A Study on the Screening of Anti-Inflammatory Drug Diclofenac Sodium in Dietary Supplements by Near Infrared Hyperspectral Imaging
WANG Cheng1, YU Hang1, YAO Wei-rong1, CHENG Yu-liang1, GUO Ya-hui1, QIAN He1, TAN Zhi-qiang2, XIE Yun-fei1*
1. School of Food Science and Technology,Jiangnan University,Wuxi 214122, China
2. State Key Laboratory of Environmental Chemistry and Ecotoxicology, Research Center for Eco-Emironmental Science, Chinese Academy of Sciences, Beijing 100085, China
Abstract:Diclofenac sodium is a non-steroidal anti-inflammatory drug with good anti-inflammation effect. The addition of diclofenac sodium is prohibited from being added to the dietary as supplements for reducing the inflammation. At present, some literature has reported that diclofenac sodium in water, meat, milk and other Chinese drugs can be quantified by using high performance liquid chromatography, surface enhanced Raman and electrochemical methods; however, all the mentioned techniques normally require a relatively complex pretreatment process, complicated operation steps and time-consuming. It is necessary to develop a rapid and nondestructive method for determing diclofenac sodium in dietary supplements. This article developeda near-infrared hyperspectral imaging techniqueat 1 000~2 524 nm combined with chemometrics analysis method for determing diclofenac sodium concentration in the dietary supplements. In this study, eight spectral pretreatment methods were established by using partial least squares regression (PLSR) and principal component regression models (PCR) based on the full spectral wavelength. Furthermore, β coefficients were used to select the optimal bands in order to improve the accuracy and stability of the model. The multiple linear regression model (MLR) of diclofenac sodium was established at 1 130~1 147, 1 412~1 468, 1 658~1 709, 2 010~2 055, 2 122~2 178, 2 395~2 423 nm as the independent variable. Subsequently, the three models were compared, and results showed that multiple linear regression model established by standard normal variables pretreatment method had the best forecasting ability. The minimum predictive value of the model accuracy was 0.05%; the Pearson coefficient R2 of the predicted values was 0.992 5; the root means square error of prediction was 0.004 9, and the standard deviation of prediction was 0.004 9. Therefore, the study has developed the near-infrared hyperspectral imaging technique for rapid determination of diclofenac sodium as an anti-inflammatory drug inthe dietary supplements provides a theoretical basis. It is expected to further developed and extended to the rapid quantitative application of other prohibited added drugs.
Key words:Near infrared hyperspectral image; Dietary supplements; Diclofenac sodium; Multiple linear regression model
王 成,于 航,姚卫蓉,成玉梁,郭亚辉,钱 和,谭志强,谢云飞. 近红外高光谱成像技术在筛查保健食品中违禁添加抗炎药物双氯芬酸钠的研究[J]. 光谱学与光谱分析, 2021, 41(02): 592-598.
WANG Cheng, YU Hang, YAO Wei-rong, CHENG Yu-liang, GUO Ya-hui, QIAN He, TAN Zhi-qiang, XIE Yun-fei. A Study on the Screening of Anti-Inflammatory Drug Diclofenac Sodium in Dietary Supplements by Near Infrared Hyperspectral Imaging. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2021, 41(02): 592-598.
[1] Sathishkumar P, Meena RAA, Palanisami T, et al., Science of The Total Environment, 2019, 9697(19): 34034.
[2] Dubreil-Cheneau E, Pirotais Y, Bessiral M, et al. J. Chromatogr A, 2011, 1218(37): 6292.
[3] Park J A, Abd El-Aty A M, Zheng W, et al. Biomed Chromatogr, 2018, 32(6): 4215.
[4] Davarani S S, Pourahadi A, Nojavan S, et al. Analytica Chimica Acta, 2012, 722: 55.
[5] Lonappan L, Brar S K, Das R K, et al. Environ. Int., 2016, 96: 127.
[6] Deng D, Yang H, Liu C, et al. Sensors and Actuators B: Chemical, 2019, 283: 563.
[7] Wang B, Liu G, Dou Y, et al. J. Pharm. Biomed. Anal., 2009, 50(2): 158.
[8] Al-Sarayreh M,Reis M M, Yan W Q, et al. Journal of Imaging, 2018, 4(5): 63.
[9] Fu X, Kim M S, Chao K, et al. Journal of Food Engineering, 2014, 124: 97.
[10] Huang Y, Min S, Duan J, et al. Food Chem., 2014, 145: 278.
[11] Huang M, Kim M S, Delwiche S R, et al. Journal of Food Engineering, 2016, 181: 10.
[12] Cheng J H, Jin H, Xu Z, et al. Analytical Methods, 2017, 9(43): 6148.
[13] Tschannerl J, Ren J C, Jack F, et al. Food Chemistry, 2019, 270: 105.
[14] Guo L, Yu Y, Yu H, et al. J. Sci. Food Agric., 2019, 99(12): 5558.
[15] Chen S, Zhang F, Ning J, et al. Food Chem., 2015, 172: 788.
[16] Wold S, Sjostrom M, Eriksson L. Chemometrics and Intelligent Laboratory Systems, 2001, (58): 109.
[17] Kandpal L M, Tewari J, Gopinathan N, et al. Analytical Chemistry, 2016, 88(22): 11055.
[18] Ozaki Y. Analytical Sciences, 2012, 28(6): 545.