Genetic diversity, population structure, and genome‑wide association study for the flowering trait in a diverse panel of 428 moth bean (Vigna aconitifolia) accessions using genotyping by sequencing

Background Moth bean (Vigna aconitifolia) is an underutilized, protein-rich legume that is grown in arid and semiarid areas of south Asia and is highly resistant to abiotic stresses such as heat and drought. Despite its economic importance, the crop remains unexplored at the genomic level for genetic diversity and trait mapping studies. To date, there is no report of SNP marker discovery and association mapping of any trait in this crop. Therefore, this study aimed to dissect the genetic diversity, population structure and marker-trait association for the flowering trait in a diversity panel of 428 moth bean accessions using genotyping by sequencing (GBS) approach. Results A total of 9078 high-quality single nucleotide polymorphisms (SNPs) were discovered by genotyping of 428 moth bean accessions. Model-based structure analysis and PCA grouped the moth bean accessions into two subpopulations. Cluster analysis revealed accessions belonging to the Northwestern region of India had higher variability than accessions from the other regions suggesting that this region represents its center of diversity. AMOVA revealed more variations within individuals (74%) and among the individuals (24%) than among the populations (2%). Marker-trait association analysis using seven multi-locus models including mrMLM, FASTmrEMMA FASTmrEMMA, ISIS EM-BLASSO, MLMM, BLINK and FarmCPU revealed 29 potential genomic regions for the trait days to 50% flowering, which were consistently detected in three or more models. Analysis of the allelic effect of the major genomic regions explaining phenotypic variance of more than 10% and those detected in at least 2 environments showed 4 genomic regions with significant phenotypic effect on this trait. Further, we also analyzed genetic relationships among the Vigna species using SNP markers. The genomic localization of moth bean SNPs on genomes of closely related Vigna species demonstrated that maximum numbers of SNPs were getting localized on Vigna mungo. This suggested that the moth bean is most closely related to V. mungo. Conclusion Our study shows that the north-western regions of India represent the center of diversity of the moth bean. Further, the study revealed flowering-related genomic regions/candidate genes which can be potentially exploited in breeding programs to develop early-maturity moth bean varieties

Statistical Feature Extraction and Recognition of Beverages Using Electronic Tongue

This paper describes an approach for extraction of features from data generated from an electronic tongue based on large amplitude pulse voltammetry. In this approach statistical features of the meaningful selected variables from current response signals are extracted and used for recognition of beverage samples. The proposed feature extraction approach not only reduces the computational complexity but also reduces the computation time and requirement of storage of data for the development of E-tongue for field applications. With the reduced information, a probabilistic neural network (PNN) was trained for qualitative analysis of different beverages. Before the qualitative analysis of the beverages, the methodology has been tested for the basic artificial taste solutions i.e. sweet, sour, salt, bitter, and umami. The proposed procedure was compared with the more conventional and linear feature extraction technique employing principal component analysis combined with PNN. Using the extracted feature vectors, highly correct classification by PNN was achieved for eight types of juices and six types of soft drinks. The results indicated that the electronic tongue based on large amplitude pulse voltammetry with reduced feature was capable of discriminating not only basic artificial taste solutions but also the various sorts of the same type of natural beverages (fruit juices, vegetable juices, soft drinks, etc.)

Non-Destructive Classification of Assam Black Tea Using Ultra-fast Gas Chromatography (UFGC) Coupled with Soft Independent Modeling of Class Analogy (SIMCA)

This paper presents the non destructive and qualitative discrimination, classification and identification of Assam black tea (Camellia Sinesis (L.) O. Kuntze) with their aroma compounds using a new and ultra fast gas chromatograph (UFGC) analyzer. Eight different varieties of Assam black tea were discriminated by using their aroma profile by recording the frequency spectra of surface acoustic wave (SAW) sensor. The result demonstrates the power of gas chromatography to discriminate the seasonal variety tea samples of same origin. Principal Component Analysis is used to visualize the data variation. Standard normal variate (SNV) transformation is applied as a preprocessing method, to remove the slope variation of the data. The multivariate data analysis method, Soft Independent Modeling of Class Analogy (SIMCA) is used to construct the classification models, which works with the individual PCA model for each group of tea samples. SIMCA provides the samples belong to the same class or more than one classes. The classification Accuracy with SIMCA model is 100 % except for the samples E and F, which shows some overlapping in Cooman’s plot. The overall result demonstrates that, frequency spectra by zNose, incorporated with suitable pattern recognition methods can be applied as a rapid and efficient method to identify the Assam black tea varieties of same origin