Four pigment-producing rhizobial strains nodulating Sesbania cannabina (Retz.) Poir. formed a unique group in genus Microvirga in the phylogeny of a 16S rRNA gene and five housekeeping genes (gyrB, recA, dnaK, glnA, and atpD) in a genome analysis, phenotypic characteristics analysis, and chemotaxonomic analysis. These four strains shared as high as 99.3% similarity with Microvirga tunisiensis LmiM8^T in the 16S rRNA gene sequence and, in an MLSA, were subdivided into two clusters, ANI (genome average nucleotide) and dDDH (digital DNA–DNA hybridization) which shared sequence similarities lower than the species thresholds with each other and with the reference strains for related Microvirga species. The polar lipids elucidated that phosphatidylcholine (PC), phosphatidylethanolamine (PE), phosphatidylglycerol (PG), and cardiolipin were the main components for strain SWF67558^T and for strain HBU65207^T, with the exception of PC. SWF67558^T and HBU65207^T strains had similar predominant cellular fatty acids, including C16:0, C18:0, summed feature 2, and summed feature8, but with different contents. In addition, all the four novel strains produced pink-pigment, and the main coloring material extract from strain SWF67558^T was identified as zeaxanthin, which presented antioxidant ability and reduction power. With all the phylogenetic and phenotypic divergency, we proposed these pink-pigmented symbiotic bacteria as two novel species, named Microvirga sesbaniae sp. nov. and Microvirga yunnanensis sp. nov., with SWF67558^T (=KCTC82331^T=GDMCC1.2024^T) and HBU65207^T (=KCTC92125^T=GDMCC1.2023^T) as the type strains, respectively.

Soluble sugar is an important index to determine the quality of jujube, and also an important factor to influence the taste of jujube. The acquisition of the soluble sugar content of jujube mainly relies on manual chemical measurement which is time-consuming and labor-intensive. In this study, the feasibility of multispectral imaging combined with deep learning for rapid nondestructive testing of fruit internal quality was analyzed. Support vector machine regression model, partial least squares regression model, and convolutional neural networks (CNNs) model were established by multispectral imaging method to predict the soluble sugar content of the whole jujube fruit, and the optimal model was selected to predict the content of three kinds of soluble sugar. The study showed that the sucrose prediction model of the whole jujube had the best performance after CNNs training, and the correlation coefficient of verification set was 0.88, which proved the feasibility of using CNNs for prediction of the soluble sugar content of jujube fruits.