Near-infrared spectral evaluation of physiological potential, biochemical composition and enzymatic activity of soybean seeds

Abstract

Seed quality is routinely evaluated in the laboratory through germination and vigor tests. Although efficient, the available test methods have limitations, which are mainly associated with long evaluation times. We aimed to verify whether near-infrared (NIR) spectroscopy can categorize soybean seed lots and genotypes into predefined vigor classes based on physiological and biochemical analyses. The classes were defined based on analyses of physiological potential; the antioxidant enzymes activities; and the contents of malonaldehyde, oil and protein. The NIR spectra of individual seeds were obtained, preprocessed, and used for modeling. Classification models using the K-Nearest Neighbors (K-NN) method, Partial Least Squares Discriminant Analysis (PLS-DA), and Support Vector Machine (SVM) were obtained. The low-vigor seeds had higher malonaldehyde and oil contents and, in general, lower antioxidative enzyme activities. The best model to classify the seed quality reached 99% accuracy. The wave-length region from 1,000 to 1,250 nm was the most important for distinguishing the levels of soybean seed quality.