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Population explosions, environmental deprivation, and industrial expansion led to an imbalanced agricultural system. Non-judicial uses of agrochemicals have decreased agrodiversity, degraded agroecosystems, and increased the cost of farming. In this scenario, a sustainable agriculture system could play a crucial role; however, it needs rigorous study to understand the biological interfaces within agroecosystems. Among the various biological components with respect to agriculture, mycorrhizae could be a potential candidate. Most agricultural crops are symbiotic with arbuscular mycorrhizal fungi (AMF). In this study, beetroot has been chose to study the effect of different AMFs on various parameters such as morphological traits, biochemical attributes, and gene expression analysis (ALDH7B4 and ALDH3I1). The AMF Gm-Funneliformis mosseae (Glomus mosseae), Acaulospora laevis, and GG-Gigaspora gigantean were taken as treatments to study the effect on the above-mentioned parameters in beetroot. We observed that among all the possible combinations of mycorrhizae, Gm+Al+GG performed best, and the Al-alone treatment was found to be a poor performer with respect to all the studied parameters. This study concluded that the more the combinations of mycorrhizae, the better the results will be. However, the phenomenon depends on the receptivity, infectivity, and past nutrient profile of the soil.

Identification of plant species is a crucial process in natural products. Ocimum, often referred to as the queen of herbs, is one of the most versatile and globally used medicinal herbs for various health benefits due to it having a wide variety of pharmacological activities. Despite there being significant global demand for this medicinal herb, rapid and comprehensive metabolomic fingerprinting approaches for species- and variety-specific classification are limited. In this study, metabolomic fingerprinting of five Ocimum species (Ocimum basilicum L., Ocimum sanctum L., Ocimum africanum Lour., Ocimum kilimandscharicum Gurke., and Hybrid Tulsi) and their varieties was performed using LC-MS, GC-MS, and the rapid fingerprinting approach FT-NIR combined with chemometrics. The aim was to distinguish the species- and variety-specific variation with a view toward developing a quality assessment of Ocimum species. Discrimination of species and varieties was achieved using principal component analysis (PCA), partial least squares discriminate analysis (PLS-DA), data-driven soft independent modelling of class analogy (DD-SIMCA), random forest, and K-nearest neighbours with specificity of 98% and sensitivity of 99%. Phenolics and flavonoids were found to be major contributing markers for species-specific variation. The present study established comprehensive metabolomic fingerprinting consisting of rapid screening and confirmatory approaches as a highly efficient means to identify the species and variety of Ocimum, being able to be applied for the quality assessment of other natural medicinal herbs.