Due to a similar plant morphology in the majority of Zingiberaceae spices, substitution and adulteration frequently take place during the sales process. Therefore, it is important to analyze the metabolites and species classification of different Zingiberaceae spices. This study preliminarily explored the differences in the metabolites in thirteen Zingiberaceae spices through untargeted gas chromatography–mass spectrometry (GC–MS) and combined spectroscopy, establishing models for classifying different Zingiberaceae spices. On one hand, a total of 81 metabolites were successfully identified by GC–MS. Thirty-seven differential metabolites were screened using variable important in projection (VIP ≥ 1). However, the orthogonal partial least squares discriminant analysis (OPLS-DA) model established using GC–MS data only explained about 30% of the variation. On the other hand, the partial least squares discriminant analysis (PLS-DA) models with three spectral data fusion strategies were compared, and their classification accuracy reached 100%. Among them, the mid-level data fusion model based on latent variables had the best performance. This study provides a powerful tool for distinguishing different Zingiberaceae spices and assists in reducing the occurrence of substitution and adulteration phenomena.

Continuous monoculture of Panax notoginseng leads to severe replant disease, yet the mechanisms by which root exudates mediate rhizosphere microbiome assembly and pathogen enrichment remain poorly understood. Here, we demonstrate that long-term root exudate accumulation acts as an ecological filter, driving the fungal community toward a phylogenetically impoverished, pathogen-dominated state. Specifically, exudates enriched the soil-borne pathogen Fusarium while reducing the abundance of potentially antagonistic fungi. In contrast, bacterial communities exhibited higher resilience, with exudates selectively enriching oligotrophic taxa such as Terrimonas and MND1, but suppressing nitrifying bacteria (e.g., Nitrospira) and plant-growth-promoting rhizobacteria (PGPR). Microbial functional profiling revealed a shift in nitrogen cycling, characterized by suppressed nitrification and enhanced nitrate reduction. Crucially, co-occurrence network analysis identified bacterial taxa strongly negatively correlated with Fusarium, providing a synthetic community blueprint for biocontrol strategies. Our study establishes a mechanistic link between root exudate accumulation and negative plant–soil feedback in monoculture systems, highlighting microbiome reprogramming as a key driver of replant disease. These insights offer novel avenues for manipulating rhizosphere microbiomes to sustain crop productivity in intensive agricultural systems.