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.
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This study was financed by Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq), grant numbers 312376/2017-0, 4008127/2018-0, and 437867/2018-8. The authors also thank Coordenação de Aperfeiçoamento de Pessoal de Nível Superior—Brasil (CAPES)—Finance Code 001.
Conflict of Interest
Gustavo S. Larios declares that he has no conflict of interest. Gustavo Nicolodelli declares that he has no conflict of interest.Giorgio S. Senesi declares that he has no conflict of interest. Matheus C.S. Ribeiro declares that he has no conflict of interest. Alfredo A.P. Xavier declares that he has no conflict of interest. Débora M.B.P. Milori declares that she has no conflict of interest. Charline Z. Alves declares that she has no conflict of interest. Bruno S. Marangoni declares that he has no conflict of interest. Cícero Cena declares that he has no conflict of interest.
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Larios, G.S., Nicolodelli, G., Senesi, G.S. et al. Laser-Induced Breakdown Spectroscopy as a Powerful Tool for Distinguishing High- and Low-Vigor Soybean Seed Lots. Food Anal. Methods (2020). https://doi.org/10.1007/s12161-020-01790-8
- LIBS analysis
- Soybean seeds
- Multivariate analysis