Near-Infrared Spectroscopy Detection of Pollution Concentration of Agricultural Machinery Lubricating Oil Based on Improved Random
Frog Algorithm
HAN Jia-qing1, ZHOU Gui-xia1*, HU Jun1*, CHENG Jie-hong2, CHEN Zheng-guang2, ZHAO Sheng-xue1, LIU Yi-ling1
1. College of Engineering, Heilongjiang Bayi Agricultural University, Daqing 163319, China
2. College of Electrical and Information, Heilongjiang Bayi Agricultural University, Daqing 163319, China
Abstract：The use of lubricating oil is necessary for the normal operation of agricultural machinery. The power performance, safety, economy and life of agricultural machinery engines are closely related to the condition of lubricating oil. The pollution concentration is the comprehensive evaluation index of oil, routine laboratory testing takes a long time and costs a lot, so it is of great significance to develop efficient detection technology for lubricating oil pollution concentration. This paper takes agricultural machinery lubricating oil as the research object. A method for detecting pollution concentration of agricultural machinery lubricating oil based on near-infrared spectroscopy is proposed. At the same time, aiming at the shortcomings of the Random Frog (RF) feature wavelength selection algorithm, such as a large number of iterations and low reproducibility, and iteratively retains informative variables-Random Frog (IRIV-RF) feature wavelength selection algorithm is proposed. On the one hand, IRIV-RF uses the iteratively retains informative variables (IRIV) algorithm to filter the strong and weak information variables. It is used as the initial variable subset of RF to eliminate the effect of the randomness of the initial variable set on the reproducibility of the results. On the other hand, IRIV-RF builds a Partial least squares regression (PLSR) model by arranging the variables in descending order of the selected probability values and then adding one wavelength at a time, starting with the first. The variable subset with the minimum Root Mean Square Error of Cross Validation (RMSECV) value is selected as the characteristic wavelength to eliminate the uncertainty of the number of characteristic wavelengths extracted by the RF algorithm. The original spectrum data of 101 samples of agricultural machinery lubricating oil with different pollution concentrations are collected by near-infrared spectrometer. Three different pretreatment methods are used to process the original spectrum, and the optimal pretreatment method is Standard Normal Variate (SNV). On this basis, the characteristic wavelength of the whole spectrum is selected by RF, IRIV and IRIV-RF algorithms, and the PLSR model is established. By comparing the prediction accuracy of full-spectrum PLSR, RF-PLSR, IRIV-PLSR and IRIV-RF-PLSR models, the results show that the prediction accuracy of the PLSR model based on the IRIV-RF algorithm is the highest, the Correlation Coefficient of Prediction (Rp) is 0.965 7 and the Root Mean Square Error of Prediction (RMSEP) is 9.058 4. It significantly improves the prediction accuracy and operation efficiency, reducing the model’s complexity. It is proved that the proposed IRIV-RF algorithm is an effective characteristic wavelength selection algorithm, and the feasibility of near-infrared spectroscopy combined with the improved IRIV-RF algorithm to detect the pollution concentration of agricultural machinery lubricating oil is proved, which provides a new idea for identifying the quality of lubricating oil.
韩嘉庆，周桂霞，胡 军，程介虹，陈争光，赵胜雪，刘奕伶. 改进的随机蛙跳算法对农机润滑油污染浓度的近红外光谱检测研究[J]. 光谱学与光谱分析, 2022, 42(11): 3482-3488.
HAN Jia-qing, ZHOU Gui-xia, HU Jun, CHENG Jie-hong, CHEN Zheng-guang, ZHAO Sheng-xue, LIU Yi-ling. Near-Infrared Spectroscopy Detection of Pollution Concentration of Agricultural Machinery Lubricating Oil Based on Improved Random
Frog Algorithm. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2022, 42(11): 3482-3488.
[1] Balabin R M, Safieva R Z, Lomakina E I. Microchemical Journal, 2011, 98(1): 121.
[2] Alves J, Poppi R J. Analytical Methods, 2013, 5(22): 6457.
[3] LIU Chen-yang, TANG Xing-jia, YU Tao, et al(刘晨阳, 唐兴佳，于 涛，等). Spectroscopy and Spectral Analysis(光谱学与光谱分析), 2020, 40(5): 1634.
[4] CHEN Bin, LIU Ge(陈 彬，刘 阁). Acta Photonica Sinica(光子学报), 2014, 43(2): 230001.
[5] ZHANG Yu, WU Di, HE Yong, et al(张 瑜，吴 迪，何 勇，等). Infrared(红外), 2011, 32(12): 39.
[6] Yun Y H, Li H D, Deng B C, et al. Trends in Analytical Chemistry, 2019, 113(7): 102.
[7] Li H D, Xu Q S, Liang Y Z. Analytica Chimica Acta, 2012, 740: 20.
[8] CHEN Li-dan，ZHAO Yan-ru(陈立旦，赵艳茹). Transactions of the Chinese Society of Agricultural Engineering(农业工程学报), 2014, 30(8): 168.
[9] Yun Y H, Wang W T, Tan M L, et al. Analytica Chimica Acta, 2014, 807(17): 36.
[10] Zheng X, Li Y, Wei W, et al. Meat Science, 2019, 149(3): 55.
[11] Chen Y, Luo P, Zhao Z Y, et al. Physics Letters A, 2017, 381(40): 3472.
[12] XIE Yue, LI Fei-yue, FAN Xing-jun, et al(谢 越，李飞跃，范行军，等). Chinese Journal of Analytical Chemistry(分析化学), 2018, 46(4): 609.
[13] LONG Yan, LIAN Ya-ru, MA Min-juan, et al(龙 燕，连雅茹，马敏娟，等). Transactions of the Chinese Society of Agricultural Engineering(农业工程学报), 2019, 35(13): 270.
[14] Yu H W, Liu H Z, Wang N, et al. Analytical Methods, 2016, 8(41)：7482.
