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Net Analyte Signal-based Quantitative Determination of Fusel Oil in Korean Alcoholic Beverage Using FT-NIR Spectroscopy

  • Lohumi, Santosh (Department of Biosystems Machinery Engineering, College of Agricultural and Life Science, Chungnam National University) ;
  • Kandpal, Lalit Mohan (Department of Biosystems Machinery Engineering, College of Agricultural and Life Science, Chungnam National University) ;
  • Seo, Young Wook (Department of Biosystems Machinery Engineering, College of Agricultural and Life Science, Chungnam National University) ;
  • Cho, Byoung Kwan (Department of Biosystems Machinery Engineering, College of Agricultural and Life Science, Chungnam National University)
  • Received : 2016.06.27
  • Accepted : 2016.07.27
  • Published : 2016.09.01

Abstract

Purpose: Fusel oil is a potent volatile aroma compound found in many alcoholic beverages. At low concentrations, it makes an essential contribution to the flavor and aroma of fermented alcoholic beverages, while at high concentrations, it induced an off-flavor and is thought to cause undesirable side effects. In this work, we introduce Fourier transform near-infrared (FT-NIR) spectroscopy as a rapid and nondestructive technique for the quantitative determination of fusel oil in the Korean alcoholic beverage "soju". Methods: FT-NIR transmittance spectra in the 1000-2500 nm region were collected for 120 soju samples with fusel oil concentrations ranging from 0 to 1400 ppm. The calibration and validation data sets were designed using data from 75 and 45 samples, respectively. The net analyte signal (NAS) was used as a preprocessing method before the application of the partial least-square regression (PLSR) and principal component regression (PCR) methods for predicting fusel oil concentration. A novel variable selection method was adopted to determine the most informative spectral variables to minimize the effect of nonmodeled interferences. Finally, the efficiency of the developed technique was evaluated with two different validation sets. Results: The results revealed that the NAS-PLSR model with selected variables ($R^2_{\upsilon}=0.95$, RMSEV = 100ppm) did not outperform the NAS-PCR model (($R^2_{\upsilon}=0.97$, RMSEV = 7 8.9ppm). In addition, the NAS-PCR shows a better recovery for validation set 2 and a lower relative error for validation set 3 than the NAS-PLSR model. Conclusion: The experimental results indicate that the proposed technique could be an alternative to conventional methods for the quantitative determination of fusel oil in alcoholic beverages and has the potential for use in in-line process control.

