Principal Component Analysis Biplot Research Articles

A remote lab exercise to introduce students to the fundamentals of bioacoustics using smartphones and R

Animals communicate through a variety of modalities including visual, auditory and chemical. Acoustic signals are particularly well suited for studying the evolution of animal communication due to the relative ease in which sounds can be recorded and analyzed. Therefore, a thorough overview of acoustic communication is a fundamental aspect of any animal behavior course. The purpose of this lab exercise is to introduce students to bioacoustics, or the study of animal sounds and their habitats. For the field component, students use their smartphones to collect focal recordings of target animals as well as collect acoustic data that will be used to investigate variation in soundscapes at different times (e.g. dawn and dusk) and/or different locations (e.g., urban versus rural). The computer lab component utilizes the R programming environment to import sound files and visualize differences in acoustic data using both spectrograms and principal component analysis biplots. The computer lab exercises are meant for undergraduate students with relatively little coding background but could be easily adapted for students of different skill levels. All materials needed to use and modify the course materials are openly available on GitHub, which should remove any financial barriers for students or instructors.

Genetic Variability and Association Analysis of Lisianthus (Eustoma grandiflorum (Raf) Shinn) Advanced Lines Under Mid Hill Conditions of Himachal Pradesh, India

The present investigation was designed to assess the extent of variability, genetic advance, heritability and interrelation of different traits of 19 lisianthus genotypes at ICAR-IARI Regional Station, Katrain, Kullu, HP during 2019–2021. The mean performance of different genotypes exhibited considerable range together with large value for most of the characters. The trend of variability at genotypic level was similar to that of at phenotypic for some of the characters. The path analysis clearly indicated that total number of flowers per plant was directly associated with plant height and number of shoots per plant. It is imperative that these traits should be prioritized while improving number of flowers per plant in lisianthus. The cluster analysis revealed existence of diversity among the evaluated genotypes. The first principal component analysis (PCA) score explained 33.798% of the total variation mainly associated to genotype and flower yield. The PCA biplot was effective in showing the genetic distance among the genotypes and their discrimination based on key traits of importance in lisianthus. Genotypes Ktlis-1, Ktlis-17, Ktlis-5, Ktlis-9 and Ktlis-7 were superior among the tested genotypes therefore could be exploited in lisianthus breeding to improve flower yield. Hence, the characters showing high heritability along with high genetic gain should be given due attention in the development of desirable genotypes through simple selection. Further, Genotypes from different clusters identified for specific characters may be used as parent for lisianthus breeding programme with an objective to improve the specific traits.

Integrative artificial intelligence models for Australian coastal sediment lead prediction: An investigation of in-situ measurements and meteorological parameters effects

Heavy metals (HMs) such as Lead (Pb) have played a vital role in increasing the sediments of the Australian bay's ecosystem. Several meteorological parameters (i.e., minimum, maximum and average temperature (Tmin, Tmax and TavgoC), rainfall (Rn mm) and their interactions with the other batch HMs, are hypothesized to have high impact for the decision-making strategies to minimize the impacts of Pb. Three feature selection (FS) algorithms namely the Boruta method, genetic algorithm (GA) and extreme gradient boosting (XGBoost) were investigated to select the highly important predictors for Pb concentration in the coastal bay sediments of Australia. These FS algorithms were statistically evaluated using principal component analysis (PCA) Biplot along with the correlation metrics describing the statistical characteristics that exist in the input and output parameter space of the models. To ensure a high accuracy attained by the applied predictive artificial intelligence (AI) models i.e., XGBoost, support vector machine (SVM) and random forest (RF), an auto-hyper-parameter tuning process using a Grid-search approach was also implemented. Cu, Ni, Ce, and Fe were selected by all the three applied FS algorithms whereas the Tavg and Rn inputs remained the essential parameters identified by GA and Boruta. The order of the FS outcome was XGBoost > GA > Boruta based on the applied statistical examination and the PCA Biplot results and the order of applied AI predictive models was XGBoost-SVM > GA-SVM > Boruta-SVM, where the SVM model remained at the top performance among the other statistical metrics. Based on the Taylor diagram for model evaluation, the RF model was reflected only marginally different so overall, the proposed integrative AI model provided an evidence a robust and reliable predictive technique used for coastal sediment Pb prediction.

