| 光谱学与光谱分析 |
|
|
|
|
|
| Study on the Recognition of Liquor Age of Gujing Based on Raman Spectra and Support Vector Regression |
| WANG Guo-xiang1, WANG Hai-yan2, WANG Hu1, ZHANG Zheng-yong2, LIU Jun1 |
1. School of Management Science & Engineering, Nanjing University of Finance & Economics，Nanjing 210046, China
2. Jiangsu Province Institute of Quality Safety & Engineering, Nanjing 210000, China |
|
|
|
|
Abstract It is an important and difficult research point to recognize the age of Chinese liquor rapidly and exactly in the field of liquor analyzing, which is also of great significance to the healthy development of the liquor industry and protection of the legitimate rights and interests of consumers. Spectroscopy together with the pattern recognition technology is a preferred method of achieving rapid identification of wine quality, in which the Raman Spectroscopy is promising because of its little affection of water and little or free of sample pretreatment. So, in this paper, Raman spectra and support vector regression (SVR) are used to recognize different ages and different storing time of the liquor of the same age. The innovation of this paper is mainly reflected in the following three aspects. First, the application of Raman in the area of liquor analysis is rarely reported till now. Second, the concentration of studying the recognition of wine age, while most studies focus on studying specific components of liquor and studies together with the pattern recognition method focus more on the identification of brands or different types of base wine. The third one is the application of regression analysis framework, which cannot be only used to identify different years of liquor, but also can be used to analyze different storing time, which has theoretical and practical significance to the research and quality control of liquor. Three kinds of experiments are conducted in this paper. Firstly, SVR is used to recognize different ages of 5, 8, 16 and 26 years of the Gujing Liquor; secondly, SVR is also used to classify the storing time of the 8-years liquor; thirdly, certain group of train data is deleted form the train set and put into the test set to simulate the actual situation of liquor age recognition. Results show that the SVR model has good train and predict performance in these experiments, and it has better performance than other non-liner regression method such as the Partial Least Squares Regression (PLS) method, and can also be applied in the practice of liquor analysis.
|
|
Received: 2014-09-29
Accepted: 2015-02-10
|
|
|
|
Corresponding Authors:
WANG Guo-xiang
E-mail: wildcat0518@163.com; gxwang1989@163.com
|
|
[1] Yu H, Lin H, Xu H, et al. Journal of Agricultural and Food Chemistry, 2008, 56(2): 307.
[2] Riu-Aumatell M, Bosch-Fusté J, López-Tamames E, et al. Food chemistry, 2006, 95(2): 237.
[3] Pinho O, Ferreira I M, Santos L H. Journal of Chromatography A, 2006, 1121(2): 145.
[4] Soufleros E H, Bouloumpasi E, Tsarchopoulos C, et al. Food Chemistry, 2003, 80(2): 261.
[5] ZHUANG Ming-yang(庄明扬). Sichuan Food and Fermentation(四川食品与发酵)，2008, 44(3)：28.
[6] XU Zhan-cheng(徐占成). Sichuan Food and Fermentation(四川食品与发酵)，2008, 44(2)：9.
[7] WANG Li, WANG Di-qiang, WANG Hua, et al(王 莉，汪地强，汪 华，等). Liquor-making(酿酒)，2005, 32(4)：18.
[8] Vapnik V N. The Nature of Statistical Learning Theory. New York: Springer-Verlag, 1995.
