A novel approach to distinguish soybean seed vigor based on Fourier transform infrared spectroscopy (FTIR) associated with chemometric methods is presented. Batches with high and low vigor soybean seeds were analyzed. Support vector machine (SVM), K-nearest neighbors (KNN), and discriminant analysis were applied to the raw spectral and reduced-dimensionality data from PCA (principal component analysis). Proteins, fatty acids, and amides were identified as the main molecules responsible for the discrimination of the batches. The cross-validation tests pointed out that high vigor soybean seeds were successfully discriminated from low vigor ones with an accuracy of 100%. These findings indicate FTIR spectroscopy associated with multivariate analysis as a new alternative approach to discriminate seed vigor.

中文翻译：

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利用红外光谱和机器学习算法对大豆种子活力进行判别。

提出了一种基于傅里叶变换红外光谱（FTIR）结合化学计量学方法来区分大豆种子活力的新方法。分析了具有高和低活力大豆种子的批次。支持向量机（SVM），K近邻（KNN）和判别分析应用于来自PCA的原始光谱和降维数据（主成分分析）。蛋白质，脂肪酸和酰胺被确定为负责区分批次的主要分子。交叉验证测试指出，成功鉴别出高活力大豆种子和低活力大豆种子的准确度为100％。这些发现表明，与多变量分析相关的FTIR光谱学是判别种子活力的一种新的替代方法。