MOLECULAR CONFIRMATION OF TWO HONEYBEE SPECIES (Apis mellifera L. and A. cerana F.) IN APIARY AND THEIR FORAGING BEHAVIOR IN LITCHI ORCHARD

The foraging of honeybees is one of the most well-organized and admirable behaviors that exists among social insects and being greatly influenced by nectarine sources and habitat adaptability. In Bangladesh, apiculture is mostly confined to rearing of European honeybee Apis mellifera L. despite of having the native A. cerana F. due to lack of information about comparative foraging efficiency and productibility of two species in Asian cropland ecosystem. The present study aimed to molecular characterization of two honeybee species in apiary and their foraging performance on litchi orchard. The genetic identity was revealed thorough phylogenetic analysis with >90% bootstrap value using mitochondrial cytochrome oxidase sub unit- 1 (CO1) gene and nucleotide sequence data deposited to NCBI GenBank with accession number ON680900- ON680902 for A. mellifera and ON703291-ON703293 of A. cerana. Upon placing the identified bee hives in litchi orchard, the foraging efficiency were studied based on egression and ingression rate, number of bees visited flowers per minutes, and nectar and pollen collection efficiency in varied time series of the day. Principal component analysis (PCA) for measuring the contribution of different foraging parameters and the species wise PCA biplot revealed the better foraging efficiency by A. mellifera L. compared to A. cerana F. in litchi blooms. However, foraging efficiency of other nectarine sources should be analyzed for suggesting best performing bee species in apiculture.

Screening of Onion (Allium cepa L.) Genotypes for Waterlogging Tolerance.

Onion production is severely affected by waterlogging conditions, which are created due to heavy rainfall. Hence, the identification of waterlogging-tolerant onion genotypes is crucial for increasing onion production. In the present study, 100 distinct onion genotypes were screened for waterlogging tolerance under artificial conditions by using the phenotypic approach in the monsoon season of 2017. Based on plant survival and recovery and changes in bulb weight, we identified 19 tolerant, 27 intermediate tolerant, and 54 highly sensitive onion genotypes. The tolerant genotypes exhibited higher plant survival and better recovery and bulb size, whereas sensitive genotypes exhibited higher plant mortality, poor recovery, and small bulb size under waterlogging conditions. Furthermore, a subset of 12 contrasting genotypes was selected for field trials during monsoon seasons 2018 and 2019. Results revealed that considerable variation in the morphological, physiological, and yield characteristics were observed across the genotypes under stress conditions. Waterlogging-tolerant genotypes, namely, Acc. 1666, Acc. 1622, W-355, W-208, KH-M-2, and RGP-5, exhibited higher plant height, leaf number, leaf area, leaf length, chlorophyll content, membrane stability index (MSI), pyruvic acid, antioxidant content, and bulb yield than sensitive genotypes under stress conditions. Furthermore, the principal component analysis biplot revealed a strong association of leaf number, leaf area, chlorophyll content, MSI, and bulb yield with tolerant genotypes under stress conditions. The study indicates that the waterlogging-tolerant onion genotypes with promising stress-adaptive traits can be used in plant breeding programs for developing waterlogging-tolerant onion varieties.

