Study on Near-Infrared Spectrum Acquisition Method of Non-Uniform Solid Particles
LI Hao-guang1,2, YU Yun-hua1,2, PANG Yan1, SHEN Xue-feng1,2
1. College of Mechanical and Contral Engineering, Shandong Institute of Petrochemical and Chemical Technology,Dongying 257061,China
2. New Energy College,China University of Petroleum(East China),Dongying 257061,China
Abstract:In the research of near-infrared spectroscopy (NIRS) qualitative analysis technology, the experimental objects mainly include liquid matter, powder matter and non-uniform solid particles. The liquid and powder materials are evenly distributed, and the spectrum collection and analysis are relatively easy. The non-uniform solid particles are different in size, shape and internal material distribution. Near-infrared spectroscopy of these samples contain the type information to be extracted in qualitative analysis and the individual difference information to be eliminated. Therefore, the analysis of non-uniform solid particles is more difficult than that of liquid or powder materials with uniform distribution. At present, there is no effective NIRS qualitative analysis method for non-uniform solid particles with different sizes and shapes at home and abroad. In this paper, non-uniform solid grain maize is taken as the research object. Based on the study of various spectral acquisition methods, the characteristics of non-uniform solid grain spectral acquisition are analyzed, and the spectral acquisition device for non-uniform solid grain is designed. In order to ensure the objectivity of the experimental results, Five pattern recognition methods such as Naive Bayesian classifier, k-nearest neighbor, support vector machine, BP neural network and Biomimetic Pattern Recognition were used to establish the qualitative analysis model of single grain maize by near-infrared spectroscopy in diffuse reflection and diffuse transmission mode, and the qualitative analysis models established in diffuse reflection and diffuse transmission mode were compared to analyze the effect of embryo orientation on the identification of single grain maize. The effect of the time interval between train and test sets on the identification accuracy under diffuse reflection and diffuses transmission is studied too. The experimental results show that the diffuse transmission model is not easily affected by the lying style of non-uniform solid grains, and the model has better generalization ability, which provides a feasible spectral acquisition method for subsequent research. Taking the non-uniform solid maize grain as the main experimental object, the research on its collection method and qualitative analysis model can provide a useful reference for the qualitative analysis of near-infrared spectroscopy of similar objects, which has important research significance.
[1] CHU Xiao-li, SHI Yun-ying, CHEN Bao, et al(褚小立,史云颖,陈 瀑,等). Journal of Instrumental Analysis(分析测试学报),2019, 38(5): 603.
[2] CHU Xiao-li(褚小立). User Manual of Near Infrared Spectroscopy(近红外光谱分析技术实用手册). Beijing: Machinery Industry Press(北京:机械工业出版社),2016.
[3] Jue T, Masuda K. Application of Near Infrared Spectroscopy in Biomedicine. Springer US, 2013.
[4] LU Wan-zhen(陆婉珍). Near Infrared Spectrometer(近红外光谱仪器). Beijing: Chemical Industry Press(北京:化学工业出版社), 2010.
[5] YAN Yan-lu, CHEN Bin, ZHU Da-zhou(严衍禄,陈 斌,朱大洲). Near Infrared Spectroscopy Analytical—Principles, Technology and Application(近红外光谱分析的原理、技术与应用). Beijing: China Light Industry Press(北京:中国轻工业出版社), 2007.
[6] Qin H, Ma J Y, Chen S J, et al. Infrared Technology,2015, 1(37): 78.
[7] LI Hao-guang, LI Wei-jun, QIN Hong, et al(李浩光,李卫军,覃 鸿,等). Transactions of the Chinese Society of Agricultural Machinery(农业机械学报),2016, 47(6): 259.
[8] LI Hao-guang, LI Wei-jun, QIN Hong, et al(李浩光,李卫军,覃 鸿,等). Transactions of the Chinese Society of Agricultural Machinery(农业机械学报),2017,(S1): 422.
[9] QIN Hong, MA Jing-yi, CHEN Shao-jiang, et al(覃 鸿,马竞一,陈绍江,等). Spectroscopy and Spectral Analysis(光谱学与光谱分析),2015, 25(11):1807.
[10] Stellan Ohlsson. Deep Learning: How the Mind Overrides Experience. Cambridge: Cambridge University Press, 2011.
[11] Wang S J, Lai J L. Neurocomputing, 2005, 67: 9.