Acknowledgement

Supported by : Rural Development Administration

References

  1. Williams, P. and K. Norris. (Eds.). 2001. Near infrared technology in the agriculture and food industries, second ed. American Association of Cereal Chemists Inc., pp. 145-169.
  2. Woo, K. L. 2005. Determination of low molecular weight alcohols including fusel oil in various samples by diethyl ether extraction and capillary gas chromatography. J. AOAC Int. 88(5):1419-1427.
  3. Xiaobo Z., Z. Jiewen, M. J. Povey, M. Holmes and M. Hanpin. 2010. Variable selection methods in near-infrared spectroscopy. Anal Chim Acta. 667:14-32. https://doi.org/10.1016/j.aca.2010.03.048
  4. McLeod, G., K. Clelland, H. Tapp, E. K. Kemsley, R. H. Wilson, G. Poulter, et al. 2009. A comparison of variate pre-selection methods for use in partial least squares regression: A case study on NIR spectroscopy applied to monitoring beer fermentation. Journal of Food Engineering 90:300-307. https://doi.org/10.1016/j.jfoodeng.2008.06.037
  5. Mirmohseni, A., H. Abdollahi and R. Rostamizadeh. 2007. Net analyte signal-based simultaneous determination of ethanol and water by quartz crystal nanobalance sensor. Analytica Chimica Acta. 585:179-184. https://doi.org/10.1016/j.aca.2006.11.082
  6. Morgan, K. 1964. Fusel oil in beer; quantitative analysis by gas-liquid chromatography. J. Inst. Brew. 71:166-171.
  7. Osborne, B. G., T. Fearn and P. T. Hindle. 1993. Practical NIR Spectroscopy with Applicationsin Food and Beverage Analysis, second ed., Longman Scientific and Technical, Singapore, 1993.
  8. Pascoa, R. N. M. J., P. L. Magalhaes and J. A. Lopes. 2013. FT-NIR spectroscopy as a tool for valorization of a spent coffee grounds: Application to assessment of antioxidant properties. Food Research International 51:579-586. https://doi.org/10.1016/j.foodres.2013.01.035
  9. Pontes, M. J. C., S. R. B. Santos, M. C. U. Araujo, L. F. Almeida, R. C. A. Lima, E. N. Gaiao and U. T. C. P. Souto. 2006. Classification of distilled alcoholic beverages and verification of adulteration by near infrared spectrometery. Food Research International 39(2):182-189. https://doi.org/10.1016/j.foodres.2005.07.005
  10. Rajalahti, T., R. Arneberg, A. C. Kroksveen, M. Berel, K. M. Myhr and O. M. Kvalheim. 2009. Discriminating variable test and selectivity ratio plot: quantitative tool for interpretation and variable (biomarker) selection in complex spectral or chromatographic profiles. Anal Chem. 81(7):2581-90. https://doi.org/10.1021/ac802514y
  11. Ribereau-Gayon, P. 1978. Wine Flavor. In Flavor of Foods and Beverages. G. Charalambous and G.E. Inglett. Academic Press, New York.
  12. Rinnan, A., F. Berg and S. B. Engelsen. 2009. Review of the most common pre-processing techniques for nearinfrared spectra. Trends in Analytical Chemistry 28(10):1201-1222. https://doi.org/10.1016/j.trac.2009.07.007
  13. Sarraguca, M. C. and J. A. Lopes. 2009. The use of net analyte signal (NAS) in near infrared spectroscopy pharamaceutical applications: Interpretability and figures of merit. Analytica Chimica Acta. 642:179-185. https://doi.org/10.1016/j.aca.2008.10.006
  14. Sun, J., B. Yu, P. Curran and S. Q. Liu. 2011 Quantitative analysis of volatiles in transesterified coconut oil by headspace-solid-phase microextraction-gas chromatography-mass spectrometry. Food Chemistry 129:1882-1888. https://doi.org/10.1016/j.foodchem.2011.05.138
  15. Suomalainen, H. and M. Lehtonen. 1978. The production of aroma compounds by yeast. J. Inst. Brew. 85:149-156.
  16. Urickova, V. and J. Sadecka. 2015. Determination of geographical origin of alcoholic beverages using ultraviolet, visible and infrared spectroscopy: A review. Spectrochemica Acta Part A Molecular and Biomolecular Spectroscopy 148:131-137. https://doi.org/10.1016/j.saa.2015.03.111
  17. Wentzell, P. D. and L. V. Montoto. 2003. Comparision of principal component regressin and partial least square regression through generic simulation of complex mixtures. Chemomatrics and Intelligent Laboratory Systems 65 2003:257-279. https://doi.org/10.1016/S0169-7439(02)00138-7
  18. Amerine, M. A. and E. B. Roessler. 1976. Composition of wines. In Wines-Their Sensory Evaluation, M.A. Amerine and E.B. Roessler (Eds.), pp. 72-77. W.H. Freeman, New York.
  19. Andersen, C. M. and Bro, R. 2010. Variable selection in regression. Journal of Chemometrics 24:728-737. https://doi.org/10.1002/cem.1360
  20. Blanco, M. and I. Villarroya. 2002. NIR spectroscopy: a rapid-response analytical tool. Trends Anal. Chem. 21:240-250. https://doi.org/10.1016/S0165-9936(02)00404-1
  21. Dickinson, JR. 2003. The formation of higher alcohols. In:Smart KA (ed) Brewing yeast fermentation performance, 2nd edn. Blackwell, Oxford, UK, pp. 196-205.