Sunflower agronomic traits in field irrigation conditions

Multi-year studies are crucial for the evaluation of sunflower hybrids and quantifying the environmental effect in the expression of genetic potential. In order to assess the adaptation of eight hybrids and test the impact of water availability on plant height (PH), head diameter (HD), 1000-grain weight (TWG), hectolitre mass (HM), seed yield (SY), oil content (OC) and oil yield (OY), a two-year study was conducted with irrigation as treatment. Analysis of variance (ANOVA) and principal component analysis (PCA) confirmed the year influence on all tested agronomic traits. According to the three-way ANOVA, irrigation significantly affected all tested traits except HD, OC and OY. All agronomic traits had lower values in both rainfed and irrigated treatment in 2014, confirming the influence of the environment. The hybrid was a significant source of variation for all traits. ANOVA and PCA grouped hybrids 1, 7, and 8 in one group and 2, 3, 4, 5, and 6 in another. The first group had lower PH, HD, TGW and SY values and higher HM, OC and OY values, and the second group had reversed traits values. Furthermore, the PCA biplot indicates SY was positively correlated with PH, HD, TGW and OY and HM was positively correlated with OC. This facilitates the breeding process because it enables indirect breeding for economically important traits such as seed yield, oil content and oil yield. As treatments were significant sources of variation for PH, HM, TWG and SY, sunflower irrigating is considered justified and can be used as an additional agrotechnical measure to target the agronomic traits. Understanding the expression of traits under rainfed and irrigation conditions will greatly help design effective breeding programs by creating hybrids suitable for cultivation in semi-arid environments.

Biplots of kinematic variables and pollutant emissions for an intercity corridor

A thorough understanding of driver behavior is an important step to improve the environmental performance of road traffic. Accurate data analysis tools can be valuable to identify these concerns. The present study explores relationships between driving patterns, tailpipe emissions, and road differentiation by using Principal Component Analysis Biplot. This statistical methodology is suitable to identify patterns hidden in data and can be used as a visualization tool. In this study, the key variables included were: speed, engine speed (RPM), acceleration, and vehicular jerk (first derivative of acceleration), as kinematic variables, and carbon dioxide (CO2), nitrogen oxides (NOx), and VSP (Vehicle Specific Power) mode, as pollutant emission variables. For this purpose, second-by-second vehicle dynamics and tailpipe emissions data were collected in three passenger cars with different powertrains (gasoline, diesel, and hybrid) along with different types of routes (one partly urban/rural and two motorways with variations in traffic volumes) in Aveiro Region (Portugal). Results revealed that Biplots allowed to distinguish different driving behaviors, separate route types (urban/rural from motorways), establish some remarks about emissions, and also present the correlated variables in a single plot. Therefore, this technique can be considered as a useful visualization tool to explore real traffic-related data.

Development of an SNP Assay for Marker-Assisted Selection of Soil-Borne Rhizoctonia solani AG-2-2-IIIB Resistance in Sugar Beet.

Rhizoctonia solani, causing Rhizoctonia crown and root rot, is a major risk to sugar beet (Beta vulgaris L.) cultivation. The development of resistant varieties accelerated by marker-assisted selection is a priority of breeding programs. We report the identification of a single-nucleotide polymorphism (SNP) marker linked to Rhizoctonia resistance using restriction site-associated DNA (RAD) sequencing of two geographically discrete sets of plant materials with different degrees of resistance/susceptibility to enable a wider selection of superior genotypes. The variant calling pipeline utilized SAMtools for variant calling and the resulting raw SNPs from RAD sequencing (15,988 and 22,439 SNPs) were able to explain 13.40% and 25.45% of the phenotypic variation in the two sets of material from different sources of origin, respectively. An association analysis was carried out independently on both the datasets and mutually occurring significant SNPs were filtered depending on their contribution to the phenotype using principal component analysis (PCA) biplots. To provide a ready-to-use marker for the breeding community, a systematic molecular validation of significant SNPs distributed across the genome was undertaken to combine high-resolution melting, Sanger sequencing, and rhAmp SNP genotyping. We report that RsBv1 located on Chromosome 6 (9,000,093 bp) is significantly associated with Rhizoctonia resistance (p < 0.01) and able to explain 10% of the phenotypic disease variance. The related SNP assay is thus ready for marker-assisted selection in sugar beet breeding for Rhizoctonia resistance.

