|
|
|
|
|
|
| Indirect Determination of Liquor Alcohol Content Based on Near-Infrared Spectrophotometry |
| HU Yao-qiang1, 2, 3, GUO Min2, YE Xiu-shen2, LI Quan2, LIU Hai-ning2*, WU Zhi-jian2 |
1. College of Ocean and Meteorology, Guangdong Ocean University, Zhanjiang 524088, China
2. Key Laboratory of Comprehensive and Highly Efficient Utilization of Salt Lake Resources, Qinghai Provincial Key Laboratory of Resources and Chemistry of Salt Lakes, Qinghai Institute of Salt Lakes, Chinese Academy of Sciences, Xining 810008, China
3. Key Laboratory of Climate, Resources and Environment in Continental Shelf Sea and Deep Sea of Department of Education of Guangdong Province, Guangdong Ocean University, Zhanjiang 524088, China
|
|
|
|
|
Abstract Alcohol is a key technical indicator of liquor, a favorite of consumers as a daily drinking. A simple and fast detection method for ethanol in liquor can help to improve the efficiency of a liquor inspection. Spectroscopy with the advantages of rapid and non-destructive can provide help for the analysis of ethanol content. Based on the good absorption effect of water molecules on infrared light, this paper explored the feasibility of indirect analysis of ethanol content in liquor by near-infrared spectrophotometry. By studying the effect of the optical path on the UV-VIS-NIR absorption spectra of water and ethanol, it is found that water molecules have an independent absorption peak at 1 448 nm. This absorption peak has a mutational point under the optical path of 10 mm. However, when the optical path is reduced to 1 mm, the shape of the absorption peak becomes symmetric and smooth, bringing the possibility of quantitative analysis. The absorption peak of aqueous ethanol solution in the range of 1 000~1 800 nm regularly decreases with the increase of ethanol content. It shows that the force between ethanol and water molecules do not affect their absorption spectrum. The linear equation between absorbance and alcohol content is obtained: A=1.38-0.013m% (R2=0.996 7) by extracting the absorbance at 1 448 nm. However, the fitting effect has a large relative deviation when the alcohol content is lower than 20% or higher than 80%. A better linear fitting equation with an excellent fitting effect is obtained: A=1.40-0.014m% (R2=0.999 3) after adjusting the fitting range to 20%~80%. The reliability of this method was tested through some purchased bulk and brand liquors. The results obtained by this method show a small relative deviation with the alcohol content marked on brand bottles, which is within the allowable error range of absorption spectrum analysis. Moreover, there are large relative deviations in the test measurement results of part of bulk liquors, which may be caused by the weaker quality control of bulk liquor. To some extent, this method has good accuracy and precision in detecting the alcohol content of liquor. This method has the advantages of convenience, rapid, no auxiliary reagent and wide linear range. It can be used as a test method for quickly analyzing the alcohol content of liquor.
|
|
Received: 2021-01-18
Accepted: 2021-04-21
|
|
|
|
Corresponding Authors:
LIU Hai-ning
E-mail: liuhn@isl.ac.cn
|
|
[1] Song X B, Zhu L, Wang X L, et al. Food Chemistry, 2019, 297: 124959.
[2] Fang C, Du H, Jia W, et al. Metabolites, 2019, 9(1): 1.
[3] Hu H C, Zeng T, Zhang S K, et al. Holzforschung, 2018, 72(11): 973.
[4] Chen S, Sha S, Qian M, et al. Journal of Food Science, 2017, 82(12): 2816.
[5] ZHAO Zhe, WANG Hui, WANG Hui-quan, et al(赵 喆，王 慧，王慧泉，等). Spectroscopy and Spectral Analysis(光谱学与光谱分析), 2019, 39(4): 1070.
[6] Shan R F, Chen Y, Meng L C, et al. Spectrochimica Acta Part A: Molecular and Biomolecular Spectroscopy, 2020, 224: 117455.
[7] Dong Q, Yu C, Li L, et al. Spectrochimica Acta Part A: Molecular and Biomolecular Spectroscopy, 2019, 22(5): 117183.
[8] Mahajan P G, Dige N C, Vanjare B D, et al. Journal of Molecular Structure, 2019, 1198: 126915.
[9] Khopkar S, Shankarling G. Dyes and Pigments, 2019, 170: 107645.
[10] Mohamed S H, Issa Y M, Elfeky S A. Spectrochimica Acta Part A: Molecular and Biomolecular Spectroscopy, 2019, 222: 117186.
[11] Mahmood A. Solar Energy, 2016, 123: 127.
[12] Ho C L, Wong W Y. Journal of Photochemistry and Photobiology C: Photochemistry Reviews, 2016, 28: 138.
[13] Dong W, Guo R N, Liu M, et al. Food Research International, 2019, 125: 108546.
[14] Yu H Y, Xie T, Xie J R, et al. Food Chemistry, 2019, 293: 8.
