Effective identification of varieties by nucleotide polymorphisms and its application for essentially derived variety identification in rice

Background Plant variety identification is the one most important of agricultural systems. Development of DNA marker profiles of released varieties to compare with candidate variety or future variety is required. However, strictly speaking, scientists did not use most existing variety identification techniques for “identification” but for “distinction of a limited number of cultivars,” of which generalization ability always not be well estimated. Because many varieties have similar genetic backgrounds, even some essentially derived varieties (EDVs) are involved, which brings difficulties for identification and breeding progress. A fast, accurate variety identification method, which also has good performance on EDV determination, needs to be developed. Results In this study, with the strategy of “Divide and Conquer,” a variety identification method Conditional Random Selection (CRS) method based on SNP of the whole genome of 3024 rice varieties was developed and be applied in essentially derived variety (EDV) identification of rice. CRS is a fast, efficient, and automated variety identification method. Meanwhile, in practical, with the optimal threshold of identity score searched in this study, the set of SNP (including 390 SNPs) showed optimal performance on EDV and non-EDV identification in two independent testing datasets. Conclusion This approach first selected a minimal set of SNPs to discriminate non-EDVs in the 3000 Rice Genome Project, then united several simplified SNP sets to improve its generalization ability for EDV and non-EDV identification in testing datasets. The results suggested that the CRS method outperformed traditional feature selection methods. Furthermore, it provides a new way to screen out core SNP loci from the whole genome for DNA fingerprinting of crop varieties and be useful for crop breeding.

Combination of Morphology, Wavelet and Convex Hull Features in Classification of Patchouli Varieties with Imbalance Data using Artificial Neural Network

Patchouli plants are main raw materials for essential oils in Indonesia. Patchouli leaves have a very varied physical form based on the area planted, making it difficult to recognize the variety. This condition makes it difficult for farmers to recognize these varieties and they need experts’ advice. As there are few experts in this field, a technology for identifying the types of patchouli varieties is required. In this study, the identification model is constructed using a combination of leaf morphological features, texture features extracted with Wavelet and shape features extracted with convex hull. The results of feature extraction are used as input data for training of classification algorithms. The effectiveness of the input features is tested using three classification methods in class artificial neural network algorithms: (1) feedforward neural networks with backpropagation algorithm for training, (2) learning vector quantization (LVQ), (3) extreme learning machine (ELM). Synthetic minority over-sampling technique (SMOTE) is applied to solve the problem of class imbalance in the patchouli variety dataset. The results of the patchouli variety identification system by combining these three features indicate the level of recognition with an average accuracy of 72.61%, accuracy with the combination of these three features is higher when compared to using only morphological features (58.68%) or using only Wavelet features (59.03 %) or both (67.25%). In this study also showed that the use of SMOTE in imbalance data increases the accuracy with the highest average accuracy of 88.56%.

Qualitative Analysis and Componential Differences of Chemical Constituents in Taxilli Herba from Different Hosts by UFLC-Triple TOF-MS/MS

Taxilli Herba (TH) is a well-known traditional Chinese medicine (TCM) with a wide range of clinical application. However, there is a lack of comprehensive research on its chemical composition in recent years. At the same time, Taxillus chinensis (DC) Danser is a semi parasitic plant with abundant hosts, and its chemical constituents varies due to hosts. In this study, the characterization of chemical constituents in TH was analyzed by ultra-fast liquid chromatography coupled with triple quadrupole-time of flight tandem mass spectrometry (UFLC-Triple TOF-MS/MS). Moreover, partial least squares discriminant analysis (PLS-DA) was applied to reveal the differential constituents in TH from different hosts based on the qualitative information of the chemical constituents. Results showed that 73 constituents in TH were identified or tentatively presumed, including flavonoids, phenolic acids and glycosides, and others; meanwhile, the fragmentation pathways of different types of compounds were preliminarily deduced by the fragmentation behavior of the major constituents. In addition, 23 differential characteristic constituents were screened based on variable importance in projection (VIP) and p-value. Among them, quercetin 3-O-β-D-glucuronide, quercitrin and hyperoside were common differential constituents. Our research will contribute to comprehensive evaluation and intrinsic quality control of TH, and provide a scientific basis for the variety identification of medicinal materials from different hosts.

