• Spectroscopy and Spectral Analysis
  • Vol. 42, Issue 1, 49 (2022)
Li-wei ZHU*, Jin-xin YAN, Juan HUANG, Tao-xiong SHI, Fang CAI, Hong-you LI, Qing-fu CHEN*;, and Qi-jiao CHEN*;
Author Affiliations
  • Research Center of Buckwheat Industry Technology, Guizhou Normal University, Guiyang 550001, China
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    DOI: 10.3964/j.issn.1000-0593(2022)01-0049-07 Cite this Article
    Li-wei ZHU, Jin-xin YAN, Juan HUANG, Tao-xiong SHI, Fang CAI, Hong-you LI, Qing-fu CHEN, Qi-jiao CHEN. Rapid Determination of Amino Acids in Golden Tartary Buckwheat Based on Near Infrared Spectroscopy and Artificial Neural Network[J]. Spectroscopy and Spectral Analysis, 2022, 42(1): 49 Copy Citation Text show less
    Simulation results of near-infrared model of essential amino acids(a): Phenylalanine; (b): Methionine; (c): Leucine; (d): Threonine; (e): Valine; (f): Isoleucine; (g): Lysine
    Fig. 1. Simulation results of near-infrared model of essential amino acids
    (a): Phenylalanine; (b): Methionine; (c): Leucine; (d): Threonine; (e): Valine; (f): Isoleucine; (g): Lysine
    Near infrared simulation of non-essential amino acids(a): Glutamic acid; (b): Cysteine; (c): Aspartic acid; (d): Serine;(e): Arginine; (f): Alanine; (g): Histidine; (h): Glycine; (i): Proline; (j): Tyrosine
    Fig. 2. Near infrared simulation of non-essential amino acids
    (a): Glutamic acid; (b): Cysteine; (c): Aspartic acid; (d): Serine;(e): Arginine; (f): Alanine; (g): Histidine; (h): Glycine; (i): Proline; (j): Tyrosine
    氨基酸最小值/
    (mg·g-1)
    最大值/
    (mg·g-1)
    平均值/
    (mg·g-1)
    苏氨酸(Thr)5.30714.3747.483
    缬氨酸(Val)6.13716.2048.238
    甲硫氨酸(Met)0.3083.0490.909
    异亮氨酸(Ile)5.25914.1347.083
    亮氨酸(Leu)9.73026.06113.119
    苯丙氨酸(Phe)5.93617.2238.212
    赖氨酸(Lys)6.64017.2809.135
    谷氨酸(Glu)10.98427.74020.185
    天冬氨酸(Asp)6.43717.2809.135
    丝氨酸(Ser)3.4678.3125.911
    精氨酸(Arg)4.93714.7729.654
    丙氨酸(Ala)3.3296.8855.208
    组氨酸(His)1.9464.7983.202
    甘氨酸(Gly)4.1969.2646.828
    脯氨酸(Pro)1.0245.6722.607
    酪氨酸(Tyr)0.1761.1730.647
    半胱氨酸(Cys)0.4221.9261.296
    Table 1. Contents of amino acid in golden tartary buckwheat leaves using chemical method
    氨基酸
    模型
    参试数据的相对误差平均相对
    误差/%
    相关
    系数
    Phe0.170 90.032 00.020 70.371 30.072 60.072 60.067 90.087 80.002 30.036 69.350.911 9
    Met0.694 90.388 70.085 40.119 60.253 60.010 20.043 00.269 00.024 50.709 625.980.914 9
    Leu0.117 50.085 60.227 70.127 90.107 10.013 00.002 60.038 40.114 70.107 19.410.936 4
    Thr0.026 30.037 80.201 70.016 20.271 70.028 50.027 90.006 50.030 40.011 86.590.917 6
    Val0.025 70.080 50.110 80.036 80.008 60.113 90.045 30.290 20.105 30.035 48.530.935 1
    Ile0.099 40.043 40.049 40.089 90.055 80.055 80.304 70.056 70.006 90.100 78.630.929 3
    Lys0.122 60.120 10.015 70.086 70.197 30.001 40.040 40.022 70.005 30.176 97.890.917 0
    Ser0.032 00.027 00.022 20.052 20.004 90.000 80.027 00.014 00.003 60.055 02.390.926 5
    Arg0.044 00.006 00.142 70.013 30.006 50.053 10.008 10.055 60.078 50.014 54.220.975 3
    Ala0.103 40.169 00.129 10.134 20.012 20.031 10.055 50.158 10.024 30.007 28.240.920 0
    His0.166 10.085 30.016 10.084 40.006 20.024 00.282 80.010 40.130 10.000 88.060.912 1
    Gly0.009 80.088 40.168 10.005 60.009 70.013 30.268 60.010 00.232 30.010 08.160.910 7
    Glu0.055 70.152 70.180 00.179 80.120 00.041 30.111 50.046 60.017 10.021 09.260.897 5
    Cys0.004 40.979 30.049 70.543 50.125 70.138 20.417 30.442 00.469 00.153 433.230.788 0
    Asp0.090 50.082 20.140 50.045 40.063 50.070 70.000 30.087 00.055 80.030 86.670.971 3
    Pro0.002 40.107 70.010 70.230 70.027 40.065 30.226 30.013 90.018 70.182 77.860.899 1
    Tyr0.008 40.022 00.025 50.299 50.028 30.064 80.071 90.215 10.082 10.008 48.260.931 2
    Table 2. Simulation results of near-infrared model of amino acids
    Li-wei ZHU, Jin-xin YAN, Juan HUANG, Tao-xiong SHI, Fang CAI, Hong-you LI, Qing-fu CHEN, Qi-jiao CHEN. Rapid Determination of Amino Acids in Golden Tartary Buckwheat Based on Near Infrared Spectroscopy and Artificial Neural Network[J]. Spectroscopy and Spectral Analysis, 2022, 42(1): 49
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