A Method for Correcting Nitrofurantoin Raman Signal in Honey Based on Internal Standard of Substrate
YAN Shuai1, LI Yong-yu1*, PENG Yan-kun1, LIU Ya-chao1, HAN Dong-hai2
1. College of Engineering, China Agricultural University, National Research and Development Center for Agro-Processing Equipment, Beijing 100083, China
2. College of Food Science and Nutritional Engineering, China Agricultural University, Beijing 100083, China
Abstract：In order to solve the problem of poor reproducibility of surface-enhanced Raman spectroscopy signals, this paper explored the surface-enhanced Raman spectroscopy correction method based on internal honey standard using the Raman point detection system built by the laboratory. Firstly, the Raman characteristic displacement at 739 cm-1 was determined to be the standard internal peak of honey by surface enhanced Raman spectra of honey samples and Raman spectra of standard products of nitrofurantoin. The ratio method was used to correct the Raman characteristic peak strength of nitrofurantoin at 1 353 and 1 612 cm-1 for quantitative analysis in honey. The surface-enhanced Raman spectra of nitrofurantoin honey samples with a concentration of 20 mg·kg-1 were collected for 30 times under the same conditions, and the peak intensity relative standard deviations of nitrofurantoin at 1 353 and 1 612 cm-1 was 11.515 6% and 11.162 5%, respectively. However, after using the Raman characteristic peak intensity at 739 cm-1 as the internal standard to correct the peak intensity of nitrofurantoin at 1 353 and 1 612 cm-1, the relative standard deviations were reduced to 4.852 6% and 4.733 2%, respectively. The repeatability and stability of the surface-enhanced Raman characteristic peaks are significantly improved. Because the instrument system errors and surface enhanced during uncontrollable factors and errors human-induced surface of the sample enhanced 739 cm-1 honey characteristic peak intensity spectrum and at 1 353 and 1 612 cm-1 nitro nitrofurantoin characteristic peak intensity the effects are the same, it is possible to effectively eliminate the difference between the Raman signals and decrease the stability or poor repeatability problem by internal standard ratio method. Because the error of the instrument system and the artificial error caused by uncontrollable factors in the surface enhancement process have the same effect on the peak strength of 739 cm-1 honey characteristic peak and the peak strength of 1 353 and 1 612 cm-1 nitrofurantoin characteristic peak in the sample surface enhancement spectrum, therefore, the internal label ratio method can effectively eliminate or reduce the problem of Raman signal stability and poor repeatability. Finally, 69 honey samples with a nitrofurantoin concentration range of 0.4~20 mg·kg-1 were collected. Based on Raman characteristic peak intensity at 1 353 and 1 612 cm-1 of nitrofurantoin and Raman characteristics at 739 cm-1 of honey, The linear regression prediction model and the multi-linear regression model were established respectively, in which the unary linear regression model based on the internal standard of honey at 739 cm-1 to correct the Raman characteristic peak intensity at 1 612 cm-1 of nitrofurantoin with higher precision and predictability. In this model, the determination coefficient of the calibration set and validation set is 0.971 2 and 0.969 6 respectively, and the root means square error of calibration set and validation set are 1.115 1 and 1.242 2 respectively, and the relative analysis error is 4.306 0. The results show that the sample of the underlying itself holds the inherent internal criteria without adding additional internal markers, and the simple internal standard ratio method can effectively eliminate the effect of the instrument’s system error and the mixing time between surface enhancers and samples on the Raman signal strength, and significantly improve the repeatability and stability of the Raman characteristic signal. It provides a technical reference for the quantitative analysis of surface-enhanced Raman spectra.
闫 帅，李永玉，彭彦昆，刘亚超，韩东海. 基于底物内标的蜂蜜中硝基呋喃妥因拉曼信号校正方法[J]. 光谱学与光谱分析, 2021, 41(02): 546-551.
YAN Shuai, LI Yong-yu, PENG Yan-kun, LIU Ya-chao, HAN Dong-hai. A Method for Correcting Nitrofurantoin Raman Signal in Honey Based on Internal Standard of Substrate. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2021, 41(02): 546-551.
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