|
|
|
|
|
|
| Research on the Rapid Discrimination Technology of the Chinese Liquor Brands Based on the Moving Window Correlation Coefficient Spectral Method |
| ZHANG Zheng-yong1, 3, SHA Min1, 3, GUI Dong-dong1, YE Xiao-jing4, WANG Hai-yan2, 3* |
1. School of Management Science and Engineering, Nanjing University of Finance and Economics, Nanjing 210023, China
2. School of Management Engineering and Electronic Commerce, Zhejiang Gongshang University, Hangzhou 310018, China
3. Jiangsu Institute of Quality and Safety Engineering, Nanjing 210023, China
4. School of Mathematics, Southwest Jiaotong University, Chengdu 611756, China |
|
|
|
|
Abstract A new strategy was proposed in this work to meet the urgent needs of the rapid discrimination technology of Chinese liquor brands. The ultraviolet spectra were used as the inputs, the moving window correlation coefficient was applied as the analytical method, and the Haizhilan spirit was used as the research object. The results showed that the quality of different batches of Haizhilan spirit was consistent in the experimental wavelength interval. The correlation coefficient of ultraviolet spectral original data and the moving window correlation coefficient spectral were all above 0.99. The processing techniques and raw materials of Haizhilan spirit and the other series of Yanghe spirits belonging to the same company had a higher similarity. It was difficult to quantitatively discriminate their brands only by using the ultraviolet spectral original data with correlation coefficient method. The moving window method could effectively improve the spectral detail differences, highlight the shape, intensity and trend differences of the spectral feature bands, so it could enhance the ability of the experimental spectra to identify and analyze the fine structure. After an overall investigation, the moving window correlation coefficient combined with ultraviolet feature spectra (270~295 nm) improved the differences among Yanghe spirits, and then Haizhilan spirit could be discriminated from the other Yanghe spirits with high similarities. Six brands of Chinese liquor and ethanol as the control groups were used to further demonstrate the feasibility of this approach. The innovation of this work is to propose a novel analytical idea for rapid identification of the Chinese liquor brands based on the moving window correlation coefficient spectral method. This method also has the advantages of rapidity, simplicity, and has a great potential in the quality and safety discrimination of other foods.
|
|
Received: 2016-11-27
Accepted: 2017-03-11
|
|
|
|
Corresponding Authors:
WANG Hai-yan
E-mail: njue_10@163.com
|
|
[1] Zheng Xiaowei，Han Beizhong. Journal of Ethnic Foods，2016，3(1): 19.
[2] SHAO Yan，ZHANG Su-yi，ZHU Cheng，et al(邵 燕，张宿义，祝 成，等). China Brewing(中国酿造)，2012，31(8): 92.
[3] Yao Feng，Yi Bin，Shen Caihong，et al. Scientific Reports，2015，5: 9553.
[4] Wang Peipei，Li Zhao，Qi Tingting，et al. Food Chemistry，2015，169: 230.
[5] Weeranantanaphan Jittima, Downey Gerard. Journal of the Institute of Brewing，2010，116(1): 56.
[6] Zhu Wenyou，Ren Cong，Nie Yao，et al. Food Control，2016，64: 37.
[7] LIANG Yi-zeng，XU Qing-song(梁逸曾，许青松). Instrumental Analysis of Complex Systems——White, Gray and Black Analytical System and Their Multivariate Methods(复杂体系仪器分析: 白、灰、黑分析体系及其多变量解析方法). Beijing: Chemical Industry Press(北京: 化学工业出版社)，2012. 1.
[8] ZHANG Zheng-yong，SONG Chao，SHA Min，et al(张正勇，宋 超，沙 敏，等). Liquor-Making Science Technology(酿酒科技)，2016，11: 20.
[9] Zhang Zhengyong，Jiang Jiuying，Wang Guoxiang，et al. American Journal of Analytical Chemistry，2015，6: 395.
[10] Peng Qi，Tian Rungang，Chen Feiran，et al. Food Chmeistry，2015，178: 301.
