Correction of Temperature Influence in Near Infrared Spectroscopy
SUN Yan-hua1, 2, FAN Yong-tao1, 2*
1. Laboratory of Micro-Nano Optoelectronic Materials and Devices, Key Laboratory of Materials for High-Power Laser, Shanghai Institute of Optics and Fine Mechanics, Chinese Academy of Sciences, Shanghai 201800, China
2. Center of Materials Science and Optoelectronics Engineering,University of Chinese Academy of Sciences, Beijing 100049, China
Abstract:For the problem that the temperature change of the sample affects the prediction result of the model, firstly, the spectrum of the same sample at different temperatures is compared with the spectrum of the same sample at the same temperature. The results show that the spectral difference at different temperatures is large. Then the effect of sample temperature on the prediction of corn crude protein model was studied. Spectral collection of samples with a crude protein content of 6.04% was performed at different temperatures, and near-infrared spectra at different temperatures were pretreated in the same way as those used in modeling, so as to eliminate the influence of factors other than the temperature on the spectra. The pre-processed spectrum is substituted into the established model for prediction. The prediction results show that the difference between the predicted result and the measured value increases as the difference between the spectral temperature and the modeled temperature increases, and the maximum error is 1.12%. In order to solve the influence of temperature on the prediction results of the model, we further analyzed the relationship between temperature and spectral data at different temperatures, and found that after removing the areas with serious noise at both ends of the spectrum, there was a certain linear relationship between spectral data at the same wavelength point at different temperatures. According to this finding, a temperature correction theory is proposed. Taking the spectrum at the time of modeling as the reference spectrum, and then using the linear regression algorithm to perform linear regression on the spectra of different wavelength points according to the linear relationship between temperature and spectrum, the difference between the spectrum at different temperatures and the reference spectrum is obtained. Finally, the spectra at different temperatures are corrected to the reference spectrum. After the spectrum is corrected by the theory, the difference between the spectra has been greatly improved compared with before the correction. The corrected spectrum is brought into the model, and most of the prediction results are improved, which meets the requirements of ±0.5% of the national standard. Finally, the temperature correction theory was verified by using 34 different samples unrelated to the modeling. The model prediction values and standard physical and chemical value determination coefficients of the crude protein before and after the spectral correction were 0.910 and 0.982, respectively, and the root means square error was 0.558 and 0.172, and the average relative error was 6.05% and 1.75%, respectively. The temperature correction theory has been temperature-corrected from the nature of near-infrared spectroscopy, providing a reference for temperature correction of other samples, which is beneficial to the promotion of handheld near-infrared spectroscopy.
Key words:Near-infrared spectroscopy; Temperature correction; Linear regression; Crude protein measurement
孙彦华,范永涛. 近红外光谱分析中温度影响的修正[J]. 光谱学与光谱分析, 2020, 40(06): 1690-1695.
SUN Yan-hua, FAN Yong-tao. Correction of Temperature Influence in Near Infrared Spectroscopy. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2020, 40(06): 1690-1695.
[1] HUANG Ya-wei, LI Huan, WANG Ruo-lan(黄亚伟,李 换,王若兰). Cereals & Oils(粮食与油脂), 2017, 30(7): 2.
[2] Fu Xiaping, Kim Moon S, Chao Kuanglin, et al. Journal of Food Engineering, 2014, 124: 97.
[3] Jan U Porep, Dietmar R Kammerer, Reinhold Carle. Journal of Food Engineering, 2015, 46: 211.
[4] Shi Z, Ji W, Viscarra Rossel R A, et al. European Journal of Soil Science, 2015, 66:679.
[5] XU Xiu-qin, CHEN Guo, ZHANG Hao, et al(许秀琴, 陈 国, 章 豪, 等). Anhui Chemical Industry(安徽化工), 2017, 43(4): 7.
[6] WANG Fan, LI Yong-yu, PENG Yan-kun, et al(王 凡,李永玉,彭彦坤,等). Transactions of the Chinese Society for Agricultural Machinery(农业机械学报), 2018, 49(7): 348.
[7] REN Dong, QU Fang-fang, LU An-xiang, et al(任 东, 瞿芳芳, 陆安详, 等). Near Infrared Spectroscopy and Its Application(近红外光谱分析技术与应用). Beijing: Science Press(北京: 科学出版社), 2016. 51.
[8] WANG Dong, XIONG Yan-mei, HUANG Rong, et al(王 冬,熊艳梅,黄 蓉,等). Chinese Journal of Analytical Chemistry(分析化学),2010,38(9):1313.
[9] Li Zhe, Zhou Mei, Luo Yongshun. Talanta, 2016, 155: 47.
[10] Wang H L, Peng J Y, Xie C Q, et al. Sensors, 2015, 15(5): 11889.
[11] GUO Zhi-ming, CHEN Quan-sheng, ZHANG Bin, et al(郭志明, 陈全胜, 张 彬, 等). Transactions of the Chinese of Agricultural Engineering(农业工程学报), 2017, 33(8): 245.
[12] GUO Ying-shi, CAO Xiao-yan, ZOU Hang-jun, et al(郭应时, 曹小彦, 邹杭君, 等). Food & Machinery(食品与机械), 2017, 33(11):67.
[13] WANG Qi(王 琦). Journal of Changzhi University(长治学院学报), 2018, 35(2): 59.
[14] GB/T18868—2002, National Standards of the People’s Republic of China(中华人民共和国国家标准). Method for Determination of Moisture, Crude Protein, Crude Fat, Crude Fibre, Lysine and Methinione in Feeds-Near Infrared Reflectance Spectroscopy(饲料中水分、粗蛋白质、粗纤维、粗脂肪、赖氨酸、蛋氨酸快速测定近红外光谱法).