Evaluation of Drought Tolerance in Maize Inbred Lines Selected from the Shaan A Group and Shaan B Group

Drought is one of the most prevailing abiotic stresses affecting the growth, development, and productivity of maize. Knowledge of drought tolerance could help in maize improvement. However, less research has been done to comprehensively evaluate the drought tolerance of maize inbred lines. We used 27 elite maize inbred lines selected from Shaan A group and Shaan B group breeding populations to estimate their drought tolerance in 3 years 2 locations under normal field conditions and low irrigation. Using principal component analysis (PCA) and GGE biplots, all inbred lines, including the controls, could be divided into four types. Ten lines could be categorized as the high-yield drought-resistant type (‘KB081’, ‘KA105’, ‘KB417’, ‘KB215’, ‘KB-7’, ‘2013KB-37’, ‘KA203’, ‘2012KA-34’, ‘KA225’, and ‘91227’) because of their stability and wide adaptability. Compared with the controls, a large proportion of the inbred lines selected from Shaan A and Shaan B breeding populations demonstrated higher drought resistance. Our results suggest that multi-year drought screening can be used as a tool to improve the drought resistance of maize inbred lines and provide a scientific basis for making better use of the Shaan A and Shaan B maize inbred lines to breed new varieties and to identify existing drought-resistant maize varieties.

Production and Profitability of Hybrid Rice Is Influenced by Different Nutrient Management Practices

The government of Nepal has recommended blanket fertilizer application for rice cultivation, which results in lower nutrient use efficiency (NUE) particularly under rainfed conditions. With the aim of finding an appropriate nutrient management practices concerning rice production and profitability, a field experiment was conducted during rainy season of 2017 and 2018 at Kavrepalanchowk and Dang district of Nepal. Altogether, five treatments comprising various nutrient management practices viz. Nutrient Expert Model (NE), use of Leaf Color Chart (LCC), Government Recommended Fertilizer Dose (GON), Farm Yard Manure (FYM), and Farmers’ Field Practice (FFP), were laid out in RCBD with four replications in farmers’ fields. The analysis of variance showed significant difference between treatments for test weight and grain yield in Kavrepalanchowk whereas all traits except number of effective tillers were significant in Dang. The significantly higher grain yield and harvest index were obtained in NE, followed by LCC; and the overall straw yield was highest in LCC, followed by NE in both the locations. Also, yield gap analysis suggested the NE had 44.44% and 23.97% increase in yield as compared to FPP in Kavrepalanchowk and Dang, respectively. The combined analysis with Best Linear Unbiased Estimator revealed the interaction of nutrient management and location significantly effects the straw yield and harvest index across both the locations. The estimated mean straw yield and harvest index were 10.93 t/ha and 34.98%, respectively. Both correlation study and biplot of principal component analysis signaled grain yield had positive correlation with all other traits. Furthermore, the net revenue was maximum for NE, followed by LCC in both the locations. The benefit: cost ratio was highest for NE which was 1.55 in Kavrepalanchowk and 2.61 in Dang. On the basis of these findings, NE and LCC can be effectively used as nutrient management practice by the farmers to obtain maximum production and profitability in Rice.

Transforming livestock productivity through watershed interventions: A case study of Parasai-Sindh watershed in Bundelkhand region of Central India

