes spectral features and a GWO optimized SVM algorithm was proposed to solve the problem of automated qianxi tomatoes classification. In this study, a total of 240 qianxi tomatoes of four varieties were taken as the research objects, divided into 160 training sets and 80 test sets according to the ratio of 2∶1. The qianxi tomatoes fruit reflective intensity in the range of 350 to 1 000 nm was obtained by using a visible/near-infrared spectral acquisition system, and the sample reflectance by spectrally corrected was obtained and analyzed. The effective information of the qianxi tomatoes spectrum in the range of 481.15 to 800.03 nm was intercepted to enhance the signal-to-noise ratio. Since the modeling effect is affected by the interference of irrelevant information in the data acquisition process, Savitzky-Golay (SG) smoothing pretreatment was performed with the smoothing point to 3. After SG smoothing pretreatment, the characteristic wavelength variables are extracted by successive projections algorithm (SPA), the reflectance of the optimal selected 11 characteristic wavelength variables as the input matrix X, preset sample variables 1, 2, 3, and 4 as output matrix Y, the SPA-SVM qualitative classification model of qianxi tomatoes was established. The average classification accuracy of the training set is 59.38%, the test set is 48.75%. On this basis, the gray wolf optimization (GWO) algorithm was introduced to train 160 samples training set, seeking the optimal penalty coefficient (c) and the nuclear function parameter (g) of the SVM. Based on the training results of the model, the classification results of 80 test set samples were predicted to establish the SPA-GWO-SVM qualitative classification model of qianxi tomatoes and the average classification accuracy of the training set is 100%, the test set is 81.25%. The research results show that the performance of the support vector machine model optimized by the grey wolf algorithm has been improved significantly. The average classification accuracy of the training set is improved by 40.62%, and the average classification accuracy of the test set is improved by 32.50%, which shows that the gray wolf optimization algorithm can be used to improve the performance of the support vector machine classification model and realize the classification of qianxi tomatoes. This study provides a new idea and method for the rapid and accurate classification of qianxi tomatoes and other fruits and vegetables.
Key words：Visible/near-infrared spectroscopy; Qianxi tomato; Classification; Grey wolf optimization; Support vector machine
[1] CHANG Pei-pei, LIANG Yan, ZHANG Jing, et al(常培培, 梁 燕, 张 静, 等). Food Science(食品科学), 2014, 35(22): 215.
[2] ZHOU Yan-chao, XUE Kun, GE Hai-yan, et al(周艳超，薛 坤，葛海燕，等). Acta Agriculturae Zhejiangensis(浙江农业学报)，2021，33(12)：2320.
[3] CHENG Yuan, WAN Hong-jian, LIU Chao-chao, et al(程 远, 万红建, 刘超超, 等). Journal of Zhejiang Agricultural Sciences(浙江农业学报), 2018, 30(11): 1859.
[4] CHENG Yuan, WAN Hong-jian, YAO Zhu-ping, et al(程 远, 万红建, 姚祝平, 等). Journal of Nuclear Agricultural Sciences(核农学报), 2019, 33(11): 2177.
[5] Villaseñor-Aguilar M J, Botello-álvarez J E, Pérez-Pinal F J, et al. Journal of Sensors, 2019, 2019: 1.
[6] Opeña H J G, Yusiong J P T. Malaysian Journal of Computer Science, 2017, 30(1): 12.
[7] ZHOU Yun-cheng, XU Tong-yu, ZHENG Wei, et al(周云成, 许童羽, 郑 伟, 等). Transactions of the Chinese Society of Agricultural Engineering(农业工程学报), 2017, 33(15): 219.
[8] GENG Lei, HUANG Ya-long, GUO Yong-min(耿 磊，黄亚龙，郭永敏). Transactions of the Chinese Society for Agricultural Machinery(农业机械学报)，2022，53(6)：304，369.
[9] LI Hong-qiang, SUN Hong, LI Min-zan(李鸿强, 孙 红, 李民赞). Journal of Instrumental Analysis(分析测试学报), 2020, 39(11): 1421.
[10] ZHAO Jie-wen, HU Huai-ping, ZOU Xiao-bo(赵杰文, 呼怀平, 邹小波). Transactions of the Chinese Society of Agricultural Engineering(农业工程学报), 2007, 23(4): 149.
[11] ZHANG Xiao-feng, WANG Xiu-ying(张晓凤, 王秀英). Computer Science(计算机科学), 2019, 46(3): 30.
[12] SUN Jun, MO Yun-nan, DAI Chun-xia, et al(孙 俊, 莫云南, 戴春霞, 等). Transactions of the Chinese Society of Agricultural Engineering(农业工程学报), 2018, 34(14): 188.
[13] AN Juan-hua, DONG Xin, WANG Ke-jian, et al(安娟华, 董 鑫, 王克俭, 等). Crop Magazine(作物杂志), 2021, 37(1): 200.
[14] WU Xin-yan, BIAN Xi-hui, YANG Sheng, et al(武新燕, 卞希慧, 杨 盛, 等). Journal of Instrumental Analysis(分析测试学报), 2020, 39(10): 1288.
[15] CHU Song-feng, ZHAO Feng-xia, FANG Shuang, et al(楚松峰, 赵凤霞, 方 双, 等). Food & Machinery(食品与机械), 2021, 37(1): 156.
[16] LONG Wen, CAI Shao-hong, JIAO Jian-jun, et al(龙 文, 蔡绍洪, 焦建军). Acta Electronica Sinica(电子学报), 2019, 47(1): 169.
[17] PENG Yao, BIAN Xi-hui, WANG Zi-fang, et al(鹏 瑶, 卞希慧, 王姿方, 等). Spectroscopy and Spectral Analysis(光谱学与光谱分析), 2019, 39(9): 2800.
[18] PENG Yan-kun, ZHAO Fang, LI Long, et al(彭彦昆, 赵 芳, 李 龙, 等). Transactions of the Chinese Society of Agricultural Engineering(农业工程学报), 2018, 34(5): 159.
[19] LUO Xia, HONG Tian-sheng, LUO Kuo, et al(罗 霞, 洪添胜, 罗 阔, 等). Laser & Optoelectronics Progress(激光与光电子学进展), 2015, 52(8): 315.
