pimiПриборы и методы измеренийDevices and Methods of Measurements2220-95062414-0473BNTU10.21122/2220-9506-2021-12-1-75-81pimi-702Research ArticleМетоды измерений, контроля, диагностикиMethods of measurements, monitoring, diagnosticsВыбор переменных в ультрафиолетовом, видимом и ближнем инфракрасном диапазонах для калибровки смеси растительных масел по спектрам оптической плотностиVariables Selection in the Ultraviolet, Visible and Near Infrared Range for Calibration of a Mixture of Vegetable Oils by Absorbance SpectraХодасевичМ. А.KhodasevichМ. A.

Адрес для переписки: Ходасевич М.А. - Институт физики имени Б.И. Степанова НАН Беларуси, пр-т Независимости, 68, г. Минск e-mail: m.khodasevich@ifanbel.bas-net.by

m.khodasevich@ifanbel.bas-net.byБорисевичД. А.BorisevichD. A.

пр-т Независимости, 68, г. Минск 220072

Nezavisimosty Ave., 68, Minsk 220072

Институт физики имени Б.И. Степанова Национальной академии наук БеларусиБеларусьB.I. Stepanov Institute of Physics of the National Academy of Sciences of BelarusBelarus2021190320211217581Copyright © Ходасевич М.А., Борисевич Д.А., 20212021Ходасевич М.А., Борисевич Д.А.Khodasevich М.A., Borisevich D.A.Данная работа распространяется под лицензией Creative Commons Attribution 4.0.This work is licensed under a Creative Commons Attribution 4.0 License.https://pimi.bntu.by/jour/article/view/702

Целью работы являлась многопараметрическая калибровка концентрации нерафинированного подсолнечного масла, рассматриваемого в качестве фальсификата льняного масла. Актуальность исследования обусловлена необходимостью разработки простого и эффективного метода обнаружения фальсификации льняного масла, превосходящего по содержанию незаменимых полиненасыщенных жирных кислот оливковое масло, выявлению подделок которого в отличие от льняного посвящено большое количество работ.

Многопараметрическая калибровка проводилась с помощью модели, основанной на методе главных компонент, кластерном анализе и проекции на латентные структуры спектров оптической плотности в УФ-, видимом и ближнем ИК диапазонах с применением трех методов выбора спектральных переменных: метода последовательного проецирования, метода поиска комбинации сдвигающихся окон и метода ранжирования переменных по коэффициенту корреляции.

Показано, что применение методов последовательного проецирования, ранжирования переменных по коэффициенту корреляции и поиска комбинации сдвигающихся спектральных окон позволяет уменьшить величину среднеквадратичного отклонения калибровки с 0,63 % для широкополосной проекции на латентные структуры до 0,46 %, 0,50 % и 0,03 %, соответственно.

Разработанный метод многопараметрической калибровки с помощью проекции на латентные структуры спектров оптической плотности в УФ-, видимом и ближнем ИК диапазонах с применением выбора спектральных переменных путём поиска комбинации сдвигающихся окон является простым и эффективным средством обнаружения фальсификации льняного масла.

The aim of the work was a multivariate calibration of the concentration of unrefined sunflower oil, considered as adulteration, in a mixture with flaxseed oil. The relevance of the study is due to the need to develop a simple and effective method for detecting the falsification of flaxseed oil which is superior in the content of essential polyunsaturated fatty acids to olive oil. A few works only are devoted to identifying adulteration of flaxseed oil, unlike olive oil.Multivariate calibration carried out using a model based on the principal component analysis, cluster analysis and projection to latent structures of absorbance spectra in UV, visible and near IR ranges. Calibration uses three methods for spectral variables selection: the successive projections algorithm, the method of searching combination moving window, and method for ranking variables by correlation coefficient.The application of the successive projections algorithm, ranking variables by correlation coefficient and searching combination moving window makes it possible to reduce the value of the root mean square error of prediction from 0.63 % for wideband projection to latent structures to 0.46 %, 0.50 %, and 0.03 %, respectively.The developed method of multivariate calibration by projection to latent structures of absorbance spectra in UV, visible and near IR ranges using the spectral variables selection by searching combination moving window is a simple and effective method of detecting adulteration of flaxseed oil.

