Variety Identification of Chinese Cabbage Seeds Using Visible and Near-Infrared Spectroscopy
Variety Identification of Chinese Cabbage Seeds Using Visible and Near-Infrared Spectroscopy
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DOI：
10.13031/2013.25382
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发表时间：
2008-10
影响因子：
1.5
通讯作者：
Di Wu;L. Feng;Yong He;Y. Bao
中科院分区：
文献类型：
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作者：
Di Wu;L. Feng;Yong He;Y. Bao
利用可见和近红外光谱技术对大白菜种子进行了品种鉴别。采用化学计量学方法对120个样品（6个品种各20个样品）建立了鉴别模型。基于主成分分析建立了一类相似的软独立建模（SIMCA）模型，在40个样本的标定集上取得了约94%的良好识别效果。采用偏最小二乘判别分析（PLS-DA）和最小二乘支持向量机（LS-SVM）进一步提高了正确率。基于40个样本的校正集，LS-SVM的正确回答率达到97%以上，优于PLS-DA的正确回答率（81%）。基于不同样本数的校正集，对LS-SVM模型的泛化能力进行了评价。当用于校准的样品数量为80时，获得了100%的正确回答率。基于PLS-DA得到的系数和加载权重，筛选出敏感波长区域，并提出了五个敏感波长（412，421，469，681和717 nm）。使用这五个波长的LS-SVM识别模型基于40个样本的校准集获得了98%的正确回答率。结果表明，可见-近红外光谱技术是一种快速、有效的大白菜种子品种鉴别技术。
Visible and near-infrared reflectance spectroscopy was applied to identify varieties of Chinese cabbage seeds. Chemometrics was used to establish the identification models from a total of 120 samples, 20 samples from each of the six varieties. Soft independent modeling of a class analogy (SIMCA) models were established based on principal component analysis, and a good identification result of about 94% was achieved based on the calibration set of 40 samples. Partial least-squares discriminant analysis (PLS-DA) and least-squares support vector machine (LS-SVM) were used to further improve the correct answer rate. A correct answer rate higher than 97% was reached by LS-SVM based on the calibration set of 40 samples, better than that of PLS-DA (81%). The generalization ability of the LS-SVM model was evaluated based on calibration sets with different numbers of samples. A correct answer rate of 100% was obtained when the number of samples for the calibration was 80. Based on the resulting coefficients and loading weights from PLS-DA, sensitive wavelength regions were screened, and five sensitive wavelengths (412, 421, 469, 681, and 717 nm) were proposed. LS-SVM identification models using these five wavelengths obtained a 98% correct answer rate based on a calibration set of 40 samples. The result shows that visible and near-infrared reflectance spectroscopy is a fast and effective technique to identify the varieties of Chinese cabbage seeds.