Paddy seed variety identification using T20-HOG and Haralick textural features

The seed is an inevitable element for agricultural and industrial production. The non-destructive paddy seed variety identification is essential to assure paddy purity and quality. This research is aimed at developing a computer vision-based system to identify paddy varieties using multiple heterogeneous features, exploiting textural, external, and physical properties. We captured the paddy seed images without any fixed setup to make the system user friendly at both industry and farmer levels, which can lead to illumination problems in the images. To overcome this problem, we introduced a modified histogram oriented gradient (T20-HOG) feature that can describe the illumination, scale, and rotational variations of a paddy image. We also utilized the existing Haralick and traditional features and the dimensionality of the features is reduced by the Lasso feature selection technique. The selected features are used to train the feed-forward neural network (FNN) to predict the paddy variety. The experiments conducted on two different datasets: BDRICE, and VNRICE. Results of our method are shown in terms of four standard evaluation metrics, namely, accuracy, precision, recall, and F_1 score, and achieved 99.28%, 98.64%, 98.48%, and 98.56% score, respectively. We also compared our system efficiency with existing studies. The experimental results demonstrate that our proposed features are effective to identify paddy variety and achieved a new state-of-the-art performance. And we also observed that our newly proposed T20-HOG features have a major impact on overall system performance.

Variety Identification of Chinese Walnuts Using Hyperspectral Imaging Combined with Chemometrics

Chinese walnuts have extraordinary nutritional and organoleptic qualities, and counterfeit Chinese walnut products are pervasive in the market. The aim of this study was to investigate the feasibility of hyperspectral imaging (HSI) technique to accurately identify and visualize Chinese walnut varieties. Hyperspectral images of 400 Chinese walnuts including 200 samples of Ningguo variety and 200 samples of Lin’an variety were acquired in range of 400–1000 nm. Spectra were extracted from representative regions of interest (ROIs), and principal component analysis (PCA) of spectra showed that the characteristic second principal component (PC2) was potentially effective in variety identification. The PC transformation was also conducted to hyperspectral images to make an exploratory visualization according to pixel-wise PC scores. Three different modeling methods including partial least squares-discriminant analysis (PLS-DA), k-nearest neighbor (KNN), and support vector machine (SVM) were individually employed to develop classification models. Results indicated that raw full spectra constructed PLS-DA model performed best with correct classification rates (CCRs) of 97.33%, 95.33%, and 92.00% in calibration, cross-validation, and prediction sets, respectively. Successful projects algorithm (SPA), competitive adaptive reweighted sampling (CARS), and PC loadings were individually used for effective wavelengths selection. Subsequently, simplified PLS-DA model based on wavelengths selected by CARS yielded the best 96.33%, 95.67% and 91.00% CCRs in the three sets. This optimal CARS-PLS-DA model acquired a sensitivity of 93.62%, a specificity of 88.68%, the area under the receiver operating characteristic curve (AUC) value of 0.91, and Kappa coefficient of 0.82 in prediction set. Classification maps were finally generated by classifying the varieties of each pixel in multispectral images at CARS-selected wavelengths, and the general variety was then readily discernible. These results demonstrated that features extracted from HSI had outstanding ability, and could be applied as a reliable tool for the further development of an on-line identification system for Chinese walnut variety.

The grapevine (Vitis vinifera L.) floral transcriptome in Pinot noir variety: identification of tissue-related gene networks and whorl-specific markers in pre- and post-anthesis phases

The comprehension of molecular processes underlying the development and progression of flowering in plants is a hot topic, not only because that often the products of interest for human and animal nutrition are linked to the development of fruits or seeds, but also because the processes of gametes formation occurring in sexual organs are at the basis of recombination and genetic variability which constitutes the matter on which evolution acts, whether understood as natural or human driven. In the present study, we used an NGS approach to produce a grapevine flower transcriptome snapshot in different whorls and tissues including calyx, calyptra, filament, anther, stigma, ovary, and embryo in both pre- and post-anthesis phases. Our investigation aimed at identifying hub genes that unequivocally distinguish the different tissues providing insights into the molecular mechanisms that are at the basis of floral whorls and tissue development. To this end we have used different analytical approaches, some now consolidated in transcriptomic studies on plants, such as pairwise comparison and weighted-gene coexpression network analysis, others used mainly in studies on animals or human’s genomics, such as the tau (τ) analysis aimed at isolating highly and absolutely tissue-specific genes. The intersection of data obtained by these analyses allowed us to gradually narrow the field, providing evidence about the molecular mechanisms occurring in those whorls directly involved in reproductive processes, such as anther and stigma, and giving insights into the role of other whorls not directly related to reproduction, such as calyptra and calyx. We believe this work could represent an important genomic resource for functional analyses of grapevine floral organ growth and fruit development shading light on molecular networks underlying grapevine reproductive organ determination.