CONTEXTGlobal experiences reveal the positive impact of watershed-based interventions in improving livelihoods and environmental security. In the drylands, increasing forage resources and improving livestock productivity is a critical challenge. OBJECTIVESThe overarching aim of this paper is to analyse the impact of watershed-based interventions on livestock population, productivity, fodder resources, and biomass availability. The paper describes the interrelationship between land, water, crop, and livestock and how the gap in forage deficit can be bridged through a range of watershed interventions. METHODSThe study was undertaken in the Parasai-Sindh watershed of Central India between 2011 and 2016. A 300-year-old defunct haveli (a traditional rainwater harvesting structure) was renovated and nine check dams along the drainage line were constructed. Nearly 25,000 running-meters of field bunds were constructed. Large fields (2-3 ha) were divided into relatively smaller plots (0.3–0.5 ha) to reduce runoff velocity. The impact of watershed interventions on water availability, livestock productivity, forage yield, and income was studied through hydrological monitoring, primary household survey and principal component analysis (PCA) biplot and hierarchical clustering. RESULTS AND CONCLUSIONSThe watershed interventions enhanced groundwater availability leading to greater fodder availability, 22% increase in livestock population (ACU) and a 120% increase in milk production over a period of five years. The bovine population recorded a surge of 193% (cattle) and 32% (buffalo) over this period. Mean dry matter (DM, fodder) availability also increased from 10 t/household/year to 16.7 t/household/year owing to improved water availability. Groundnut (rainy season) and wheat (post rainy season) contributed significantly as forage resources. There was an annual forage demand of 5560 t DM for a livestock population of 2175 ACU in 2011 and of 6770 t DM for a livestock population of 2650 ACU in 2016. Annual forage biomass availability was estimated at 4219 t DM in 2011 and 6977 t DM in 2016. There was a deficit of 1341 t DM (24%) in 2011 which turned into a 3% surplus of 210 t DM in 2016 through watershed-based interventions. With increased cropping intensity and milk production, average annual household income increased from US$ 1325 to US$ 2430 over the five-year period. SIGNIFICANCEThis case study clearly illustrates the impact of watershed-based interventions on livestock population, productivity, forage resources, and biomass availability to bridge the gap in forage deficit. Its findings serve as a guide to widely scale up watershed-based interventions for improved water and biomass availability, and livestock productivity in semi-arid and dryland regions.

Evaluation, Effect of Whitening Agents on Physico-Chemical and Functional Properties of Sardine Surimi (Sardina pilchardus)

The effects of hydrogen peroxide (H2O2), sodium bicarbonate (NaHCO3) and calcium carbonate (CaCO3) treatments on the colour and textural properties of sardine surimi (Sardina pilchardus) were studied. Principal Component Analysis (PCA) was applied in order to investigate their effects and to determine the optimum whitening agents used. Addition of CaCO3 and H2O2 significantly improved whiteness of surimi in comparison to NaHCO3 treatments (p &lt; 0.05). Some textural damage and a reduction in WHC values were observed for surimi treated withH2O2 and NaHCO3 (p &lt; 0.05). PCA biplot showed that 1.5% CaCO3 tended to result in improved whiteness, WHC and textural properties since 1.5% CaCO3 sample sits closer to these functional properties vector lines than the other treatments. Whereas, 2.5% H2O2 had positively affected only the whiteness parameter. Results indicated that treating mince with the appropriate type and concentration of whitening agent can improve the functional properties of surimi, particularly from fish species with darker meat such as sardine.&#x0D; Aims: In order to improve whiteness and functional properties of sardine surimi (Sardina pilchardus), the effects of hydrogen peroxide (H2O2), sodium bicarbonate (NaHCO3) and calcium carbonate (CaCO3) treatments were studied. Principal Component Analysis (PCA) was applied in order to investigate their effects and to determine the optimum whitening agents used.&#x0D; Study Design: Experimental Research Design&#x0D; Place and Duration of Study: Research Unit “Biopreservation and Valorisation of Agro-Food Products” of the Higher Graduate School of Food Industry of Tunisia. The study was conducted in 3 months.&#x0D; Methodology: Sardine surimi samples were prepared with different treatments at different concentrations: calcium carbonate (CaCO3), hydrogen peroxide (H2O2) and sodium bicarbonate (NaHCO3). Proximate composition, total pigment, whiteness, water holding capacity and textural properties were investigated. Optimal levels of each whitening agent were determined using PCA.&#x0D; Results: Addition of CaCO3 and H2O2 significantly improved whiteness of surimi in comparison to NaHCO3 treatments (p &lt; 0.05). Some textural damage and a reduction in WHC values were observed for surimi treated withH2O2 and NaHCO3 (p &lt; 0.05). PCA biplot showed that 1.5% CaCO3 tended to result in improved whiteness, WHC and textural properties since 1.5% CaCO3 sample sits closer to these functional properties vector lines than the other treatments. Whereas, 2.5% H2O2 had positively affected only the whiteness parameter.&#x0D; Conclusion: Results indicated that treating mince with the appropriate type and concentration of whitening agent can improve the functional properties of surimi, particularly from fish species with darker meat such as sardine.

