|
|
|
|
|
|
Beer Freshness Detection Method Based on Spectral Analysis Technology |
WANG Nan1, ZHANG Li-fu1*, DENG Chu-bo1, PENG Ming-yuan1, 2, LU Xu-hui1, 2 |
1. Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing 100101, China
2. University of Chinese Academy of Sciences, Beijing 100049, China |
|
|
Abstract Traditional beer freshness detection methods usually need very expensive analytical instruments and chemical reagents, which consume a lot of time and cost a lot. In this paper, spectral analysis technology is used to explore the Beer Fresh Index (BFI), which can detect the agree of beer freshness rapidly and non-destructively. Specifically, the spectrum of beer samples was collected by PSR-3500 spectrometer every 24 hours. Then, the spectral data were processed by band selection and continuum removal. The enhanced spectra showed that the depth at 842.0 nm was decreased with the increase of storage time. Therefore, the characteristic spectral index (BFI) of beer freshness was constructed based on the depth at 842.0 nm. The experimental results show that BFI value decreases gradually with the increase of storage time, which can indicate the freshness of beer well. In addition, the sensitivity of BFI to the spectral equipment was evaluated by simulating different spectral resolution and signal-to-noise ratio levels. Specifically, the data with a spectral resolution of 5~40 nm and signal-to-noise ratio of 10~60 dB are generated by using the Gauss function distribution function and the average distribution function respectively and the BFI values are calculated and analyzed. Experiments show that when the spectral resolution is less than 15 nm and the signal-to-noise ratio is less than 10 dB, the absorption feature of 842.0 nm in the spectrum are gradually concealed, and BFI is difficult to indicate the freshness of beer. However, as long as the spectral resolution is better than 10 nm and the signal-to-noise ratio is not less than 35 dB in 798~872 nm, BFI can accurately indicate the freshness of beer. The requirements of BFI for the spectrometer are not strict. To sum up, the BFI proposed in this study can accurately indicate beer freshness, serve the design of portable beer freshness equipment, and promote the application of spectral analysis technology in non-destructive detection of beer quality.
|
Received: 2019-03-02
Accepted: 2019-07-14
|
|
Corresponding Authors:
ZHANG Li-fu
E-mail: zhanglf@radi.ac.cn
|
|
[1] DONG Jian-jun, HUANG Shu-xia, YU Jun-hong, et al(董建军, 黄淑霞, 余俊红, 等). Beer Tech.(啤酒科技), 2012,(12): 20.
[2] YAN Peng, YIN Hua, HAO Jun-guang, et al(闫 鹏, 尹 花, 郝俊光, 等). Chinese Journal of Analysis Laboratory(分析试验室), 2013,32(2): 117.
[3] LI Qian, XU Ye, XU Ming-ming, et al(李 千, 徐 烨, 徐明明, 等). Chinese Journal of Analysis Laboratory(分析试验室), 2011, 30(1): 98.
[4] BAI Yan-long, HE Li-dong, LIU Yue-qin(白艳龙, 贺立东, 刘月琴). Global Alcinfo Beer Tech(中外酒业·啤酒科技), 2016,(2): 45.
[5] SHAO Kai, LI Hong, ZHANG Wu-jiu(邵 铠, 李 红, 张五九). China Brewing(中国酿造), 2012, 31(4): 154.
[6] SUN Gui-fang, ZHAO Hai-feng, ZHAO Mou-ming(孙桂芳, 赵海锋, 赵谋明). Science and Technology of Food Industry(食品工业科技), 2012, 33(7): 49.
[7] SUN Tao, YIN Xue-hong, KANG Yong-feng, et al(孙 涛,银旭红,康永峰,等). Science and Technology of Food Industry(食品工业科技), 2010, 31(6): 72.
[8] MENG De-su(孟德素). Liquor Making Science Technology(酿酒科技), 2011, (4): 87.
[9] ZHOU Qing-mei, GUO Li-yun, LIN Zhi-ping(周青梅, 郭立芸, 林智平). Food and Fermentation Industries(食品与发酵工业), 2013, 39(10): 223.
[10] CHEN Xiao-hui, HUANG Jian, FU Yun-xia,et al(陈晓辉, 黄 剑, 付云侠, 等). Computer Engineering and Applications(计算机工程与应用), 2016, 52(16): 229.
[11] ZHAO Huan, HUAN Ke-wei, SHI Xiao-guang, et al(赵 环, 宦克为, 石晓光, 等). Chinese Journal of Analytical Chemistry(分析化学), 2018,46(1): 136.