спектральный анализметод главных компонентпроекция на латентные структурывыбор спектральных переменныхspectral analysisprincipal component analysisprojection to latent structuresspectral variables selectionThis work was supported by the Belarusian Republican Foundation for Fundamental Research grant (project No. F19-060).ReferencesValdés A., Beltrán A., Mellinas C., Jiménez A., Garrigós M.C. Analytical methods combined with multivariate analysis for authentication of animal and vegetable food products with high fat content. Trends in Food Science & Technology, 2018, vol. 77, pp. 120‒130. DOI: 10.1016/j.tifs.2018.05.014Valdés A., Beltrán A., Mellinas C., Jiménez A., Garrigós M.C. Analytical methods combined with multivariate analysis for authentication of animal and vegetable food products with high fat content. Trends in Food Science & Technology, 2018, vol. 77, pp. 120‒130. DOI: 10.1016/j.tifs.2018.05.014Milanez K., Nóbrega T., Nascimento D., Insausti M., Band B., Pontes M. Multivariate modeling for detecting adulteration of extra virgin olive oil with soybean oil using fluorescence and UV–Vis spectroscopies: A preliminary approach. Food Science and Technology, 2017, vol. 85, pp. 9‒15. DOI: 10.1016/j.lwt.2017.06.060Milanez K., Nóbrega T., Nascimento D., Insausti M., Band B., Pontes M. Multivariate modeling for detecting adulteration of extra virgin olive oil with soybean oil using fluorescence and UV–Vis spectroscopies: A preliminary approach. Food Science and Technology, 2017, vol. 85, pp. 9‒15. DOI: 10.1016/j.lwt.2017.06.060Lima T., Musso M., Menezes D. Using Raman spectroscopy and an exponential equation approach to detect adulteration of olive oil with rapeseed and corn oil. Food Chemistry, 2020, vol. 333, pp. 127454. DOI: 10.1016/j.foodchem.2020.127454Lima T., Musso M., Menezes D. Using Raman spectroscopy and an exponential equation approach to detect adulteration of olive oil with rapeseed and corn oil. Food Chemistry, 2020, vol. 333, pp. 127454. DOI: 10.1016/j.foodchem.2020.127454Li Y., Xiong Y., Min S. Data fusion strategy in quantitative analysis of spectroscopy relevant to olive oil adulteration. Vibrational Spectroscopy, 2019, vol. 101, pp. 20‒27. DOI: 10.1016/j.vibspec.2018.12.009Li Y., Xiong Y., Min S. Data fusion strategy in quantitative analysis of spectroscopy relevant to olive oil adulteration. Vibrational Spectroscopy, 2019, vol. 101, pp. 20‒27. DOI: 10.1016/j.vibspec.2018.12.009Elzey B., Pollard D., Fakayode S. Determination of adulterated neem and flaxseed oil compositions by FTIR spectroscopy and multivariate regression analysis. Food Control, 2016, vol. 68, pp. 303‒309. DOI: 10.1016/j.foodcont.2016.04.008Elzey B., Pollard D., Fakayode S. Determination of adulterated neem and flaxseed oil compositions by FTIR spectroscopy and multivariate regression analysis. Food Control, 2016, vol. 68, pp. 303‒309. DOI: 10.1016/j.foodcont.2016.04.008De Souza L., De Santana F., Gontijo L., Mazivila S., Neto W. Quantification of adulterations in extra virgin flaxseed oil using MIR and PLS. Food Chemistry, 2015, vol. 182, pp. 35‒40. DOI: 10.1016/j.foodchem.2015.02.081De Souza L., De Santana F., Gontijo L., Mazivila S., Neto W. Quantification of adulterations in extra virgin flaxseed oil using MIR and PLS. Food Chemistry, 2015, vol. 182, pp. 35‒40. DOI: 10.1016/j.foodchem.2015.02.081Khodasevich M.A., Borisevich D.A. Identification of Flax Oil by Linear Multivariate Spectral Analys. Journal of Applied Spectroscopy, 2019, vol. 86, no. 6, pp. 880–884. DOI: 10.1007/s10812-020-00929-zKhodasevich M.A., Borisevich D.A. Identification of Flax Oil by Linear Multivariate Spectral Analys. Journal of Applied Spectroscopy, 2019, vol. 86, no. 6, pp. 880–884. DOI: 10.1007/s10812-020-00929-zBro R., Smilde A.K. Principal component analysis. Analytical Methods, 2016, vol. 6, pp. 2812–2831. DOI: 10.1039/C3AY41907JBro R., Smilde A.K. Principal component analysis. Analytical Methods, 2016, vol. 6, pp. 