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 ENG  Vol.9 No.2 , February 2017
Near-Infrared Spectroscopy Combined with Partial Least Squares Discriminant Analysis Applied to Identification of Liquor Brands
Abstract: The identification of liquor brands is very important for food safety. Most of the fake liquors are usually made into the products with the same flavor and alcohol content as regular brand, so the identification for the liquor brands with the same flavor and the same alcohol content is essential. However, it is also difficult because the components of such liquor samples are very similar. Near-infrared (NIR) spectroscopy combined with partial least squares discriminant analysis (PLS-DA) was applied to identification of liquor brands with the same flavor and alcohol content. A total of 160 samples of Luzhou Laojiao liquor and 200 samples of non-Luzhou Laojiao liquor with the same flavor and alcohol content were used for identification. Samples of each type were randomly divided into the modeling and validation sets. The modeling samples were further divided into calibration and prediction sets using the Kennard-Stone algorithm to achieve uniformity and representativeness. In the modeling and validation processes based on PLS-DA method, the recognition rates of samples achieved 99.1% and 98.7%, respectively. The results show high prediction performance for the identification of liquor brands, and were obviously better than those obtained from the principal component linear discriminant analysis method. NIR spectroscopy combined with the PLS-DA method provides a quick and effective means of the discriminant analysis of liquor brands, and is also a promising tool for large-scale inspection of liquor food safety.
Cite this paper: Yang, B. , Yao, L. and Pan, T. (2017) Near-Infrared Spectroscopy Combined with Partial Least Squares Discriminant Analysis Applied to Identification of Liquor Brands. Engineering, 9, 181-189. doi: 10.4236/eng.2017.92009.
References

[1]   Williams, P. and Norris, K. (2001) Near-Infrared Technology in the Agricultural and Food Industries. American Association of Cereal Chemists, USA.

[2]   Eriksson, L., Johansson, E., Kettaneh-Wold, N., Trygg, J., Wikström, C. and Wold, S. (2006) Multi- and Megavariate Data Analysis Part I: Basic Principles and Applications. Umetrics Academy, Umea, Sweden.

[3]   Chen, H.Z., Pan, T., Chen, J.M. and Lu, Q.P. (2011) Waveband Selection for NIR Spectroscopy Analysis of Soil Organic Matter Based on SG Smoothing and MWPLS Methods. Chemometrics and Intelligent Laboratory Systems, 107, 139-146.
https://doi.org/10.1016/j.chemolab.2011.02.008

[4]   Pan, T., Li, M.M. and Chen, J.M. (2014) Selection Method of Quasi-Continuous Wavelength Combination with Applications to the Near-Infrared Spectroscopic Analysis of Soil Organic Matter. Applied Spectroscopy, 68, 263-271.
https://doi.org/10.1366/13-07088

[5]   Lyu, N., Chen, J.M., Pan, T., Yao, L.J., Han, Y. and Yu, J. (2016) Near-Infrared Spectroscopy Combined with Equidistant Combination Partial Least Squares Applied to Multi-Index Analysis of Corn. Infrared Physics & Technology, 76, 648-654.
https://doi.org/10.1016/j.infrared.2016.01.022

[6]   Liu, Z.Y., Liu, B., Pan, T. and Yang, J.D. (2013) Determination of Amino Acid Nitrogen in Tuber Mustard Using Near-Infrared Spectroscopy with Waveband Selection Stability.Spectrochimica Acta. Part A: Molecular and Biomolecular Spectroscopy, 102, 269-274.
https://doi.org/10.1016/j.saa.2012.10.006

[7]   Qu, F.F., Ren, D., Wang, J.H., Zhang, Z., Lu, N. and Meng, L. (2016) An Ensemble Successive Project Algorithm for Liquor Detection Using Near Infrared Sensor. Sensors, 16, 89-102.
https://doi.org/10.3390/s16010089

[8]   Liu, J.X., Zhang, W.W., Han, S.H., Li, X., Li, P.Y., Yang, G.D., Yang, Y., Xu, B.C. and Luo, D.L. (2016) Rapid Detection of Caproic Acid and Acetic Acid in Liquor Base Based on Fourier Transform Near-Infrared Spectroscopy. Food Science, 37, 181-185.

[9]   Zhang, W.W., Liu, J.X., Han, S.H., Pan, Y.O., Li, X., Li, P.Y., Xu, B.C. and Luo, D.L. (2016) Determination of Aldehydes in Liquor Base Based on Fourier Transform Near-Infrared Spectroscopy. Food Science, 37, 111-115.

[10]   Pan, T., Chen, Z.H., Chen, J.M. and Liu, Z.Y. (2012) Near-Infrared Spectroscopy with Waveband Selection Stability for the Determination of COD in Sugar Refinery Wastewater. Analytical Methods, 4, 1046-1052.
https://doi.org/10.1039/c2ay05856a

[11]   Xie, J., Pan, T., Chen, J.M., Chen, H.Z. and Ren, X.H. (2010) Joint Optimization of Savitzky-Golay Smoothing Models and Partial Least Squares Factors for Near-Infrared Spectroscopic Analysis of Serum Glucose.Chinese Journal of Analytical Chemistry, 38, 342-346.
https://doi.org/10.3724/sp.j.1096.2010.00342

[12]   Pan, T., Liu, J.M., Chen, J.M., Zhang, G.P. and Zhao, Y. (2013) Rapid Determination of Preliminary Thalassaemia Screening Indicators Based on Near-Infrared Spectroscopy with Wavelength Selection Stability.Analytical Methods, 5, 4355-4362.
https://doi.org/10.1039/c3ay40732b

[13]   Han, Y., Chen, J.M., Pan, T. and Liu, G.S. (2015) Determination of Glycated Hemoglobin Using Near-Infrared Spectroscopy. Chemometrics And Intelligent Laboratory Systems, 145, 84-92.
https://doi.org/10.1016/j.chemolab.2015.04.015

[14]   Yao, L.J., Lyu, N., Chen, J.M., Pan, T. and Yu, J. (2016) Joint Analyses Model for Total Cholesterol and Triglyceride in Human Serum with Near-Infrared Spectroscopy. Spectrochimica Acta Part A, 159, 53-59.
https://doi.org/10.1016/j.saa.2016.01.022

[15]   Zhang, A.N. and Zhang, J.H. (2010) Tutorial of Liquor Production and Blending. Science Press, Beijing, 140-146.

[16]   Kennard, R.W. and Stone, L.A. (1969) Computer-aided Design of Experiments. Technometrics, 11, 137-148.
https://doi.org/10.1080/00401706.1969.10490666

[17]   Claeys, D.D., Verstraelen, T., Pauwels, E., Stevens, C.V., Waroquier, M. and Speybroeck, V.V. (2010) Conformational Sampling of Macrocyclic Alkenes Using a Kennard-Stone-Based Algorithm. Journal of Physical Chemistry A, 114, 6879-6887.
https://doi.org/10.1021/jp1022778

[18]   Barker, M. and William, R. (2003) Partial Least Squares for Discrimination. Journal of Chemometrics, 17, 166-173.
https://doi.org/10.1002/cem.785

[19]   Miguel, P.E. and Michel, T. (2003) Prediction of Clinical Outcome with Microarray Data: A Partial Least Squares Discriminant Analysis (PLS-DA) Approach. Human Genetics, 112, 581-592.

[20]   Lu, W.Z., Yuan, H.F. and Xu, G.T. (2000) Modern near Infrared Spectroscopy Analytical Technology. China Petrochemical Press, Beijing, 29-31.

 
 
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