[12] TIAN Yu-hong, CHEN Rong, YIN Hua, et al(田玉红, 陈 嵘, 尹 花, 等). Liquor Making Science Technology(酿酒科技), 2015, (3): 113. |
[1] |
GAO Hong-sheng1, GUO Zhi-qiang1*, ZENG Yun-liu2, DING Gang2, WANG Xiao-yao2, LI Li3. Early Classification and Detection of Kiwifruit Soft Rot Based on
Hyperspectral Image Band Fusion[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2024, 44(01): 241-249. |
[2] |
MU Da1, 2, WANG Qi-shu1, 2*, CUI Zong-yu1, 2, REN Jiao-jiao1, 2, ZHANG Dan-dan1, 2, LI Li-juan1, 2, XIN Yin-jie1, 2, ZHOU Tong-yu3. Study on Interference Phenomenon in Terahertz Time Domain
Spectroscopy Nondestructive Testing of Glass Fiber Composites[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(10): 3031-3040. |
[3] |
MA Qian1, 2, YANG Wan-qi1, 2, LI Fu-sheng1, 2*, CHENG Hui-zhu1, 2, ZHAO Yan-chun1, 2. Research on Classification of Heavy Metal Pb in Honeysuckle Based on XRF and Transfer Learning[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(09): 2729-2733. |
[4] |
CAI Jian-rong1, 2, HUANG Chu-jun1, MA Li-xin1, ZHAI Li-xiang1, GUO Zhi-ming1, 3*. Hand-Held Visible/Near Infrared Nondestructive Detection System for Soluble Solid Content in Mandarin by 1D-CNN Model[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(09): 2792-2798. |
[5] |
TANG Ruo-han1, 2, LI Xiu-hua1, 2*, LÜ Xue-gang1, 2, ZHANG Mu-qing2, 3, YAO Wei2, 3. Transmittance Vis-NIR Spectroscopy for Detecting Fibre Content of
Living Sugarcane[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(08): 2419-2425. |
[6] |
LUO Zheng-fei1, GONG Zheng-li1, 2, YANG Jian1, 2*, YANG Chong-shan2, 3, DONG Chun-wang3*. Rapid Non-Destructive Detection Method for Black Tea With Exogenous Sucrose Based on Near-Infrared Spectroscopy[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(08): 2649-2656. |
[7] |
ZHANG Yue1, 2, LI Yang1, 2, SONG Yue-peng1, 2*. Nondestructive Detection of Slight Mechanical Damage of Apple by Hyperspectral Spectroscopy Based on Stacking Model[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(07): 2272-2277. |
[8] |
WANG Yu-qi, LI Bin, ZHU Ming-wang, LIU Yan-de*. Optimizations of Sample and Wavelength for Apple Brix Prediction Model Based on LASSOLars Algorithm[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(05): 1419-1425. |
[9] |
ZHANG Bao-ping1, NING Tian1, ZHANG Fu-rong1, CHEN Yi-shen1, ZHANG Zhan-qin2, WANG Shuang1*. Study on Raman Spectral Characteristics of Breast Cancer Based on
Multivariable Spectral Data Analysis Methods[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(02): 426-434. |
[10] |
SHENG Qiang1, 2, ZHENG Jian-ming1*, LIU Jiang-shan2, SHI Wei-chao1, LI Hai-tao2. Advances and Prospects in Inner Surface Defect Detection Based on Cite Space[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(01): 9-15. |
[11] |
JIANG Xiao-gang1, ZHU Ming-wang1, YAO Jin-liang1, LI Bin1, LIAO Jun1, LIU Yan-de1*, ZHANG Jian-yi2, JING Han-song2. Research on Parameter Optimization of Apple Sugar Model Based on Near-Infrared On-Line Device[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(01): 116-121. |
[12] |
ZHU Jin-yan, ZHU Yu-jie*, FENG Guo-hong*, ZENG Ming-fei, LIU Si-qi. Optimization of Near-Infrared Detection Model of Blueberry Sugar Content Based on Deep Belief Network and Hybrid Wavelength Selection Method[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2022, 42(12): 3775-3782. |
[13] |
WANG Wei, LI Yong-yu*, PENG Yan-kun, YANG Yan-ming, YAN Shuai, MA Shao-jin. Design and Experiment of a Handheld Multi-Channel Discrete Spectrum Detection Device for Potato Processing Quality[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2022, 42(12): 3889-3895. |
[14] |
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. |
[15] |
JIN Cheng-qian1, 2, GUO Zhen1, ZHANG Jing1, MA Cheng-ye1, TANG Xiao-han1, ZHAO Nan1, YIN Xiang1. Non-Destructive Detection and Visualization of Soybean Moisture Content Using Hyperspectral Technique[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2022, 42(10): 3052-3057. |
|
|
|
|