[9] Hsu C W, Chang C C, Lin C J. Practical Guide to Support Rector Classification, 2003. |
| [1] |
CHENG Hui-zhu1, 2, YANG Wan-qi1, 2, LI Fu-sheng1, 2*, MA Qian1, 2, ZHAO Yan-chun1, 2. Genetic Algorithm Optimized BP Neural Network for Quantitative
Analysis of Soil Heavy Metals in XRF[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(12): 3742-3746. |
| [2] |
LI Wen-wen1, 2, LONG Chang-jiang1, 2, 4*, LI Shan-jun1, 2, 3, 4, CHEN Hong1, 2, 4. Detection of Mixed Pesticide Residues of Prochloraz and Imazalil in
Citrus Epidermis by Surface Enhanced Raman Spectroscopy[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(10): 3052-3058. |
| [3] |
XU Su-an, WANG Jia-xiang, LIU Yong. Detection of Adulteration of Vine Pepper Oil by Near-Infrared
Spectroscopy Combined With Improved Whale Optimization
Algorithm Model BAS-WOA-SVR[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(02): 569-576. |
| [4] |
ZHANG Zhe-yu, LI Yao-xiang*, WANG Zhi-yuan, LI Chun-xu. NIR Model Optimization Study of Larch Wood Density Based on IFSR Abnormal Sample Elimination[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2022, 42(11): 3395-3402. |
| [5] |
LI Ming-liang1, DAI Yu-jia1, QIN Shuang1, SONG Chao2*, GAO Xun1*, LIN Jing-quan1. Influence of LIBS Analysis Model on Quantitative Analysis Precision of Aluminum Alloy[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2022, 42(02): 587-591. |
| [6] |
XIAO Wen1, CAO Ying-li1,2*, FENG Shuai1, LIU Ya-di1, JIANG Kai-lun1, YU Zheng-xin1, YAN Li1. Detection of Rice Sheath Blight Disease Index Based on Split-Window Gram-Schmidt Transformation and PSO-SVR Algorithm[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2021, 41(07): 2181-2187. |
| [7] |
FU Xing-hu, ZHAO Fei, WANG Zhen-xing, LU Xin, FU Guang-wei, JIN Wa, BI Wei-hong. Quantitative Analysis of Goat Serum Protein Content by Raman Spectroscopy Based on IABC-SVR[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2021, 41(02): 540-545. |
| [8] |
HUANG Lin-sheng, WANG Fang, WENG Shi-zhuang*, PAN Fang-fang, LIANG Dong. Surface-Enhanced Raman Spectroscopy for Rapid and Accurate Detection of Fenitrothion Residue in Maize[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2018, 38(09): 2782-2787. |
| [9] |
LI Yun1,2,3, ZHANG Ji1,2, LIU Fei4, XU Fu-rong3, WANG Yuan-zhong1,2*, ZHANG Jin-yu1,2,3*. Prediction of Total Polysaccharides Content in P. notoginseng Using FTIR Combined with SVR[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2018, 38(06): 1696-1701. |
| [10] |
DONG Jia-lin1, HONG Ming-jian1, 3*, ZHENG Xiang-quan2, 3, XU Yi2, 3. Discrimination of Human, Dog and Rabbit Blood Using Raman Spectroscopy[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2018, 38(02): 459-466. |
| [11] |
ZHANG Bao-yong1,2，ZHOU Hong-ji1,2，WU Qiang1,2，GAO Xia3. Raman Spectra Characteristics of Gas Hydrate Growth with Different Driving Forces[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2017, 37(09): 2768-2773. |
| [12] |
WU Cheng-ling1, GUO Jun-hong1, 2, YAO Heng-bin1, PAN Ling-nan1, WANG Fei1, WU Wen-qi1, JI Tong1, HU Fang-ren1, 2*. Study on the Photoluminescence Properties of ZnO Single Crystal[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2017, 37(06): 1700-1702. |
| [13] |
XU Dong-mei1, PAN Kun1, LIU Xu-wei1, WANG Xue-jin1*, WANG Wen-zhong2, LIANG Chun-jun3, WANG Zhi2 . Raman and Visible-Near Infrared Spectra of Cu(InGa)Se2 Films [J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2016, 36(10): 3197-3201. |
| [14] |
YU Lei1, 2, HONG Yong-sheng1, 2, ZHOU Yong1, 2*, ZHU Qiang1, 2. Inversion of Soil Organic Matter Content Using Hyperspectral Data Based on Continuous Wavelet Transformation[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2016, 36(05): 1428-1433. |
| [15] |
ZHANG Bao-yong1, 2, YU Yue1, 2*, WU Qiang1, 2, GAO Xia3 . Quantitative Analysis of the Hydration Process of Mine Gas Mixture Based on Raman Spectroscopy [J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2015, 35(07): 1912-1916. |
|
|
|
|
