Prediction of Acetic Acid Concentration in Chinese Liquors Based on Fluorescence Spectrumand Simulated Annealing Algorithm
XU Lei1, ZHU Wei-hua1*, YAO Hong-bing1*, CHEN Guo-qing2, QIAO Hua3, ZHU Feng4，5, GENG Ying5, TANG Chun-mei1, HE Xiang1
1. College of Science, Hohai University, Nanjing 210098, China
2. School of Science, Jiangnan University, Wuxi 214122, China
3. Department of Chemistry, Basic College of Shanxi Medical University, Taiyuan 030051, China
4. CCCC Airport Investigation and Design Institute Co., Ltd., Guangzhou 510000, China
5. CCCC-FHDI Engineering Co., Ltd., Guangzhou 510000, China
Abstract：In recent years, the industry of vintage liquor market is not standardized. It is of deep significance and market value to study year liquor. The concentration of monomer in liquor will change with liquor age, so the detection of monomer concentration in liquor can be used to identify liquor quality and age. In this paper, based on the three-dimensional fluorescence spectrum of a certain domestic puree liquor brand, the concentration prediction model of acetic acid is studied. The main contents and innovations are as follows: Firstly, wavelet decomposition and derivative preprocessing are performed on the original spectrum. It is found that the first layer and the second layer of the wavelet mainly present the characteristics of noise, the concentration information is mainly distributed in the third and fourth layer signals. The intensity distribution of fluorescence emission spectra with different excitation wavelengths is different. At present, there is no unified method to select the appropriate excitation wavelength. According to wavelet decomposition signal, this article introduced effective signal strength and obtained the proper modeling excitation wavelength (200 nm). The derivative spectrum has more detailed features than the original spectrum, which can improve the spectral resolution. Secondly, the correlation between acetic acid concentration and fluorescence spectrum was studied. In general, the correlation between the original fluorescence spectrum and the concentration of acetic acid is not high. The correlation between the wavelet decomposition spectrum and derivative spectrum and the concentration is more than 0.8 and shows more discrete correlation peaks. Therefore, the wavelet decomposition spectrum and derivative spectrum contain more information about the acetic acid concentration, which has a wider distribution than the original spectrum’s. Finally, the partial least squares (PLS) multiple regression model of acetic acid concentration was studied based on fluorescence spectra and simulated annealing. The results show that the root means square error of the prediction set of acetic acid concentration in the original spectrum is as high as 70.03 mg·L-1, so its model’s effect is poor. Wavelet decomposition spectrum and derivative spectrum have better prediction effect because the multiple correlations between the spectra is reduced, and the resolution is improved. The second derivative spectral modeling is the best. The root mean square error of the prediction set is 20.32 mg·L-1, and the correlation coefficient is 0.9998. The spectral information density curve based on 1000 simulated annealing algorithms shows that the second derivative spectrum contains more acetic acid concentration information than the original spectrum. This study provides a simple optical method for predicting the concentration of substances in the year liquor. The research methods have a certain reference value for studying the concentration prediction of multi-component gradual change system.
Key words：Fluorescence spectrum; Year liquor; Acetic acid; Simulated annealing algorithm
