Detection of Purple Rice Adulteration by Terahertz Time Domain Spectroscopy
LIU Yan-de, DU Xiu-yang, LI Bin, ZHENG Yi-lei, HU Jun, LI Xiong, XU Jia
School of Mechatronics Engineering, East China Jiaotong University, National and Local Joint Engineering Research Center of Fruit Intelligent Photoelectric Detection Technology and Equipment, Nanchang 330013, China
Abstract：Purple rice is a common ingredient in life and has rich nutritional value. Due to the high price of purple rice, the dyed purple rice has entered the market in large quantities. In this paper, terahertz time-domain spectroscopy combined with chemometric methods is used to explore the rapid detection method of purple rice adulteration. The spectral data of purple rice adulteration in the range of 0～7 THz was collected by Terahertz Time domain Spectroscopy (THz-TDS), and the absorption coefficient spectrum and refractive index spectrum of 0.5～2.5 THz band were selected for analysis and adopted. The chemometric method models and analyzes the spectral data. Savitzky-Golay convolution smoothing (SG smoothing), baseline correction (Baseline), normalization (Normalization), multiple scattering correction (MSC) and other methods are used for spectral preprocessing. Qualitative analysis of purple rice, purple rice mixed with rice and purple rice mixed with black rice was carried out by partial least squares decision analysis (PLS-DA). Qualitative analysis showed that there were significant differences in the plane distribution of the three samples by Principal Component Analysis (PCA); the PLS-DA model established by baseline corrected spectral data had the best effect, and the false positive rate was 0. Then using partial least squares (PLS) combined with SG smoothing, Baseline, Normalization, MSC and other pretreatment methods to establish a PLS quantitative model for the spectral data of the black rice mixed with dyed rice and purple rice. The results showed that the PLS model with baseline correction pretreatment method had the best effect. The correlation coefficient of the prediction set of purple rice-doped rice was 0.936, and the root means square error of prediction (RMSEP) was 0.095. The correlation coefficient of the prediction set of purple rice blended black rice was 0.914, and the root mean square error of the prediction set was 0.096. In order to compare and analyze the prediction accuracy of linear (PLS) and nonlinear (LS-SVM) quantitative model methods, the least squares support vector machine (least squares support vector) is established by using the same pretreatment method. Machine, LS-SVM) predictive model, using radial basis function (RBF) as the kernel function. The results showed that the LS-SVM model had the best effect after baseline correction. RMSEP of the predicted rice with purple rice was 0.092, and the correlation coefficient (Rp) of the prediction set was 0.979. RMSEP of the meter is 0.093, and the prediction set correlation coefficient (Rp) is 0.948. The comparison found that the LS-SVM prediction model for the content of purple rice adulteration is better and more accurate than the PLS model. Studies have shown that terahertz time-domain spectroscopy combined with chemometric methods can provide a fast and accurate analytical method for qualitative and quantitative analysis of purple rice adulteration.
Key words：Terahertz time-domain spectroscopy; Purple rice; Partial least squares; Least squares support vector machine
刘燕德，杜秀洋，李 斌，郑艺蕾，胡 军，李 雄，徐 佳. 太赫兹时域光谱技术对紫米掺假的检测研究[J]. 光谱学与光谱分析, 2020, 40(08): 2382-2387.
LIU Yan-de, DU Xiu-yang, LI Bin, ZHENG Yi-lei, HU Jun, LI Xiong, XU Jia. Detection of Purple Rice Adulteration by Terahertz Time Domain Spectroscopy. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2020, 40(08): 2382-2387.
[1] ZHAO Jia（赵 佳）. China Food Safety Magazine（食品安全导刊）, 2018，(12): 105.
[2] Josette El Haddad, Frederick de Miollis, Joyce Bou Sleiman，et al. Analytical Chemistry，2014, 86：4927.
[3] HUANG Li-juan, ZHANG Xin, WANG Guo, et al（黄丽娟, 张 欣, 王 果，等）. Spectroscopy and Spectral Analysis（光谱学与光谱分析）, 2016, 36(S1): 79.
[4] YIN Ming（殷 明）. Identification and Adulteration Detection of Edible Oils Based on Broad Band Terahertz Spectrum and Stoichiometry（基于宽频太赫兹光谱和化学计量学的食用油脂鉴别及掺假检测研究）. China University of Mining and Technology（中国矿业大学）, 2017.
[5] LI Bin, DU Xiu-yang, LIU Yan-de, et al（李 斌, 杜秀洋, 刘燕德，等）. Laser and Optoelectronics Progress（激光与光电子学进展），2019, (20): 322.
[6] Liu W, Zhang Y, Yang S, et al. Spectrochimica Acta Part A: Molecular and Biomolecular Spectroscopy, 2018, 196: 123.
[7] Dorney T D, Baraniuk R G, Mittleman D M. Journal of the Optical Society of America A-Optics Image Science and Vision, 2001, 18(7): 1562.
[8] Duvillaret L, Garet F, Coutaz J L. Applied Optics, 1999, 38(2): 409.
[9] YAN Yan-lu(严衍禄). Basic and Application of Near Infrared Spectroscopy(近红外光谱分析基础与应用) Beijing：China Light Industry Press(北京：中国轻工业出版社)，2005. 8.
[10] CHU Xiao-li（褚小立）. Molecular Spectroscopy Analytical Technology Combined with Chemometrics and Its Applications（化学计量学方法与分子光谱分析技术）. Beijing：Chemical Industry Press(北京：化学工业出版社)，2011. 2.
[11] HAO Yong, SUN Xu-dong, GENG Xiang（郝 勇, 孙旭东, 耿 响）. Food Science（食品科学）, 2013, 34(18): 137.
[12] OUYANG Ai-guo, ZHANG Yu, CHENG Meng-jie，et al（欧阳爱国, 张 宇, 程梦杰，等）. Chinese Optics（中国光学）, 2017, 10(6): 752.
