1. School of Food and Biological Engineering, Jiangsu University, Zhenjiang 212013, China
2. Jiangsu King’s Luck Brewer Co.,Ltd., Lianshui 223411, China
3. Institute of Agricultural Engineering, Jiangsu University, Zhenjiang 212013, China
Abstract:The judgment of base liquor grades is of vital importance in quality control, it’s also an important basis for liquor classified storage and blending. At present, the grades of base liquor are identified by the artificial sensory evaluation of liquor assessment professionals combined with the determination of the main esters by gas chromatography based on the preliminary classification of the workers in workshop according to their experience. However, the artificial sensory evaluation and gas chromatography are cumbersome and time-consuming, which cannot provide real-time information and rapid detection. In recent years, infrared spectroscopy has been widely used in the field of food identification because of its quick analysis, nondestructive testing, high sensitivity and good reproducibility. In this paper, the Fourier transform infrared (FTIR) spectroscopy method combined with attenuated total reflectance (ATR) were applied for the effective qualitative and quantitative analysis of base liquor. FTIR-ATR spectrum in the mid-IR region was used for classifying different grades of base liquor and quantitative analysis of the main esters combined with chemometrics. The results showed that the FTIR-ATR combined with liner discriminant analysis (LDA) could distinguish four different grades of base liquor. The accuracy of the training set and test set both were 100%. The FTIR-ATR combined with back propagation artificial neural network (BPANN) could also achieve an accurate determination of different grades of base liquor effectively, and the correct recognition rate of the training set and test set both were over 95%. The results showed a comparatively good recognition performance of the two methods. The FTIR-ATR combined with synergy interval partial least squares (siPLS) models for predicting the content of the ester compounds were realized with good results. The correlation coefficients of the training set of the ethyl hexanoate, ethyl lactate, ethyl acetate, and ethyl butyrate in the siPLS model were 0.986 4, 0.991 5, 0.970 2 and 0.951 4, respectively. And the correlation coefficients of the test set were 0.982 4, 0.961 9, 0.905 2, and 0.808 0, respectively. These results corroborate the hypothesis that the FTIR-ATR can effectively achieve accurate determination of different grades of base liquor combined with chemometrics, and the quantitative model for rapidly detecting the main esters in base liquor is good, which can meet testing requirements during liquor production. FTIR-ATR combined with chemometrics provide a fast and accurate method for the determination of the grades of base liquor which can effectively enhance the level of intelligent production of liquor.
[1] SHAO Chun-fu, LI Chang-wen, WANG Shan, et al(邵春甫, 李长文, 王 珊, 等). China Brewing(中国酿造), 2013, 32(4): 15.
[2] LI Yun-hui, ZHANG Su-yi, ZHOU Jun, et al(李云辉, 张宿义, 周 军, 等). Liquor-Making Science & Technology(酿酒科技), 2010, 37(6): 22.
[3] LIU Jian-xue, ZHANG Wei-wei, HAN Si-hai, et al(刘建学, 张卫卫, 韩四海, 等). Food Science(食品科学), 2016, 37(4): 181.
[4] Anjos O, Santos A J A, Estevinho L M, et al. Food Chemistry, 2016, 205: 28.
[5] Silva S D, Feliciano R P, Boas L V, et al. Food Chemistry, 2014, 150(2): 489.
[6] Peng B, Ge N, Cui L, et al. LWT - Food Science and Technology, 2016, 66: 86.
[7] Santos C A T D, Páscoa R N M J, Porto P A L S, et al. LWT-Food Science and Technology, 2016, 67: 181.
[8] Qin O, Chen Q, Zhao J. Spectrochimica Acta Part A Molecular & Biomolecular Spectroscopy, 2015, 154: 42.
[9] ZHAO Jie-wen, CHEN Quan-sheng(赵杰文, 陈全胜). Quality and Safety Detection and Analysis Method of the Tea(茶叶质量与安全检测技术及分析方法). Beijing: China Light Industry Press(北京: 中国轻工业出版社), 2011.
[10] Shen T, Zou X, Shi J, et al. Food Analytical Methods, 2015, 9(1): 1.
[11] Ouyang Q, Chen Q, Zhao J, et al. Food & Bioprocess Technology, 2012, 6(9): 2486.
[12] DING Qing-zhen, LIU Ling-ling, WU Yan-wen, et al(丁轻针, 刘玲玲, 武彦文, 等). Spectroscopy and Spectral Analysis(光谱学与光谱分析), 2014, 34(10): 2690.
[13] TAO Si-jia, LI Meng-hua, LI Jing-ming, et al(陶思嘉, 李梦华, 李景明, 等). Analytical Chemistry(分析化学), 2014, 42(2): 215.