Seed Germination and Seedling Growth Responses to Different Sources and Application Rates of Hydrothermal Carbonization Processed Liquid

Hydrothermal carbonization processed liquid (HTCPL) is a by-product of hydrothermal carbonization of biomass, which is used sparingly as natural fertilizer. A study was performed in the Faculty of Agriculture, Dalhousie University (Canada) between June 2019 and April 2020 to evaluate the elemental composition of HTCPL derived from three different biomass feedstock; namely, seafood compost; buckwheat (Fagopyrum esculentum), and willow (Salix babylonica). Different HTCPL application rates (0-10%) were tested on seed germination and seedling growth of pea (Pisum sativum), sunflower (Helianthus annuus), pac choi (Brassica rapa subsp. chinensis), kale (Brassica oleracea var. sabellica) and lettuce (Lactuca sativa). Elemental composition was higher in the HTCPLs compared to their respective feedstocks except for nitrogen. The 5% and 10% willow HTCPL with a pH between 3.8-4.0 inhibited seed germination and seedling growth compared to the other treatments with a pH range between 4.6-5.8. Kale, lettuce and sunflower radicle and hypocotyl growth were promoted following treatments of their respective seeds with seafood compost HTCPL while pea radicle and hypocotyl lengths were best promoted by 5% buckwheat and 10% seafood compost HTCPLs. Comparatively, 0.5% willow HTCPL increased surface area of seedling radicles while 1% willow and 0.5% buckwheat HTCPLs increased surface area of hypocotyls, irrespective of plant species. The distinction among the treatments was demonstrated on a 2-dimensional principal component analysis biplot that explained 89% of the variations in dataset. Overall, buckwheat HTCPL proved to be more effective at increasing seed germination and seedling growth compared to the other HTCPLs. The inhibitory effect of willow HTCPL at high application rate (5-10%) were obvious for all plant species. A comprehensive non-targeted chemical profile of HTCPL will help to explain mechanisms.

Modification of Kutki millet (Panicum sumatrense) starch properties by the addition of amino acids for the preparation of hydrogels and its characterization

Starch is a biopolymer containing hydrophilic groups and is used in hydrogels preparation. Amino acids are multifunctional monomers of proteins that can be used as a cross linker to modify the starch by incorporating new functional groups into its chains. In this study, the Kutki millet starch was isolated and modified with lysine (positively charged), aspartic acid (negatively charged), and threonine (neutral) at varying pH levels. These modified starches were characterized for their various functional, structural, pasting, and textural properties. Hydrogels prepared from Lys9-KMS, Thr9-KMS, and AA11-KMS, possessing less adhesiveness, strong integrity, and hardness were then characterized for their XRD and morphological characterization. The principal component analysis (PCA) biplot showed that the samples modified at higher pH levels are positively correlated with the textural properties, swelling power and amylose content (I and IV quadrants), than those modified at lower pH. It may be inferred that starch modified with amino acid at higher pH have good textural properties than those at lower pH. Results of the overall investigation indicated that among these three amino acids, lysine could be a better cross linker for modification of Kutki millet starch and preparation of their gels for the delivery of nutraceuticals.