2812–2831. DOI: 10.1039/C3AY41907JGeladi P., Kowalski B.R. Partial Least-Squares Regression: A Tutorial. Analytica Chimica Acta, 1986, vol. 185, pp. 1–17. DOI: 10.1016/0003-2670(86)80028-9Geladi P., Kowalski B.R. Partial Least-Squares Regression: A Tutorial. Analytica Chimica Acta, 1986, vol. 185, pp. 1–17. DOI: 10.1016/0003-2670(86)80028-9Soares S.F.C., Gomes A.A., Araujo M.C.U., Filho A.R.G., Galvão R.K.H. The successive projections algorithm. TrAC Trends in Analytical Chemistry, 2013, vol. 42, pp. 84–98. DOI: 10.1016/j.trac.2012.09.006Soares S.F.C., Gomes A.A., Araujo M.C.U., Filho A.R.G., Galvão R.K.H. The successive projections algorithm. TrAC Trends in Analytical Chemistry, 2013, vol. 42, pp. 84–98. DOI: 10.1016/j.trac.2012.09.006Xiaobo Z., Jiewen Z., Malcolm J.W. Povey, Holmes M., Hanpin M. Variables selection methods in near-infrared spectroscopy. Analytica Chimica Acta, 2010, vol. 667, pp. 14–32. DOI: 10.1016/j.aca.2010.03.048Xiaobo Z., Jiewen Z., Malcolm J.W. Povey, Holmes M., Hanpin M. Variables selection methods in near-infrared spectroscopy. Analytica Chimica Acta, 2010, vol. 667, pp. 14–32. DOI: 10.1016/j.aca.2010.03.048Li Z., Wang J. , Xiong Y., Li Z., Feng S. The determination of the fatty acid content of sea buckthorn seed oil using near infrared spectroscopy and variable selection methods for multivariate calibration. Vibrational Spectroscopy, 2016, vol. 84, pp. 24–29. DOI: 10.1016/J.VIBSPEC.2016.02.008Li Z., Wang J. , Xiong Y., Li Z., Feng S. The determination of the fatty acid content of sea buckthorn seed oil using near infrared spectroscopy and variable selection methods for multivariate calibration. Vibrational Spectroscopy, 2016, vol. 84, pp. 24–29. DOI: 10.1016/J.VIBSPEC.2016.02.008Wang L., Lee F., Wang X., He Y. Feasibility study of quantifying and discriminating soybean oil adulteration in camellia oils by attenuated total reflectance MIR and fiber optic diffuse reflectance NIR. Food Chemistry, 2006, vol. 95, pp. 529‒536. DOI: 10.1016/j.foodchem.2005.04.015Wang L., Lee F., Wang X., He Y. Feasibility study of quantifying and discriminating soybean oil adulteration in camellia oils by attenuated total reflectance MIR and fiber optic diffuse reflectance NIR. Food Chemistry, 2006, vol. 95, pp. 529‒536. DOI: 10.1016/j.foodchem.2005.04.015Khodasevich M.A., Aseev V.A. Selection of Spectral Variables and Improvement of the Accuracy of Calibration of Temperature by Projection onto Latent Structures Using the Fluorescence Spectra of Yb3+:CaF . Optics and Spectroscopy, 2018, no. 124, pp. 713–717. DOI: 10.1134/S0030400X18050089Khodasevich M.A., Aseev V.A. Selection of Spectral Variables and Improvement of the Accuracy of Calibration of Temperature by Projection onto Latent Structures Using the Fluorescence Spectra of Yb3+:CaF . Optics and Spectroscopy, 2018, no. 124, pp. 713–717. DOI: 10.1134/S0030400X18050089Nawar S., Mouazen A.M. Optimal sample selection for measurement of soil organic carbon using online vis-NIR spectroscopy. Computers and Electronics in Agriculture, 2018, vol. 151, pp. 469‒477. DOI: 10.1016/j.compag.2018.06.042Nawar S., Mouazen A.M. Optimal sample selection for measurement of soil organic carbon using online vis-NIR spectroscopy. Computers and Electronics in Agriculture, 2018, vol. 151, pp. 469‒477. DOI: 10.1016/j.compag.2018.06.042Li Y., Fang T., Zhu S., Huang F., Chen Zh., Wang Y. Detection of olive oil adulteration with waste cooking oil via Raman spectroscopy combined with iPLS and SiPLS. Spectrochimica Acta Part A: Molecular and Biomolecular Spectroscopy, 2018, vol. 189, pp. 37–43. DOI: 10.1016/j.saa.2017.06.049Li Y., Fang T., Zhu S., Huang F., Chen Zh., Wang Y. Detection of olive oil adulteration with waste cooking oil via Raman spectroscopy combined with iPLS and SiPLS. Spectrochimica Acta Part A: Molecular and Biomolecular Spectroscopy, 2018, vol. 189, pp. 37–43. DOI: 10.1016/j.saa.2017.06.049

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