Study on Quantitative Analysis of Edible Oil Peroxide Value by Terahertz Time Domain Spectroscopy
LIU Cui-ling1, YANG Yu-fei1*, TIAN Fang2, WU Jing-zhu1, SUN Xiao-rong1
1. Beijing Key Laboratory of Big Data Technology for Food Safety, Beijing Technology and Business University, Beijing 100048, China
2. School of Foreign Languages, Beijing Technology and Business University, Beijing 100048, China
Abstract：At present, Terahertz (THz) spectroscopy techniques are mainly used for qualitative analysis, but the application of THz technology can hardly be found in quantitative analysis in the detection of edible oil’s quality. This paper presents an approach to analyze edible oil quality based on Attenuated Total Reflection (ATR) and Terahertz Time Domain Spectroscopy(THz-TDS). Firstly, the THz-TDS of edible oil samples with different types and degrees of oxidation were collected, the effective signal band was filtered and the optical constants were extracted, the preprocessing algorithm corrected the optical constants, a variety of chemometrics methods were used to establish quantitative analysis models, in order to quickly and accurately predicted the peroxide value of edible oils. 70 experimental samples were used, including soybean oils, rapeseed oils and corn oils, the peroxide value ranged from 0.41 to 10.23 mmol·kg-1, and the peroxide value distribution of the samples was evenly distributed. A TeraPulse 4000 terahertz pulse spectroscopy system equipped with an ATR detection module belonging to TeraView was used to collect samples’ THz-TDS signals. According to THz-TDS characteristics, the effective band 10 to 86.78 cm-1 was selected for modeling analysis. The frequency domain signals were obtained by fast Fourier transform, and the optical constants were extracted: refractive index and absorption coefficient. Refractive index and absorption coefficient were preprocessed separately through Savitzky-Golay 7-points convolution smoothing, which had achieved the purpose of removing interference signals. The SPXY algorithm was used to divide the calibration set, and prediction set samples in a 3∶1 ratio. The peroxide value analysis models based on refractive index and absorption coefficient were established by the principal component regression algorithm and partial least square algorithm. The root mean square error and correlation coefficient of the model evaluation indexes were analyzed, the peroxide value analysis model based on the refractive index was modeled by partial least squares algorithm had ideal prediction accuracy. When the optimal principal component number was selected to be 6, RMSEC is 0.168%，R2 is 0.981，RMSEP is 0.231%，r2 is 0.977. The principal component regression algorithm modeled the peroxide value analysis model based on the absorption coefficlent, and the prediction model had the best robustness. When the optimal principal component number was selected to be 10, RMSEC is 0.192%，R2 is 0.979，RMSEP is 0.262%，r2 is 0.97. This study verifies it is feasible to detect the peroxide value of edible oil by THz technology, and the more important innovation is a high-precision, stable performance, fast and non-destructive detection method for the evaluation of edible oil quality has been found. Furthermore, this research has important guiding significance for improving the safety of edible oil quality and building edible risk assessment systems.
Key words：Edible oil; Terahertz time domain spectroscopy technology; Principal component regression; Partial least squares; Quantitative analysis
刘翠玲，杨雨菲，田 芳，吴静珠，孙晓荣. 基于太赫兹时域光谱的食用油过氧化值定量分析研究[J]. 光谱学与光谱分析, 2021, 41(05): 1387-1392.
LIU Cui-ling, YANG Yu-fei, TIAN Fang, WU Jing-zhu, SUN Xiao-rong. Study on Quantitative Analysis of Edible Oil Peroxide Value by Terahertz Time Domain Spectroscopy. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2021, 41(05): 1387-1392.
[1] Gong Weichao, Shi Ruyi, Chen Min, et al. Journal of Food Measurement and Characterization, 2019, 13(3): 2203.
[2] JI Min, LIU Zhong-yi, ZHANG Jian, et al(季 敏, 刘忠义, 张 剑，等). China Oils and Fats(中国油脂), 2018, 43(5): 92.
[3] Liang Jie, Guo Qijia, Chang Tianying, et al. Optik, 2018, 174: 7.
[4] Benjamin S Y Ung，Brian W H Ng，Derek Abbott. 35th International Conference on Infrared, Millimeter, and Terahertz Waves, IEEE, 2010.
[5] LIAN Fei-yu, FU Mai-xia, GE Hong-yi, et al(廉飞宇, 付麦霞, 葛宏义，等). China Oils and Fats(中国油脂), 2017, 42(7): 69.
[6] NIE Mei-tong, XU De-gang, WANG Yu-ye, et al(聂美彤, 徐德刚, 王与烨，等). Spectroscopy and Spectral Analysis(光谱学与光谱分析), 2018, 38(7): 2016.
[7] GB 5009.227—2016 National Standard of the People’s Republic of China(中华人民共和国国家标准). National Food Safety Standard-Determination of Peroxide Value in Foods(食品安全国家标准 食品中过氧化值的测定).
[8] 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. 124.
[9] Nakajima Shusaku, Shiraga Keiichiro, Suzuki Tetsuhito, et al. Food Chemistry, 2019, 294(5): 203.
[10] Hu Keqing, Huyan Zongyao, Sherazi Syed Tufail Hussain, et al. Journal of Oleo Science, 2019, 68(11): 1073.
[11] LIU Jun-xiu, DU Bin, DENG Yu-qiang, et al(刘俊秀, 杜 彬, 邓玉强，等). Chinese Journal of Lasers(中国激光), 2019, 46(6): 614039.
[12] Zhan Honglei, Xi Jianfeng, Zhao Kun, et al. Food Control, 2016, 67: 114.