  22. Gautam, R., S. Vanga., Ariese, F. and S. Umapathy. 2015. Review of multidiamentional data processing approaches for Raman and infrared spectroscopy. EPJ Techniques and Instrumentation 2(8):1-38. https://doi.org/10.1140/epjti/s40485-014-0011-5
  23. Goicoechea, H. C. and A. C. Olivieri. 2001. A comparison of orthogonal signal correction and net analyte preprocessing methods. Theoretical and experimental study. Chemometrics and intelligent laboratory systems 56:73-81. https://doi.org/10.1016/S0169-7439(01)00110-1
  24. Grassi, S., J. M. Amigo, C. B. Lyndgaard. R. Foschino and E. Casiraghi. 2014. Beer fermentation: Monitoring of process parameters by FT-NIR and multivariate data analysis. Food Chemistry 155:279-286. https://doi.org/10.1016/j.foodchem.2014.01.060
  25. Hazelwood, L.A., J. M. Daran and A. J. Van Maris. 2008. The Ehrlich pathway for fusel alcohol production: A century of research on Saccharomyces cerevisiae metabolism. Appl. Environ. Microbiol. 74:2259-2266. https://doi.org/10.1128/AEM.02625-07
  26. Hemmateenejad, B., R. Ghavami, R. Miri and M. Shamsipur. 2006. Net analyte signal-based simultaneoud determination of anthazoline and nephazoline using wavelength region selection by experimental designneural networks. Talanta 68:1222-1229. https://doi.org/10.1016/j.talanta.2005.07.045
  27. Hori, H., W. Fujii, Y. Hatanaka and Y. Suwa. 2003. Effects of fusel oil on animal hangover models. Alcohol Clin. Exp. Res. 27(8):37S-41S. https://doi.org/10.1097/01.ALC.0000078828.49740.48
  28. Hsieh, C.W., Y. H. Huang, C. H. Lai, W. J. Ho and W. C. Ko. 2010. Develop a novel method for removing fusel alcohols from rice sprits suing nanofiltration. Journal of Food Science 75(2):25-29.
  29. In, H. Y., T. S. Lee, D. S. Lee and B. S. Noh. 1995. Volatile components and fusel oils of sojues and mashes brewed by Korean traditional method. Korean J. Food Sci, Technol. 27(2):235-240.
  30. Inon, F. A., S. Garrigues and M. Guardia. 2006. Combination of mid-and near-infrared spectroscopy for the determination of the quality properties of beers. Analytica Chemica Acta 571:167-174. https://doi.org/10.1016/j.aca.2006.04.070
  31. Kim, D. Y. and B. K. Cho, 2015. Rapid monitoring of the fermentation process for Korean traditional rice wine 'Makgeolli' using FT-NIR spectroscopy. Infrared Physics & Technology 73:95-102. https://doi.org/10.1016/j.infrared.2015.09.007
  32. Kolomiets, O. A., D. W. Lachenmeier, U. Hoffmann and H. W. Seisler. 2010. Quanitative determination of quality parameters and authentication of vodka using near infrared spectroscopy. Journal of Near Infrared Spectroscopy 18(1):59-67. https://doi.org/10.1255/jnirs.866
  33. Kujawski W., W. Capala, M. Palczewska-Tuli nska, W. Ratajcza, D. Linkiewicz and B. Michalak B. 2002. Application ofmembrane pervaporation process to the enhanced separation of fusel oils. Presented at the 28th International Conference of the Slovak Society of Chemical Engineering 56:3-6.
  34. Lachenmeier, D. W., S. Haupt and K. Schulz. 2008. Defining maximum levels of higher alcohols in alcoholic beverages and surrogate alcohol products. Regulatory Toxicology and Pharmacology 50:313-321. https://doi.org/10.1016/j.yrtph.2007.12.008
  35. Lavine, B. K. 2000. Fundamental reviews: Chemometrics. Anal. Chem. 72(12):91R-98R. https://doi.org/10.1021/a1000016x
  36. Liu, H. H., Y. Q. Li and C. J. Sun. 2002. Determination of methanol and fusel oil in alcoholic beverages using headspace solid-phase microextraction and gas chromatography. Chinese journal of chromatography 20(1):90-103.
  37. Liu, L., D. Cozzolino, W. U. Cynkar, M. Gishen and C. B. Colby. 2006. Geographical classification of Spanish and Australian tempranillo red wines by visible and near-infrared spectroscopy combined with multivariate analysis. J. Agric. Food Chem. 54(18):6754-6759. https://doi.org/10.1021/jf061528b
  38. Lohumi, S., S. Lee. and B. K. Cho. 2015. Optimal variable selection for Fourier transform infrared spectroscopic analysis of starch adulterated garlic powder. Sensors and Acturators B: Chemical 216:622-628. https://doi.org/10.1016/j.snb.2015.04.060
  39. Lorber, A. (1986). Error propagation and figures of merit for quantification by solving matrix equation. Anal. Chem. 58:1167-1172. https://doi.org/10.1021/ac00297a042
  40. Marsili, N. R., M. S. Sobrero and H. C. Goicoechea. 2003. Spectrophotometric determination of sorbic and benzoic acid in fruit juices by a net analyte signal-based method with selection of the wavelength range to avoid non-modelled interferences. Anal. Bioanal. Chem. 376:126-133. https://doi.org/10.1007/s00216-003-1835-z

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