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Background Mei (Prunus mume) is the only woody plant in the genus Prunus with a floral fragrance, but the underlying mechanisms of aroma compound biosynthesis are unclear despite being a matter of considerable interest. Results The volatile contents of the petals of two cultivars with significantly different aromas, Prunus mume ‘Xiao Lve’ and Prunus mume ‘Xiangxue Gongfen’, were characterised by GC-MS at different flowering periods, and a total of 44 volatile compounds were detected. Among these, the main substances forming the typical aroma of P. mume were identified as eugenol, cinnamyl acetate, hexyl acetate and benzyl acetate, with variations in their relative concentrations leading to sensory differences in the aroma of the two cultivars. We compiled a transcriptome database at key stages of floral fragrance formation in the two cultivars and used it in combination with differential analysis of floral volatiles to construct a regulatory network for the biosynthesis of key aroma compounds. The results indicated that PmPAL enzymes and PmMYB4 transcription factors play important roles in regulating the accumulation of key biosynthetic precursors to these compounds. Cytochrome P450s and short-chain dehydrogenases/reductases might also influence the biosynthesis of benzyl acetate by regulating production of key precursors such as benzaldehyde and benzyl alcohol. Furthermore, by analogy to genes with verified functions in Arabidopsis, we predicted that three PmCAD genes, two 4CL genes, three CCR genes and two IGS genes all make important contributions to the synthesis of cinnamyl acetate and eugenol in P. mume. This analysis also suggested that the downstream genes PmBGLU18-like, PmUGT71A16 and PmUGT73C6 participate in regulation of the matrix-bound and volatile states of P. mume aroma compounds. Conclusions These findings present potential new anchor points for further exploration of floral aroma compound biosynthesis pathways in P. mume, and provide new insights into aroma induction and regulation mechanisms in woody plants.

A new test method for stability of digital camera system based on camera station optimization was presented. To enhance the geometric network of camera station for the camera system structural instability compensated, and coordinate measurement repeatability was verified and analyzed with experiment. Experimental results indicated that the 9-point network testing method used based on camera station optimization could meet the requirements for stability testing of a digital camera system, and its coordinate measurement repeatability was higher.