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Mentha haplocalyx (Lamiaceae) is a herbaceous perennial economic plant which is widely cultivated in China. Plants of M. haplocalyx with mosaic symptoms were collected from Zhaotong, Yunnan Province. Alfalfa mosaic virus (AMV) was detected from symptomatic leaf samples using small RNA sequencing and RT-PCR. The complete genome sequence of AMV-Mint was determined. AMV-Mint RNA1 was 3,644 nt, RNA2 was 2,594 nt and RNA3 was 2,040 nt, encoding P1 of 1,126 amino acids (aa), P2 of 794 aa, MP of 300 aa and CP of 218 aa. The genome structure of AMV-Mint was similar to those reported previously, except for an insertion of one adenine nucleotide at 2,389 nt of the RNA2, which results in reading frame shift mutation of the P2. Phylogenetic analysis based on RNA3 sequences grouped AMV-Mint into the Group I clade, with closest relationship to AMV-Lst. No recombination event was detected in the genome of AMV-Mint. This is the first report of the complete genome sequence of AMV from M. haplocalyx. [ABSTRACT FROM AUTHOR]

In this study, complex substances such as Mint (Mentha haplocalyx Briq.) samples from different growing regions in China were analyzed for phenolic compounds by high-performance liquid chromatography with diode array detection and for the volatile aroma compounds by gas chromatography with mass spectrometry. Chemometrics methods, e.g. principal component analysis, back-propagation artificial neural networks, and partial least squares discriminant analysis, were applied to resolve complex chromatographic profiles of Mint samples. A total of 49 aroma components and 23 phenolic compounds were identified in 79 Mint samples. Principal component analysis score plots from gas chromatography with mass spectrometry and high-performance liquid chromatography with diode array detection data sets showed a clear distinction among Mint from three different regions in China. Classification results showed that satisfactory performance of prediction ability for back-propagation artificial neural networks and partial least squares discriminant analysis. The major compounds that contributed to the discrimination were chlorogenic acid, unknown 3, kaempherol 7- O-rutinoside, salvianolic acid L, hesperidin, diosmetin, unknown 6 and pebrellin in Mint according to regression coefficients of the partial least squares discriminant analysis model. This study indicated that the proposed strategy could provide a simple and rapid technique to distinguish clearly complex profiles from samples such as Mint. [ABSTRACT FROM AUTHOR]