Abstract：Buckwheat is rich in lysine, which is a lack in cereal crops, making it different from other cereal crops and has high economic value. Traditional determination of amino acids was time-consuming and expensive. In order to meet the needs of breeding of golden Tartary buckwheat, this study selected near-infrared spectroscopy combined with an artificial neural network algorithm to establish a near-infrared model for rapid determination of amino acid content in buckwheat leaves. A total of 255 samples with different amino acid contents were studied, and their chemical values were determined after scanning spectra. It was found that the content of threonine (Thr) in the samples ranged from 5.307 to 14.374 mg·g-1. Valine (Val) content ranged from 6.137 to 16.204 mg·g-1. The content of methionine (Met) ranged from 0.308 to 3.049 mg·g-1. The content of isoleucine (Ile) ranged from 5.259 to 14.134 mg·g-1. Leucine (Leu) content ranged from 9.730 to 26.061 mg·g-1. The content of phenylalanine (Phe) ranged from 5.936 to 17.223 mg·g-1. Lysine (Lys) content ranged from 6.640 to 17.280 mg·g-1. The content of glutamic (Glu) ranged from 10.984 to 27.740 mg·g-1. Aspartic (Asp) content ranged from 6.437 to 17.280 mg·g-1. Serine (Ser) content ranged from 3.467 to 8.312 mg·g-1. Arginine (Arg) content ranged from 4.937 to 14.772 mg·g-1. The content of Alanine (Ala) ranged from 3.329 to 6.885 mg·g-1. Histidine (His) content ranged from 1.946 to 4.798 mg·g-1. The content of glycine (Gly) ranged from 4.196 to 9.264 mg·g-1. Proline (Pro) content ranges from 1.024 to 5.672 mg·g-1. The content of tyrosine (Tyr) ranged from 0.176 to 1.173 mg·g-1. The content of cysteine (Cys) ranged from 0.422 to 1.926 mg·g-1. During each modeling, 50 samples were randomly selected and randomly divided into the training set and test set at a ratio of 4∶1. After data normalization, the neural network structure 1102-9-1 was used for model construction. The simulation results of Arg and Asp near-infrared models were the best, the correlation coefficient (R2) between the predicted value and the real value was greater than 0.97, and the average relative error (RSD) was less than 10%. Simulation test process found, Val, Tyr, Ile, Ser, Ala, Thr, His, Phe, Gly and Lys of model prediction and the real value of R2 are greater than 0.90, the RSD is less than 10%, models are available; When the models of Met and Cys were tested in simulation, the R2 between the predicted value and the true value were both greater than 0.78, but the RSD was greater than 10%, so the model was not available. The results showed that golden Tartary buckwheat leaves had a high content of essential amino acids and had high application value. The analysis method of near infrared spectroscopy combined with an artificial neural network could be used to predict the amino acid content of buckwheat, which provided convenience for the breeding of high-quality buckwheat.
Key words：Buckwheat; Near infrared spectroscopy; Artificial neural network; Amino acid; Model
朱丽伟，严金欣，黄 娟，石桃雄，蔡 芳，李洪有，陈庆富，陈其皎. 近红外光谱和人工神经网络的金苦荞氨基酸快速测定[J]. 光谱学与光谱分析, 2022, 42(01): 49-55.
ZHU Li-wei, YAN Jin-xin, HUANG Juan, SHI Tao-xiong, CAI Fang, LI Hong-you, CHEN Qing-fu, CHEN Qi-jiao. Rapid Determination of Amino Acids in Golden Tartary Buckwheat Based on Near Infrared Spectroscopy and Artificial Neural Network. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2022, 42(01): 49-55.
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