[15] Zheng J, Zhao D, Peng Z F, et al. Food Research International, 2018, 114: 64. |
| [1] |
GAO Feng1, 2, XING Ya-ge3, 4, LUO Hua-ping1, 2, ZHANG Yuan-hua3, 4, GUO Ling3, 4*. Nondestructive Identification of Apricot Varieties Based on Visible/Near Infrared Spectroscopy and Chemometrics Methods[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2024, 44(01): 44-51. |
| [2] |
BAO Hao1, 2，ZHANG Yan1, 2*. Research on Spectral Feature Band Selection Model Based on Improved Harris Hawk Optimization Algorithm[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2024, 44(01): 148-157. |
| [3] |
BAI Xue-bing1, 2, SONG Chang-ze1, ZHANG Qian-wei1, DAI Bin-xiu1, JIN Guo-jie1, 2, LIU Wen-zheng1, TAO Yong-sheng1, 2*. Rapid and Nndestructive Dagnosis Mthod for Posphate Dficiency in “Cabernet Sauvignon” Gape Laves by Vis/NIR Sectroscopy[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(12): 3719-3725. |
| [4] |
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. |
| [5] |
HU Cai-ping1, HE Cheng-yu2, KONG Li-wei3, ZHU You-you3*, WU Bin4, ZHOU Hao-xiang3, SUN Jun2. Identification of Tea Based on Near-Infrared Spectra and Fuzzy Linear Discriminant QR Analysis[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(12): 3802-3805. |
| [6] |
LIU Xin-peng1, SUN Xiang-hong2, QIN Yu-hua1*, ZHANG Min1, GONG Hui-li3. Research on t-SNE Similarity Measurement Method Based on Wasserstein Divergence[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(12): 3806-3812. |
| [7] |
LUO Li, WANG Jing-yi, XU Zhao-jun, NA Bin*. Geographic Origin Discrimination of Wood Using NIR Spectroscopy
Combined With Machine Learning Techniques[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(11): 3372-3379. |
| [8] |
ZHANG Shu-fang1, LEI Lei2, LEI Shun-xin2, TAN Xue-cai1, LIU Shao-gang1, YAN Jun1*. Traceability of Geographical Origin of Jasmine Based on Near
Infrared Diffuse Reflectance Spectroscopy[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(11): 3389-3395. |
| [9] |
YANG Qun1, 2, LING Qi-han1, WEI Yong1, NING Qiang1, 2, KONG Fa-ming1, ZHOU Yi-fan1, 2, ZHANG Hai-lin1, WANG Jie1, 2*. Non-Destructive Monitoring Model of Functional Nitrogen Content in
Citrus Leaves Based on Visible-Near Infrared Spectroscopy[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(11): 3396-3403. |
| [10] |
HUANG Meng-qiang1, KUANG Wen-jian2, 3*, LIU Xiang1, HE Liang4. Quantitative Analysis of Cotton/Polyester/Wool Blended Fiber Content by Near-Infrared Spectroscopy Based on 1D-CNN[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(11): 3565-3570. |
| [11] |
HUANG Zhao-di1, CHEN Zai-liang2, WANG Chen3, TIAN Peng2, ZHANG Hai-liang2, XIE Chao-yong2*, LIU Xue-mei4*. Comparing Different Multivariate Calibration Methods Analyses for Measurement of Soil Properties Using Visible and Short Wave-Near
Infrared Spectroscopy Combined With Machine Learning Algorithms[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(11): 3535-3540. |
| [12] |
KANG Ming-yue1, 3, WANG Cheng1, SUN Hong-yan3, LI Zuo-lin2, LUO Bin1*. Research on Internal Quality Detection Method of Cherry Tomatoes Based on Improved WOA-LSSVM[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(11): 3541-3550. |
| [13] |
HUANG Hua1, LIU Ya2, KUERBANGULI·Dulikun1, ZENG Fan-lin1, MAYIRAN·Maimaiti1, AWAGULI·Maimaiti1, MAIDINUERHAN·Aizezi1, GUO Jun-xian3*. Ensemble Learning Model Incorporating Fractional Differential and
PIMP-RF Algorithm to Predict Soluble Solids Content of Apples
During Maturing Period[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(10): 3059-3066. |
| [14] |
CHEN Jia-wei1, 2, ZHOU De-qiang1, 2*, CUI Chen-hao3, REN Zhi-jun1, ZUO Wen-juan1. Prediction Model of Farinograph Characteristics of Wheat Flour Based on Near Infrared Spectroscopy[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(10): 3089-3097. |
| [15] |
GUO Ge1, 3, 4, ZHANG Meng-ling3, 4, GONG Zhi-jie3, 4, ZHANG Shi-zhuang3, 4, WANG Xiao-yu2, 5, 6*, ZHOU Zhong-hua1*, YANG Yu2, 5, 6, XIE Guang-hui3, 4. Construction of Biomass Ash Content Model Based on Near-Infrared
Spectroscopy and Complex Sample Set Partitioning[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(10): 3143-3149. |
|
|
|
|
