zhpsЖурнал прикладной спектроскопииZhurnal Prikladnoii Spektroskopii0514-7506B. I. Stepanov Institute of Physics of the National Academy of Scienceszhps-1448Research ArticleАННОТАЦИИ АНГЛОЯЗЫЧНЫХ СТАТЕЙABSTRACTS ENGLISH-LANGUAGE ARTICLESКоличественный анализ концентрации кумарина в бинарной смеси с использованием терагерцовой спектроскопии в сочетании с оптимизированным методом опорных векторов и наименьших квадратовQuantitative Analysis of the Concentration of Coumarin in a Binary Mixture Using Terahertz Spectroscopy Combined with Optimized Least Square Support Vector MachineGuoY.GuoYuanfeng

Пекин

Key Laboratory of Terahertz Optoelectronics, Ministry of Education, Department of Physics.

Beijing

LiX.LiXu

Пекин

Key Laboratory of Terahertz Optoelectronics, Ministry of Education, Department of Physics,

Beijing

ZhuangZ.ZhuangZhiying

Пекин

Key Laboratory of Terahertz Optoelectronics, Ministry of Education, Department of Physics.

Beijing

YanJ.YanJiayu

Пекин

Key Laboratory of Terahertz Optoelectronics, Ministry of Education, Department of Physics.

Beijing

ShiY.ShiYulei

Пекин

Key Laboratory of Terahertz Optoelectronics, Ministry of Education, Department of Physics.

Beijing

ZuoJ.ZuoJian

Пекин

Key Laboratory of Terahertz Optoelectronics, Ministry of Education, Department of Physics.

Beijing

2200602060@cnu.edu.cnZhangС.ZhangCunlin

Пекин

Key Laboratory of Terahertz Optoelectronics, Ministry of Education, Department of Physics.

Beijing

Пекинский педагогический университетCapital Normal University202326112023905812812Copyright © Guo Y., Li X., Zhuang Z., Yan J., Shi Y., Zuo J., Zhang С., 20232023Guo Y., Li X., Zhuang Z., Yan J., Shi Y., Zuo J., Zhang С.Guo Y., Li X., Zhuang Z., Yan J., Shi Y., Zuo J., Zhang C.Данная работа распространяется под лицензией Creative Commons Attribution 4.0.This work is licensed under a Creative Commons Attribution 4.0 License.https://zhps.ejournal.by/jour/article/view/1448

Кумарин количественно проанализирован в системе бинарных смесей для более точного и удобного контроля за безопасностью пищевых продуктов. Представлен новый подход терагерцовой спектроскопии во временной области (THz-TDS) и оптимизированного метода опорных векторов и наименьших квадратов (LS-SVM) для анализа содержания кумарина в бинарной смеси кумарина и ванилина. Измерены THz-отклики бинарных смесей, для обработки спектров коэффициента поглощения применен алгоритм Савицкого–Голея. Анализ главных компонент использован для извлечения признаков из предварительно обработанных данных. Отличные результаты прогнозирования получены с использованием метода LS-SVM, оптимизированного алгоритмом поиска воробья (SSA), с коэффициентом детерминации R2 > 0.999, среднеквадратической ошибкой 0.001 и остаточным прогнозирующим отклонением 146. Показано, что прогнозирующий эффект алгоритма LS-SVM, оптимизированного с помощью SSA, лучше, чем у алгоритма метода опорных векторов и алгоритма LSSVM без модели SSA. Комбинация THz-спектра и SSA-оптимизированного метода LS-SVM перспективна для анализа бинарных смесей кумарина и ванилина, а также более сложных многокомпонентных смесей.

