为满足多年生苦荞育种工作的需要, 采用近红外光谱分析技术结合定量偏最小二乘法对多年生苦荞叶片蛋白质和γ-氨基丁酸(GABA)含量进行了快速测定研究, 实验使用了222份多年生苦荞材料, 扫描光谱后测定其化学值。 研究发现样品蛋白质含量的平均值、 最大值和最小值含量分别是164, 331和121 mg·g-1; 样品GABA含量的平均值、 最大值和最小值含量分别是2.489, 3.968和1.439 mg·g-1。 蛋白质建模结果: 采用不同光谱区建模时, 建模集的平均决定系数（R2）、 校正标准差（SEP）和平均相对误差（RSD）分别是93.46%, 0.63和3.82%, 检验集的平均R2, SEP和RSD分别是91.77%, 0.88和5.28%。 采用不同比例的建模样品和检验样品时, 建模集的平均R2, SEP和RSD分别是93.55%, 0.63和3.82%, 检验集的平均R2, SEP和RSD分别是92.18%, 0.87和5.20%。 采用4 000～9 000 cm-1光谱范围, 二阶导数（13）预处理光谱, 建模集与检验集的比例为4∶1, 模型最优, 其建模集内部交叉R2, SEP和RSD分别是93.57%, 0.55和3.38%, 检验集内部交叉R2, SEP和RSD分别是93.35%, 0.73和4.40%。 GABA建模结果: 采用不同光谱区建模时, 建模集的平均R2, SEP和RSD分别是86.28%, 0.21和8.30%, 检验集的平均R2, SEP和RSD分别是84.35%, 0.22和8.76%。 采用不同比例的建模样品和检验样品时, 建模集的平均R2, SEP和RSD分别是88.51%, 0.20和8.04%, 检验集的平均R2, SEP和RSD分别是86.80%, 0.21和8.40%。 4 000～10 000 cm-1光谱范围, 原始光谱, 建模集与检验集的比例为4∶1, 模型最优, 其建模集内部交叉R2, SEP和RSD分别是93.28%, 0.15和6.10%, 检验集内部交叉R2, SEP和RSD分别是91.49%, 0.17, 6.68%。 证明了使用近红外光谱技术定量测定多年生苦荞叶片蛋白质和GABA含量的可行性以及模型的稳定性。

In order to aidthe buckwheat breeding work, quantitative identification models for testing the content of protein and γ-aminobutyric acid (GABA) in perennial buckwheat leaves were built by near-infrared reflectance spectroscopy (NIRS) with quantitative partial least squares (QPLS). NIR spectra of 222 buckwheat samples were collected, and calibration models were established based on the spectra and chemical values. It was found the average, maximum and minimum protein contents of the samples were 164, 331 and 121 mg·g-1, respectively; the mean, maximum and minimum GABA contents of the samples were 2.489, 3.968 and 1.439 mg·g-1, respectively. Protein modeling results were as follows: when using different spectral regions, themean coefficient of determination (R2), standard error of calibration(SEC) and relative standard deviation(RSD) for the calibration set was 93.46%, 0.63 and 3.82% respectively, for the validation set, the mean R2, SEC and RSD was 91.77%, 0.88 and 5.28% respectively; when using different ratios of the modeling samples and testing samples, the R2, SEC and RSD for the calibration set was 93.55%, 0.63 and 3.82%, for the validation set, the mean R2, SEC and RSD was 92.18%, 0.87 and 5.20% respectively; when through second derivative (13) pretreatment, the wave number range of 4 000~9 000 cm-1 was appropriate for modeling (calibration sets∶validation set=4∶1), the R2, SEC and RSD for the calibration set was 93.57%, 0.55 and 3.38% respectively, for the validation set, the mean R2, SEC and RSD was 93.35%, 0.73 and 4.40% respectively. GABA modeling results were as follows: using different spectral regions, the mean R2, SEC and RSD for the calibration set was 86.28%, 0.21 and 8.30% respectively, for the validation set, the mean R2, SEC and RSD was 84.35%, 0.22 and 8.76% respectively; using different ratios of the modeling samples and testing samples, the mean R2, SEC and RSD for the calibration set was 88.51%, 0.20 and 8.04%, for the validation set, the mean R2, SEC and RSD was 86.80%, 0.21 and 8.40% respectively; no pretreatment, the wave number range of 4 000~10 000 cm-1 was appropriate for modeling (calibration sets∶validation set=4∶1), the R2, SEC and RSD for the calibration set was 93.28%, 0.15 and 6.10% respectively, for the validation set, the mean R2, SEC and RSD was 91.49%, 0.17 and 6.68% respectively. This study has demonstrated the feasibility and reliability of using NIRS to detect the content of protein and GABA in perennial buckwheat leaves.