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Germination of aged seeds may be associated with specific metabolic changes. The objective of this study was to examine physiological and metabolic alterations before and after germination of control and aged oat (Avena sativa) seeds. The activity of antioxidant enzymes and the level of storage compounds were measured in the embryo and endosperm at 0, 4, 16, and 32 h of imbibition for control seeds and 0, 4, 16, 32, and 60 h of imbibition for medium vigor seeds after artificially accelerated aging; metabolomic changes were determined in embryos at 16 and 32 h of seed imbibition. In aged oat seeds, superoxide dismutase activity and catalase activity increased in the late imbibition stage. The content of soluble sugars decreased significantly in the later stages of imbibition, while the content of proteins increased in 32 h of seed imbibition eventually producing mannitol and proline. The mobilization of fat in deteriorated seeds was mainly through the sphingolipid metabolic pathway generated by cell growth-promoting dihydrosphingosine-1-phosphate. Ascorbic acid, avenanthramide and proline levels increased significantly at 60 h of imbibition, playing an important role in the germination of aged oat seeds., (© 2023 Scandinavian Plant Physiology Society.)

Seed vigor is an important index to evaluate seed quality in plant species. How to evaluate seed vigor quickly and accurately has always been a serious problem in the seed research field. As a new physical testing method, multispectral technology has many advantages such as high sensitivity and accuracy, nondestructive and rapid application having advantageous prospects in seed quality evaluation. In this study, the morphological and spectral information of 19 wavelengths (365, 405, 430, 450, 470, 490, 515, 540, 570, 590, 630, 645, 660, 690, 780, 850, 880, 940, 970 nm) of alfalfa seeds with different level of maturity and different harvest periods (years), representing different vigor levels and age of seed, were collected by using multispectral imaging. Five multivariate analysis methods including principal component analysis (PCA), linear discriminant analysis (LDA), support vector machine (SVM), random forest (RF) and normalized canonical discriminant analysis (nCDA) were used to distinguish and predict their vigor. The results showed that LDA model had the best effect, with an average accuracy of 92.9% for seed samples of different maturity and 97.8% for seed samples of different harvest years, and the average sensitivity, specificity and precision of LDA model could reach more than 90%. The average accuracy of nCDA in identifying dead seeds with no vigor reached 93.3%. In identifying the seeds with high vigor and predicting the germination percentage of alfalfa seeds, it could reach 95.7%. In summary, the use of Multispectral Imaging and multivariate analysis in this experiment can accurately evaluate and predict the seed vigor, seed viability and seed germination percentages of alfalfa, providing important technical methods and ideas for rapid non-destructive testing of seed quality.