The purpose of this study was to quantitatively analyze coumarin in a binary mixture system and to provide a more accurate and convenient potential for food safety inspection and supervision. This investigation has presented a novel terahertz time-domain spectroscopy (THz-TDS) approach and an optimized least square support vector machine (LS-SVM) to analyze the content of coumarin in the binary mixture of coumarin and vanillin. Terahertz responses of the binary mixtures have been measured and a Savitzky–Golay algorithm has been applied to process the absorption coefficient spectra. The principal component analysis has been used to extract features from the preprocessed data. The excellent prediction results can be obtained using LS-SVM optimized by sparrow search algorithm (SSA) with the coefficient of determination (R2), root-mean-square error, and residual predictive deviation of the prediction set were more than 0.999, 0.001, and 146, respectively. The research shows that the prediction effect of the LS-SVM algorithm optimized by the SSA model is better than that of the support vector machine (SVM) algorithm and LS-SVM algorithm without the SSA model. Our research shows that the combination of THz spectrum and SSAoptimized LS-SVM is very promising for the analysis of coumarin and vanillin binary mixtures, and has great potential for the quantitative analysis of more complex multicomponent mixtures.

терагерцовая спектроскопия во временной областиколичественный анализбинарная смесьалгоритм поиска воробьякомбинация методов опорных векторов и наименьших квадратовterahertz time-domain spectroscopyquantitative analysisbinary mixturesparrow search algorithmLS-SVMThis work was supported by the National Natural Science Foundation of China (grants Nos. 12174272, 11204190). The authors sincerely appreciate Baofeng Jiang for his guidance and help in the experimental design, as well as Anjie Wei and Nan Li for their support for the inspiration of the experimental algorithmReferencesLowri S. de Jager, Gracia A. Perfetti, Gregory W. Diachenko, J. Chromatogr. A, 1145 (2007) 83–88 (2007).Lowri S. de Jager, Gracia A. Perfetti, Gregory W. Diachenko, J. Chromatogr. A, 1145 (2007) 83–88 (2007).Pei Liang, Yong Feng Zhou, De Zhang, Microchim. Acta, 302 (2019).Pei Liang, Yong Feng Zhou, De Zhang, Microchim. Acta, 302 (2019).D. Havkin-Frenkel, F. C. Belanger, Appl. Microbiol. Biotech., 56, 296–314 (2001).D. Havkin-Frenkel, F. C. Belanger, Appl. Microbiol. Biotech., 56, 296–314 (2001).N. Robledo-O’Ryan, M. J. Matos, Bioorg. Med. Chem. Lett., 25, 621–632 (2017).N. Robledo-O’Ryan, M. J. Matos, Bioorg. Med. Chem. Lett., 25, 621–632 (2017).G. S. Clark, Perfumer &Flavorist, 20, 23–34 (1995).G. S. Clark, Perfumer &Flavorist, 20, 23–34 (1995).A. W. Archer, J. Chromatogr., 447, N 1, 272–276 (1988).A. W. Archer, J. Chromatogr., 447, N 1, 272–276 (1988).EFSA, The EFSA J.,104, 1–36 (2004).EFSA, The EFSA J.,104, 1–36 (2004).Mirjana Lonˇcar, Martina Jakovljevi´c, Foods, 9, 645 (2020).Mirjana Lonˇcar, Martina Jakovljevi´c, Foods, 9, 645 (2020).E. L. Maistro, M. E. De Souza, R. P. Fedato, Environ. Heal., 78A, 109–118 (2015).E. L. Maistro, M. E. De Souza, R. P. Fedato, Environ. Heal., 78A, 109–118 (2015).Harris H. Wisneski, AOAC Int., 84, No. 3 (2001).Harris