Discrimination of Varieties of Dry Red Wines Based on Independent Component Analysis and BP Neural Network
WU Gui-fang1, 2, JIANG Yi-hong1, WANG Yan-yan1, HE Yong1*
1. College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou 310029, China 2. College of Mechanical and Electrical Engineering, Inner Mongolia Agriculture University, Huhhot 010018, China
Abstract:In order to achieve the rapid discrimination of the varieties of red wines, the authors selected 5 kinds of dry red wine for study with Vis/NIR spectroscopy. Firstly, Characteristics of the pattern were analyzed by independent component analysis (ICA). Through comparing the results of modeling performance by different number of independent components, 20 principal components presenting important information of spectra were confirmed as the best number of principal components. The 20 independent components (ICs)extracted by ICA were employed as the inputs of the BP neural networks, and then a three layers of BP neural network was built, category analysis was performed, and the work of building mathematics model and optimizing the algorithm was completed. Five samples from each variety and a total of 25 samples were selected randomly as the prediction sets. The remaining 150 samples were used as the training sets to build the training model, which was validated by the samples of the prediction sets. The recognition rate was 100%. In addition, based on the independent component analysis, the authors selected two characteristic wave bands in reference to vector loading map of mixed matrix. So the pattern recognition methods developed in this paper not only played a good role in the classification and discrimination, but also had the capability to extract the finger feature of red wine, and offered a new way for detecting and developing red wines.
吴桂芳1, 2,蒋益虹1,王艳艳1,何勇1* . 基于独立主成分和BP神经网络的干红葡萄酒品种的鉴别[J]. 光谱学与光谱分析, 2009, 29(05): 1268-1271.
WU Gui-fang1, 2, JIANG Yi-hong1, WANG Yan-yan1, HE Yong1* . Discrimination of Varieties of Dry Red Wines Based on Independent Component Analysis and BP Neural Network. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2009, 29(05): 1268-1271.
[1] ZHU Mei, LI Wen-yan, GUO Qi-chang(朱 梅,李文庵,郭其昌). Technology of Red Wine(葡萄酒工艺学). Beijing: Light Industry Publishing House(北京:轻工业出版社),1986. [2] LI Hua, WANG Hua, YUAN Chun-long, et al(李 华, 王 华, 袁春龙, 等). Red Wine Chemistry(葡萄酒化学). Beijing: Science Press(北京:科学出版社),2005. [3] Catarino S, Curvelo-Garcia A S, Bruno de Sousa R. Talanta, 2006, 70: 1073. [4] PENG De-hua(彭德华). Collected Works of Red Wine Brewing Technology(葡萄酒酿造技术文集). Beijing: Science Press(北京:科学出版社),2005. [5] LI Hua(李 华). Study of Red Wine Tasting(葡萄酒品尝学). Beijing: Science Press(北京:科学出版社),2006. [6] HE Yong, LI Xiao-li, SHAO Yong-ni(何 勇,李晓丽, 邵咏妮). Spectroscopy and Spectral Analysis(光谱学与光谱分析), 2006, 26(5): 850. [7] HE Yong, FENG Shui-juan, LI Xiao-li, et al(何 勇,冯水娟,李晓丽, 等). Spectroscopy and Spectral Analysis(光谱学与光谱分析), 2006, 26(11): 2021. [8] ZHU Hui-ju, ZHANG Zhuo-yong(朱惠菊, 张卓勇). Journal of Instrumental Analysis(分析测试学报), 2004, 23(6): 95. [9] HE Shu-hua, ZHANG Jie, QU Lian-ying(何淑华,张 洁,曲连颖). Acta Scientiarum Naturalium Universitatis Jilinensis(吉林大学自然科学学报),1999,(4):103. [10] LI Hong-yan, WANG Hua-kui(李鸿燕, 王华奎). Chinese Journal of Scientific Instrument(仪器仪表学报), 2006, 27(6): 664. [11] Hyvarinen A, Oja E. Neural Networks, 2000, 13: 411. [12] Haykin S. Neural Network-A Comprehensive Foundation, 1994, 1: 44. [13] He Yong, Li Xiaoli, Deng Xunfei. Journal of Food Engineering, 2007, 79(4): 1238. [14] TANG Qi-yi, FENG Ming-guang(唐启义,冯明光). DPS Data Processing System for Practical Statistics(实用统计分析及其DPS数据处理系统). Beijing: Science Press(北京:科学出版社),2002. [15] CHU Xiao-li, YUAN Hong-fu, LU Wan-zhen(褚小立,袁洪福,陆婉珍). Progress in Chemistry (化学进展),2004,16(4):528.