Soybean seed vigor discrimination by infrared spectroscopy and machine learning algorithms

A novel approach to distinguish soybean seed vigor based on Fourier Transform infrared spectroscopy (FTIR) associated with chemometric methods are presented. Batches with high and low vigor soybean seeds were analyzed. The 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 chemometric methods as a new alternative approach to discriminate seed vigor.