Species identification, phylogenetic analysis and detection of herbicide-resistant biotypes of Amaranthus based on ALS and ITS

The complete plastome of Amaranthus roxburghianus (Amaranthaceae)

Amaranthus roxburghianus H.W. Kung 1935, belonging to the Amaranthaceae family, is recognized for its significant medicinal properties. However, molecular research on this species has been limited. This study represents the inaugural documentation of the sequencing and assembly of the complete plastome of A. roxburghianus. The genome spans a total length of 149,969 base pairs (bp), exhibiting a conventional quadripartite structure. This structure comprises a large single-copy (LSC) region of 83,917 bp, a small single-copy (SSC) region of 18,124 bp, and two inverted repeat (IR) regions, each extending to 23,964 bp. In its entirety, the A. roxburghianus plastome encompasses 128 genes, of which 107 are unique, encompassing 77 individual protein-coding genes, 26 unique tRNA genes, and four unique rRNA genes. Phylogenetic analysis has shown a close resemblance between A. roxburghianus and A. polygonoides, both part of the subgenus Albersia. Although the genus Amaranthus is roughly divided into three subgenera, additional plastid genomic data are required for a more accurate assignment of A. albus and A. blitoides. The sequencing of this plastome is a significant step forward, likely to expedite the development of molecular markers and significantly contribute to genetic assays involving this distinctive species.

Genetic variation and structure of complete chloroplast genome in alien monoecious and dioecious Amaranthus weeds

Amaranthus is a complex taxon with economic importance as well as harmful weeds. We studied the genetic variation and structure of the chloroplast genomes of 22 samples from 17 species of three subgenera. It was found that the length of the chloroplast genome of Amaranthus varied from 149,949 bp of A. polygonoides to 150,757 bp of A. albus. The frequencies of SNPs and InDels in chloroplast genomes were 1.79% and 2.86%, and the variation mainly occurred in the non-coding regions. The longest InDel was 387 bp, which occurred on ycf2, followed by 384 bp InDel on psbM-trnD. Two InDels in ndhE-I on the SSC make the three subgenera clearly distinguished. In LSC, SSC and IRs regions, there were four 30 bp forward and reverse repeats, and the repeats in SSC and LSC were in nearly opposite positions in circular genome structure, and almost divided the circular genome into symmetrical structures. In the topological tree constructed by chloroplast genome, species in subgen. Amaranthus and subgen. Acnida form monophyletic branches separately and cluster together. A. albus, A. blitoides and A. polygonoides were separated from subgen. Albersia, and the rest of subgen. Albersia were clustered into a monophyletic branch. The rpoC2, ycf1, ndhF-rpl32 were good at distinguishing most amaranths. The trnk-UUU-atpF, trnT-UGU-atpB, psbE-clpP, rpl14-rps19, and ndhF-D can distinguish several similar species. In general, the chloroplast genome is of certain value for the identification of the similar species of Amaranthus, which provides more evidence for clarifying the phylogenetic relationships within the genus.

Identification of Amaranthus Species Using Visible-Near-Infrared (Vis-NIR) Spectroscopy and Machine Learning Methods

The feasibility of rapid and non-destructive classification of six different Amaranthus species was investigated using visible-near-infrared (Vis-NIR) spectra coupled with chemometric approaches. The focus of this research would be to use a handheld spectrometer in the field to classify six Amaranthus sp. in different geographical regions of South Korea. Spectra were obtained from the adaxial side of the leaves at 1.5 nm intervals in the Vis-NIR spectral range between 400 and 1075 nm. The obtained spectra were assessed with four different preprocessing methods in order to detect the optimum preprocessing method with high classification accuracy. Preprocessed spectra of six Amaranthus sp. were used as input for the machine learning-based chemometric analysis. All the classification results were validated using cross-validation to produce robust estimates of classification accuracies. The different combinations of preprocessing and modeling were shown to have a classification accuracy of between 71% and 99.7% after the cross-validation. The combination of Savitzky-Golay preprocessing and Support vector machine showed a maximum mean classification accuracy of 99.7% for the discrimination of Amaranthus sp. Considering the high number of spectra involved in this study, the growth stage of the plants, varying measurement locations, and the scanning position of leaves on the plant are all important. We conclude that Vis-NIR spectroscopy, in combination with appropriate preprocessing and machine learning methods, may be used in the field to effectively classify Amaranthus sp. for the effective management of the weedy species and/or for monitoring their food applications.