laser-induced breakdown spectroscopy as a powerful tool for distinguishing high- and low-vigor soybean seed lots

The tests commonly used to determine seed vigor are often laborious and time-consuming; thus, rapid methods are highly required for identifying high-vigor seeds among different batches. in this paper, we describe a novel approach able to distinguishing among batches of soybean seeds of different physiological quality based on their nutrient content measured by laser-induced breakdown spectroscopy (libs) assisted by multivariate analysis and machine learning algorithms. these include principal component analysis (pca), support vector machine learning (svm), linear and quadratic discriminant analyses (lda and qda), and nearest neighbor methods (knn). a total of 92 measurements, 46 collected from batches marketed as low-vigor seeds and 46 as high-vigor seeds, were analyzed. the svm method performed the best in discriminating among the batches. in particular, the quadratic svm function could classify correctly 100% of the high-vigor samples and 97.8% of the low-vigor samples, whereas the cubic function yielded the opposite result; i.e., 97.8% of the high-vigor samples and 100% of the low-vigor samples were classified correctly. the best libs spectral region for the analysis was in the range of 350–450 nm, with calcium being the main distinguishing element. thus, the libs technique combined with machine learning classification methods showed a promising potential for classifying soybean seed batches according to their physiological quality.