CLC number: O43
On-line Access: 2024-08-27
Received: 2023-10-17
Revision Accepted: 2024-05-08
Crosschecked: 2008-10-28
Cited: 11
Clicked: 7324
Li-juan XIE, Xing-qian YE, Dong-hong LIU, Yi-bin YING. Application of principal component-radial basis function neural networks (PC-RBFNN) for the detection of water-adulterated bayberry juice by near-infrared spectroscopy[J]. Journal of Zhejiang University Science B, 2008, 9(12): 982-989.
@article{title="Application of principal component-radial basis function neural networks (PC-RBFNN) for the detection of water-adulterated bayberry juice by near-infrared spectroscopy",
author="Li-juan XIE, Xing-qian YE, Dong-hong LIU, Yi-bin YING",
journal="Journal of Zhejiang University Science B",
volume="9",
number="12",
pages="982-989",
year="2008",
publisher="Zhejiang University Press & Springer",
doi="10.1631/jzus.B0820057"
}
%0 Journal Article
%T Application of principal component-radial basis function neural networks (PC-RBFNN) for the detection of water-adulterated bayberry juice by near-infrared spectroscopy
%A Li-juan XIE
%A Xing-qian YE
%A Dong-hong LIU
%A Yi-bin YING
%J Journal of Zhejiang University SCIENCE B
%V 9
%N 12
%P 982-989
%@ 1673-1581
%D 2008
%I Zhejiang University Press & Springer
%DOI 10.1631/jzus.B0820057
TY - JOUR
T1 - Application of principal component-radial basis function neural networks (PC-RBFNN) for the detection of water-adulterated bayberry juice by near-infrared spectroscopy
A1 - Li-juan XIE
A1 - Xing-qian YE
A1 - Dong-hong LIU
A1 - Yi-bin YING
J0 - Journal of Zhejiang University Science B
VL - 9
IS - 12
SP - 982
EP - 989
%@ 1673-1581
Y1 - 2008
PB - Zhejiang University Press & Springer
ER -
DOI - 10.1631/jzus.B0820057
Abstract: near-infrared (NIR) spectroscopy combined with chemometrics techniques was used to classify the pure bayberry juice and the one adulterated with 10% (w/w) and 20% (w/w) water. Principal component analysis (PCA) was applied to reduce the dimensions of spectral data, give information regarding a potential capability of separation of objects, and provide principal component (PC) scores for radial basis function neural networks (RBFNN). RBFNN was used to detect bayberry juice adulterant. Multiplicative scatter correction (MSC) and standard normal variate (SNV) transformation were used to preprocess spectra. The results demonstrate that PC-RBFNN with optimum parameters can separate pure bayberry juice samples from water-adulterated bayberry at a recognition rate of 97.62%, but cannot clearly detect water levels in the adulterated bayberry juice. We conclude that NIR technology can be successfully applied to detect water-adulterated bayberry juice.
[1] Akhlaghi, Y., Kompany-Zareh, M., 2005. Comparing radial basis function and feed-forward neural networks assisted by linear discriminant or principal component analysis for simultaneous spectrophotometric quantification of mercury and copper. Analytica Chimica Acta, 537(1-2):331-338.
[2] Andre, M., 2003. Multivariate analysis and classification of the chemical quality of 7-aminocephalsporanic acid using near-infrared reflectance spectroscopy. Analytical Chemistry, 75(14):3460-3467.
[3] Bechmann, I.E., Jorgensen, B.M., 1998. Rapid assessment of quality parameters for frozen cod using near infrared spectroscopy. Lebensmittel-Wissenschaft und-Technologie, 31(7-8):648-652.
[4] Chen, J., Arnold, M.A., Small, G.W., 2004. Comparison of combination and first overtone spectral regions of near-infrared calibration models for glucose and other biomolecules in aqueous solutions. Analytical Chemistry, 76(18):5405-5413.
[5] Chen, Z.L., 1996. The history of bayberries. Journal of Fruit Science, 13(1):59-61 (in Chinese).
[6] Cozzolino, D., Kwiatkowski, M.J., Parker, M., Cynkar, W.U., Dambergs, R.G., Gishen, M., Herderich, M.J., 2004. Prediction of phenolic compounds in red wine fermentations by visible and near infrared spectroscopy. Analytica Chimica Acta, 513(1):73-80.
[7] Derks, E.P.P.A., Sánchez, M.S., Buydens, L.M.C., 1995. Robustness analysis of radial base function and multi-layered feed-forward neural network models. Chemometrics and Intelligent Laboratory Systems, 28(1):49-60.
[8] Fang, Z., Zhang, M., Wang, L., 2007. HPLC-DAD-ESIMS analysis of phenolic compounds in bayberries (Myrica rubra Sieb. et Zucc.). Food Chemistry, 100(2):845-852.
[9] Fidêncio, P.H., Ruisánchez, I., Poppi, R.J., 2001. Application of artificial neural networks to the classification of soils from São Paulo state using near-infrared spectroscopy. Analyst, 126(12):2194-2200.