Untargeted Metabolomics of Rind Essential Oils Allowed to Differentiate Two Closely Related Clementine Varieties

Chemical characterization of clementine varieties (Citrus clementina Hort. ex Tan.) essential oils (EO) can lead to variety identification and valorization of their potential use in food and aroma industries. The goal of this study was the chemometric discrimination between two very closely related and morphologically identical clementine varieties, Clemenules (NL) and Clemenpons (PO), based on their rind EO, to identify the differential volatile organic compounds (VOCs) and to determine their antioxidant capacity. EO rind volatile profile was determined by gas chromatography coupled to mass spectrometry in Citrus fruit at different ripening stages grown two independent years in two different locations. Untargeted metabolomics and multivariate data analysis showed an evolution of EO volatile profiles markedly parallel in both varieties. Although EO qualitative composition was identical in both varieties, PLS-DA allowed the identification of characteristic VOCs, quantitatively discriminating them along all the ripening process. PO showed higher accumulation of several mono- and sesquiterpene compounds such as trans-carveol, while NL showed higher levels of aldehyde and alcohol non-terpenoids like dodecanal. Both varieties evinced identical EO antioxidant activities, indicating a similar value for food preservation. Hence, untargeted metabolomics approach based on rind EO volatiles was revealed as a powerful technique able to differentiate between morphologically undistinguishable Citrus varieties.

Assessment of genetic diversity and variety identification based on developed retrotransposon-based insertion polymorphism (RBIP) markers in sweet potato (Ipomoea batatas (L.) Lam.)

Sweet potato, a dicotyledonous and perennial plant, is the third tuber/root crop species behind potato and cassava in terms of production. Long terminal repeat (LTR) retrotransposons are highly abundant in sweet potato, contributing to genetic diversity. Retrotransposon-based insertion polymorphism (RBIP) is a high-throughput marker system to study the genetic diversity of plant species. To date, there have been no transposon marker-based genetic diversity analyses of sweet potato. Here, we reported a structure-based analysis of the sweet potato genome, a total of 21555 LTR retrotransposons, which belonged to the main LTR-retrotransposon subfamilies Ty3-gypsy and Ty1-copia were identified. After searching and selecting using Hidden Markov Models (HMMs), 1616 LTR retrotransposon sequences containing at least two models were screened. A total of 48 RBIP primers were synthesized based on the high copy numbers of conserved LTR sequences. Fifty-six amplicons with an average polymorphism of 91.07% were generated in 105 sweet potato germplasm resources based on RBIP markers. A Unweighted Pair Group Method with Arithmatic Mean (UPGMA) dendrogram, a model-based genetic structure and principal component analysis divided the sweet potato germplasms into 3 groups containing 8, 53, and 44 germplasms. All the three analyses produced significant groupwise consensus. However, almost all the germplasms contained only one primary locus. The analysis of molecular variance (AMOVA) among the groups indicated higher intergroup genetic variation (53%) than intrapopulation genetic variation. In addition, long-term self-retention may cause some germplasm resources to exhibit variable segregation. These results suggest that these sweet potato germplasms are not well evolutionarily diversified, although geographic speciation could have occurred at a limited level. This study highlights the utility of RBIP markers for determining the intraspecies variability of sweet potato and have the potential to be used as core primer pairs for variety identification, genetic diversity assessment and linkage map construction. The results could provide a good theoretical reference and guidance for germplasm research and breeding.

Genetic Diversity and Variety Identification of Panax Species Based on EST-SSR Markers

Background: The roots of Panax species are widely used in the East because of their high medicinal and economic value. They are similar in plant morphology and chemical composition, but have quite differences in medicinal properties and efficacy, therefore, genetic diversity and variety identification of Panax species is particularly important. Methods: We screened 7 Simple Sequence Repeat (SSR) markers from expressed sequence tags (ESTs) database of Panax species in NCBI. Using these markers test SSR polymorphism in Panax species. Results: Seven SSR markers could successfully identify Panax ginseng, Panax quinquefolium, Panax notoginseng, and their commercial products. Among three ginseng varieties, garden ginseng, forest ginseng, and wild ginseng, the polymorphism of EST-SSR markers decreased gradually, which may be related to age and environment. Two pairs of EST-SSR primers can specifically identify three ginseng cultivars. The phylogenetic relationships analysis showed that Panax ginseng and Panax quinquefolium were closer than Panax notoginseng. Compared with wild ginseng, the relationship between the garden ginseng and the forest ginseng was closer. Conclusion: SSR molecular markers have high repeatability and can be used as reliable molecular markers for genetic diversity and variety identification of Panax species.