H. Wisneski, AOAC Int., 84, No. 3 (2001).Afidah A. Rahim, Food Chem., 126, 1412–1416 (2011).Afidah A. Rahim, Food Chem., 126, 1412–1416 (2011).Yan Shen, J. Agricult. Food Chem., 10881–10888 (2014).Yan Shen, J. Agricult. Food Chem., 10881–10888 (2014).W. Wang, J. Tang, S. Wang, L. Zhou, Z. D. Hu, J. Chromatogr. A, 1148, No. 1, 108–114 (2007).W. Wang, J. Tang, S. Wang, L. Zhou, Z. D. Hu, J. Chromatogr. A, 1148, No. 1, 108–114 (2007).J. Huang, P. Liang, J. Xu, Roy. Soc. Chem., 7, 49097 (2017).J. Huang, P. Liang, J. Xu, Roy. Soc. Chem., 7, 49097 (2017).Li Fu, J. Food Meas. Charact., 15, 5439–5444 (2021)Li Fu, J. Food Meas. Charact., 15, 5439–5444 (2021)Lowri S. de Jager, Gracia A. Perfetti, Food Chem., 1701–1709 (2008).Lowri S. de Jager, Gracia A. Perfetti, Food Chem., 1701–1709 (2008).Y. Shen, C. Han, B. Liu, Z. Lin, X. Zhou, C. Wang, Z. Zhu, J. Dairy Sci., 97, 679–686 (2014).Y. Shen, C. Han, B. Liu, Z. Lin, X. Zhou, C. Wang, Z. Zhu, J. Dairy Sci., 97, 679–686 (2014).D. M. Mittleman, Nature Photon., 7, 666–669 (2013).D. M. Mittleman, Nature Photon., 7, 666–669 (2013).M. Walther, P. Plochocka, B. Fischer, H. Helm, P. U. Jepsen, Biopolymers, 67, 310–313 (2002).M. Walther, P. Plochocka, B. Fischer, H. Helm, P. U. Jepsen, Biopolymers, 67, 310–313 (2002).J. E. Haddad, F. De Miollis, J. B. Sleiman, L. Canioni, P. Mounaix, B. Bousquet, Anal. Chem., 86, No. 10, 4927–4933 (2014).J. E. Haddad, F. De Miollis, J. B. Sleiman, L. Canioni, P. Mounaix, B. Bousquet, Anal. Chem., 86, No. 10, 4927–4933 (2014).P. H. Siegel, IEEE Transact. Microwave Theory and Techniques, 52, 2438–2447 (2004).P. H. Siegel, IEEE Transact. Microwave Theory and Techniques, 52, 2438–2447 (2004).Tao Chen, Lingxiao Yu, Spectrochim. Acta A: Mol. Biomol. Spectrosc., 263, 120159 (2021).Tao Chen, Lingxiao Yu, Spectrochim. Acta A: Mol. Biomol. Spectrosc., 263, 120159 (2021).Tao Chen, Xin Zhong, IEEE Transact. Terahertz Sci. Tech., 11, No. 3 (2021).Tao Chen, Xin Zhong, IEEE Transact. Terahertz Sci. Tech., 11, No. 3 (2021).Tao Chen, Lingjie Ma, Food Sci., 87, No. 3, 1108–1118 (2022).Tao Chen, Lingjie Ma, Food Sci., 87, No. 3, 1108–1118 (2022).Noureddine Maamar, Int. Conf. Adv. Electrical Engineering (ICAEE) (2019).Noureddine Maamar, Int. Conf. Adv. Electrical Engineering (ICAEE) (2019).J. T. Kindt, C. A. Schmuttenmaer, J. Phys. Chem., 100, 10373–10379 (1996).J. T. Kindt, C. A. Schmuttenmaer, J. Phys. Chem., 100, 10373–10379 (1996).V. N. Vapnik, The Nature of Statistical Learning Theory, New York, Springer Verlag (1995).V. N. Vapnik, The Nature of Statistical Learning Theory, New York, Springer Verlag (1995).J. A. K. Suykens, J. Vandewalle, Neural Process Lett., 9, 293–300 (1999).J. A. K. Suykens, J. Vandewalle, Neural Process Lett., 9, 293–300 (1999).J. A. K. Suykens, J. Vandewalle, IEEE Trans. Circuits Syst. I, Fund. Theory Appl., 47, No. 7, 1109–1114 (2000).J. A. K. Suykens, J. Vandewalle, IEEE Trans. Circuits Syst. I, Fund. Theory Appl., 47, No. 7, 1109–1114 (2000).X. Sun, Z. Zhou, C. Liu, F. U. Xinxin, Y. Dou, Food Sci. (2018).X. Sun, Z. Zhou, C. Liu, F. U. Xinxin, Y. Dou, Food Sci. (2018).Jiankai Xue, Bo Shen, Systems Sci. Control Eng., 22–34 (2020).Jiankai Xue, Bo Shen, Systems Sci. Control Eng., 22–34 (2020).

The authors declare that there are no conflicts of interest present.