[10] Fu, X.P., Ying, Y.B., Zhou, Y., Xie, L.J., Xu, H.R., 2008. Application of NIR spectroscopy for firmness evaluation of peaches. Journal of Zhejiang University SCIENCE B, 9(7):552-557.
[11] Gautz, L.D., Kaufusi, P., Jackson, M.C., Bittenbender, H.C., Tong, C., 2006. Determination of kavalactones in dried kava (Piper methysticum) power using near-infrared reflectance spectroscopy and partial least-squares regression. Journal of Agricultural and Food Chemistry, 54(17):6147-6152.
[12] Gayo, J., Hale, S.A., 2007. Detection and quantification of species authenticity and adulteration in crabmeat using visible and near-infrared spectroscopy. Journal of Agricultural and Food Chemistry, 55(3):585-592.
[13] Gayo, J., Hale, S.A., Blanchard, S.M., 2006. Quantitative analysis and detection of adulteration in crab meat using visible and near-infrared spectroscopy. Journal of Agricultural and Food Chemistry, 54(4):1130-1136.
[14] Gestal, M., Gómez-Carracedo, M.P., Andrade, J.M., Dorado, J., Fernández, E., Prada, D., Pazos, A., 2004. Classification of apple beverages using artificial neural networks with previous variable selection. Analytica Chimica Acta, 524(1-2):225-234.
[15] Gierlinger, N., Schwanninger, M., Wimmer, R., 2004. Characteristics and classification of Fourier-transform near infrared spectra of the heartwood of different larch species (Larix sp.). Journal of Near Infrared Spectroscopy, 12(22):113-119.
[16] Iñón, F.A., Llario, R., Garrigues, S., Guardia, M., 2005. Development of a PLS based method for determination of the quality of beers by use of NIR: spectral ranges and sample-introduction considerations. Analytical and Bioanalytical Chemistry, 382(7):1549-1561.
[17] Laporte, M.F., Paguin, P., 1999. Near-infrared analysis of fat, protein, and casein in cow’s milk. Journal of Agricultural and Food Chemistry, 47(7):2600-2605.
[18] Leόn, L., Kelly, J.D., Downey, G., 2005. Detection of apple juice adulteration using near-infrared transflectance spectroscopy. Applied Spectroscopy, 59(5):593-599.
[19] Liu, H.X., Zhang, R.S., Yao, X.J., Liu, M.C., Hu, Z.D., Fan, B.T., 2004. Prediction of electrophoretic mobility of substituted aromatic acids in different aqueous-alcoholic solvents by capillary zone electrophoresis based on support vector machine. Analytica Chimica Acta, 525(1):31-41.
[20] Liu, L., Cozzolino, D., Cynkar, W.U., Gishen, M., Colby, C.B., 2006. Geographic classification of Spanish and Australian Tempranillo red wines by visible and near-infrared spectroscopy combined with multivariate analysis. Journal of Agricultural and Food Chemistry, 54(18):6754-6759.
[21] Mouazen, A.M., Karoui, R., de Baerdemaeker, J., Ramon, H., 2006. Classification of Soils into Different Moisture Content Levels Based on VIS-NIR Spectra. The 2006 ASABE Annual International Meeting. Oregon Convention Center, Portland, Oregon, 9-12 July, Paper No. 061067.
[22] Nagy, S., 1997. Economic adulteration of fruit beverages. Fruit Process, 4:125-131.
[23] Park, B., Abbott, J.A., Lee, K.J., Choi, C.H., Choi, K.H., 2003. Near-infrared diffuse reflectance for quantitative and qualitative measurement of soluble solids and firmness of delicious and gala apples. Transactions of the ASAE, 46(6):1721-1731.
[24] Pedro, A.M.K., Ferreira, M.M.C., 2005. Nondestructive determination of solids and carotenoids in tomato products by near-infrared spectroscopy and multivariate calibration. Analytical Chemistry, 77(8):2505-2511.
[25] Pulido, A., Ruisánchez, I., Rius, F.X., 1999. Radial basis functions applied to the classification of UV-visible spectra. Analytica Chimica Acta, 388(3):273-281.
[26] Qu, N., Li, X., Dou, Y., Mi, H., Guo, Y., Ren, Y., 2007. Nondestructive quantitative analysis of erythromycin ethylsuccinate powder drug via short-wave near-infrared spectroscopy combined with radial basis function neural networks. European Journal of Pharmaceutical Sciences, 31(3-4):156-164.
[27] Rambla, F.J., Garrigues, S., Guardia, M., 1997. PLS-NIR determination of total sugar, glucose, fructose and sucrose in aqueous solutions of fruit juices. Analytica Chimica Acta, 344(1-2):41-53.
[28] Walczak, B., Massart, D.L., 1996. The radial basis functions-partial least squares approach as a flexible non-linear regression technique. Analytica Chimica Acta, 331(3):177-185.
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