[11] NIE Qing-qing，FAN Wen-lai，XU Yan，et al(聂庆庆，范文来，徐 岩，等). Science and Technology of Food Industry(食品工业科技)，2012，33(12): 68. |
| [1] |
WANG Zhi-qiang1, CHENG Yan-xin1, ZHANG Rui-ting1, MA Lin1, GAO Peng1, LIN Ke1, 2*. Rapid Detection and Analysis of Chinese Liquor Quality by Raman
Spectroscopy Combined With Fluorescence Background[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(12): 3770-3774. |
| [2] |
WANG Qi-biao1, HE Yu-kai1, LUO Yu-shi1, WANG Shu-jun1, XIE Bo2, DENG Chao2*, LIU Yong3, TUO Xian-guo3. Study on Analysis Method of Distiller's Grains Acidity Based on
Convolutional Neural Network and Near Infrared Spectroscopy[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(12): 3726-3731. |
| [3] |
LI Yang1, LI Xiao-qi1, YANG Jia-ying1, SUN Li-juan2, CHEN Yuan-yuan1, YU Le1, WU Jing-zhu1*. Visualisation of Starch Distribution in Corn Seeds Based on Terahertz Time-Domain Spectral Reflection Imaging Technology[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(09): 2722-2728. |
| [4] |
SU Xin-yue1, MA Yan-li2, ZHAI Chen3, LI Yan-lei4, MA Qian-yun1, SUN Jian-feng1, WANG Wen-xiu1*. Research Progress of Surface Enhanced Raman Spectroscopy in Quality and Safety Detection of Liquid Food[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(09): 2657-2666. |
| [5] |
ZHANG Peng1, 3, YANG Yi-fan1, WANG Hui1, TU Zong-cai1, 2, SHA Xiao-mei2, HU Yue-ming1*. A Review of Structural Characterization and Detection Methods of Glycated Proteins in Food Systems[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(09): 2667-2673. |
| [6] |
LI Xin-xing1, 2, ZHANG Ying-gang1, MA Dian-kun1, TIAN Jian-jun3, ZHANG Bao-jun3, CHEN Jing4*. Review on the Application of Spectroscopy Technology in Food Detection[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(08): 2333-2338. |
| [7] |
LI Chun-ying1, WANG Hong-yi1, LI Yong-chun1, LI Jing1, CHEN Gao-le2, FAN Yu-xia2*. Application Progress of Surface-Enhanced Raman Spectroscopy for
Detection Veterinary Drug Residues in Animal-Derived Food[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(06): 1667-1675. |
| [8] |
SHI Zhi-feng1, 2, LIU Jia2, XIAO Juan2, ZHENG Zhi-wen1*. Investigation of Novel Method for Detecting Vanillin Based on X-Ray Diffraction Technology[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(05): 1563-1568. |
| [9] |
XU Wei-xuan1, CHEN Wen-bin2, 3*. Determination of Barium in Purple Clay Products for Food Contact by
Energy Dispersive X-Ray Fluorescence Spectrometry[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(02): 475-483. |
| [10] |
SUN Xu-dong1, 2, LIAO Qi-cheng1, HAN Xi3, Hasan Aydin4, XIE Dong-fu1, GONG Zhi-yuan1, FU Wei1, WANG Xin-peng1. Research on Online Detection of Tea Stalks and Insect Foreign Bodies by Near-Infrared Spectroscopy and Fluorescence Image Combined With
Electromagnetic Vibration Feeding[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(01): 100-106. |
| [11] |
LI Ming1*, HAN Dong-hai2*, LU Ding-qiang1, LU Xiao-xiang1, CHAI Chun-xiang1, LIU Wen3, SUN Ke-xuan1. Research Progress of Universal Model of Near-Infrared Spectroscopy in Agricultural Products and Foods Detection[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2022, 42(11): 3355-3360. |
| [12] |
TANG Yi-yun1, 4, LIU Rui2, WANG Lu2, LÜ Hui-ying1, 4, TANG Zhong-hai1, 4*, XIAO Hang1, 3, GUO Shi-yin1, 4, FAN Wei1, 4*. Application of One-Class Classification Combined With Spectral Analysis in Food Authenticity Identification[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2022, 42(11): 3336-3344. |
| [13] |
CAI Song-tao1, XIE Hua-lin2, HUANG Jian-hua3*. Analysis of Heavy Metal Cd in Cereal-Based Complementary Foods for
Infants and Young Children by Inductively Coupled Plasma Tandem
Mass Spectrometry (ICP-MS/MS)[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2022, 42(09): 2818-2823. |
| [14] |
MA Ling-kai, ZHU Shi-ping*, MIAO Yu-jie, WEI Xiao, LI Song, JIANG You-lie, ZHUO Jia-xin. The Discrimination of Organic and Conventional Eggs Based on
Hyperspectral Technology[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2022, 42(04): 1222-1228. |
| [15] |
ZHOU Jun1, 2, YANG Yang2, YAO Yao2, LI Zi-wen3, WANG Jian3, HOU Chang-jun1*. Application of Mid-Infrared Spectroscopy in the Analysis of Key Indexes of Strong Flavour Chinese Spirits Base Liquor[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2022, 42(03): 764-768. |
|
|
|
|
