Although endogenous hormones play an important role in flower bud differentiation and seed-filling, their effects on the flowering and fruiting of Glycyrrhiza uralensis Fisch. remain unknown. In the present study, we investigate the differences in the levels of endogenous hormones gibberellic acid (GA), abscisic acid (ABA), zeatin riboside (ZR), and indoleacetic acid (IAA) between the fruiting and seedless plants of G. uralensis Fisch. at different growth stages. We also determine the correlations of the endogenous hormone with the rates of flower and fruit falling, rate of empty seeds, rate of shrunken grains, and thousand kernel weight (TKW). The results demonstrate that the IAA and ZR levels of the flowering plants are significantly higher than those of the nonflowering plants at the flower bud differentiation stage. The GA and ABA levels of exfoliated inflorescence plants are considerably higher than those of the flowering and fruiting plants; the rates of falling flowers and fruit are negatively correlated with the IAA level and positively correlated with the ABA level. The ABA content of nonflowering plants is significantly higher than that of fruiting plants. The ZR:GA and IAA:ABA ratios are significantly positively correlated with TKW. The IAA:GA and IAA:ABA ratios are significantly negatively correlated with the rates of empty and shrunken seeds. Thus, we speculate that high IAA and ZR contents are good for flower bud differentiation and seed-filling, and low ABA and ZR contents are beneficial to flower bud development and seed-filling.

Wild licorice in China is mainly distributed in northern China, such as Gansu, Ningxia, and Inner Mongolia Provinces. The origin of wild licorice has varied among historical periods. The cultivated origin of planted licorice has the same as 59.26% of wild licorice. The distribution of cultivated licorice was shifted to the northwest relative to that of wild licorice. The yield and quality of cultivated licorice vary greatly from different origins, showing a certain pattern of variation from west to east. The same batch of licorice seedlings was planted at 8 sites overlapping the main licorice production areas in China. The yield and quality of licorice in the Baicheng experimental plot were low. The yield of licorice in the Jingtai and Altay experimental plots was high, but the quality was poor. The quality of licorice in Chifeng and Yuzhong experimental sites was high, but the yield was low. Principal component analysis of environmental and soil factors generated five characteristic roots with a cumulative contribution rate of 80%, three of which were related to soil and referred to as the soil charge factor, soil water factor, and soil nutrient factor, and the load coefficients of the water and nutrient factor were the largest. Soil conditions, especially water and nutrients, might have a substantial effect on the observed changes in the licorice production area. Generally, the regulation of water and nutrients merits special attention when selecting areas for the production and cultivation of licorice. This study can provide reference for the selection of cultivated licorice production areas and the research of high-quality cultivation techniques.

The formation of high-quality Chinese medicinal materials is a micro-evolutionary process of multiple genes involving quantitative inheritance under environmental stress. Atractylodes lancea is a traditionally used medicinal plant in China that is broadly distributed and possesses a considerable amount of essential oils. However, to date, limited research has been conducted to characterize the genetics and metabolites of A. lancea shaped by natural variation. Hence, we assembled a high-quality genome of A. lancea, featuring a contig N50 of 1.18 Mb. We further integrated population resequencing of A. lancea and conducted analyses to characterize its genetic diversity, population evolution, and rewiring of volatile metabolites. The natural variation effect exerted significant pressure on A. lancea from different geographic locations, resulting in genetic differentiation among three groups. Correlation analysis of metabolites in A. lancea revealed significant natural variations of terpenoids, heterocyclic compounds, ketones, and esters. We also found that 427 metabolites displayed noteworthy divergence due to directional selection. Additionally, our genome-wide association studies on the metabolome for medicinal quality traits identified several candidate genes, such as AlZFP706 and AlAAHY1, exhibiting significant correlations with atractylodin and hinesol levels, respectively. Overall, this study provides an intricate genomic resource for A. lancea, thereby expanding our understanding of the effect of natural variation on metabolites and facilitating the genetic improvement of its medicinal properties.

Hyperspectral imaging (HSI) technology was combined with chemometrics to achieve rapid determination of tanshinone contents in Salvia miltiorrhiza, as well as the rapid identification of its origins. Derivative (D1), second derivative (D2), Savitzky–Golay filtering (SG), multiplicative scatter correction (MSC), and standard normal variate transformation (SNV) were utilized to preprocess original spectrum (ORI). Partial least squares discriminant analysis (PLS-DA) and support vector machine (SVM) models were employed to discriminate 420 Salvia miltiorrhiza samples collected from Shandong, Hebei, Shanxi, Sichuan, and Anhui Provinces. The contents of tanshinone IIA, tanshinone I, cryptotanshinone, and total tanshinones in Salvia miltiorrhiza were predicted by the back-propagation neural network (BPNN), partial least square regression (PLSR), and random forest (RF). Finally, effective wavelengths were selected using the successive projections algorithm (SPA) and variable iterative space shrinkage approach (VISSA). The results indicated that the D1-PLS-DA model performed the best with a classification accuracy of 98.97%. SG-BPNN achieved the best prediction effect for cryptotanshinone (RMSEP = 0.527, RPD = 3.25), ORI-BPNN achieved the best prediction effect for tanshinone IIA (RMSEP = 0.332, RPD = 3.34), MSC-PLSR achieved the best prediction effect for tanshinone I (RMSEP = 0.110, RPD = 4.03), and SNV-BPNN achieved the best prediction effect for total tanshinones (RMSEP = 0.759, RPD = 4.01). When using the SPA and VISSA, the number of wavelengths was reduced below 60 and 150, respectively, and the performance of the models was all very good (RPD > 3). Therefore, the combination of HSI with chemometrics provides a promising method for predicting the active ingredients of Salvia miltiorrhiza and identifying its geographical origins.

The lily, valued for its edibility and medicinal properties, is rich in essential nutrients. However, storage conditions and sulfur fumigation during processing can degrade key nutrients like polysaccharides, phenols, and sulfur dioxide. To address this, we applied a deep learning model combined with hyperspectral imaging for the rapid prediction of nutrient quality. The CLSTM (convolutional neural network–long short-term memory) model, utilizing variable combination population analysis (VCPA) for wavelength selection, effectively differentiated sulfur fumigation patterns in lilies. In terms of nutrient content prediction, the CLSTM model combined with full-wavelength data demonstrated superior performance, achieving an R² value of 0.769 for polysaccharides and 0.699 for total phenols. Additionally, the CLSTM model combined with IRF-selected characteristic wavelengths exhibited remarkable performance in predicting sulfur dioxide content, with an R² value of 0.755. These findings highlight the potential of hyperspectral imaging and the CLSTM model in enhancing the quality assessment and ensuring the nutritional integrity of lily products.

Arisaema erubescens is a well-known medicinal plant in China. In this study, we sequenced the complete chloroplast (cp) genome sequence of A. erubescens to investigate its phylogenetic relationship in the family Araceae. The cp genome was 167,607 bp in length, consisting of a pair of inverted repeats (IRa and IRb: 26,193 bp) separated by a large single-copy region (LSC: 93,660 bp) and a small single-copy region (SSC: 21,561 bp). The GC content of whole cp genome is 35.3%. De novo assembly and annotation showed the presence of 114 unique genes with 80 protein-coding genes, 30 tRNA genes, and four rRNA genes. Phylogenetic analysis indicated that A. erubescens was closely related to A. franchetianum, and the genus Arisaema was sister to the genus Pinellia.