Discriminability and uncertainty in principal component analysis (PCA) of temporal check-all-that-apply (TCATA) data

Temporal check-all-that-apply (TCATA) data can be summarized and explored using principal component analysis (PCA). Here we analyze TCATA data on Syrah wines obtained from a trained sensory panel. We evaluate new and existing methods to explore the uncertainty in the PCA scores. To do so, we use the bootstrap procedure to obtain many virtual panels from the real panel’s data. Virtual-panel PCA scores are obtained using two methods. The first method, called the partial bootstrap (PB), obtains virtual-panel scores from regression. The second method, called the truncated total bootstrap (TTB), applies PCA to the virtual-panel results to obtain scores, which are truncated and superimposed on the real-panel scores by Procrustes rotation. We use the virtual scores from each method to investigate uncertainty in the real-panel PCA scores visually and numerically. To understand the uncertainty of the scores, we obtain confidence ellipses (CEs) and their areas, as well as confidence intervals (CIs) and their widths. Next, to determine whether PCA scores for different samples are well separated, we propose a procedure for approximating the standard errors of sample differences and correcting for multiple comparisons. We propose a discriminability index, and show that it can enhance the interpretability of PCA results. We incorporate graphical features into our PCA biplots to visualize discriminability. We did not find a large difference between the PB and TTB methods for understanding the uncertainty and discriminability in PCA scores. Although the TCATA data that we analyzed have a special structure, the methodological approaches presented here can be readily adapted to other applications of PCA.

BIPLOT ANALYSIS ON PRINCIPAL COMPONENTS OF HUMAN DEVELOPMENT IN ASEAN COUNTRIES

The Human Development Index (HDI) has been the key indicator for assessing the development of a country throughout the years. It is conducted from four indicators that represent the health dimension, the education dimension, and the standard of living dimension. In ASEAN countries, the HDI tends to rise from year to year, with some countries can achieve the very high and high level of human development, while the others are still in the medium level. The aim of this study is to find the information about relative positions, characteristic similarities between ASEAN countries and the diversity of the components that construct the human development index. The Principal Component Analysis Biplot used divides the ten countries into four groups. Group 1 are the countries with the high scores especially in GNI per capita, group 2 are the ones with high scores especially in the mean years of schooling, group 3 have low scores especially in GNI per capita, and group 4 have low scores especially in the mean years of schooling

English

Salinity stress is one of the leading abiotic stresses seriously affecting the crop productivity across the world. Early growth stages are more affected by salinity stress than terminal growth stages. Therefore, maize genotypes were evaluated for their performance in three different studies (Experiment-1: irrigation of seeds with salt solutions (distilled water, 4 dS/m, 6 dS/m and 10 dS/m) for 5 days, Experiment-2: application of different salinity treatments (distilled water, 4 dS/m, 6 dS/m and 10 dS/m) for 20 days from sowing to seedling emergence stage, Experiment-3: application of salinity stresses in hydroponic culture under four above mentioned different salinity treatments. Time to start germination (TSG), time to 50% germination (TFG) and final germination percentage (FGP) of the maize genotypes was reduced by salinity stress. Radicle length, plumule length, root and shoot length and fresh weight and chlorophyll contents were also linearly reduced in maize genotypes by increasing salinity levels. Moreover, sodium (Na+) concentration was increased in seeds and seedlings of maize genotypes whereas; potassium (K+) concentration was reduced with gradual rise in salinity levels. Principal Component Analysis (PCA) biplots facilitated the efficient assortment of susceptible, moderately susceptible, moderately tolerant and tolerant maize genotypes for each of the three studies separately. Genotype „YS-2008‟ was susceptible for seed treatment and hydroponic studies whereas, genotype „T267-5‟ was susceptible for early seedling and hydroponic studies. Genotype „C864-284‟ was tolerant in early seedling and hydroponic studies. The GGE biplot analysis indicated that „YS-2008‟ genotype was susceptible for salinity stress. Under hydroponic conditions, genotypes could effectively be discriminated by the imposition of severe salt stress (10 dS/m); however, under seed treatments and early seedling growth stages, mild stress treatments (4 dS/m) were sufficient enough to discriminate the maize genotypes. Hydroponic evaluation of genotypes is suitable and preferable under high stress conditions at early growth stages. Identified tolerant and susceptible genotypes could be effectively used in different breeding programs to develop salt tolerant new cultivars for commercial purposes. © 2021 Friends Science Publishers

Effect of capsaicinoids in hot pepper powder on microbial community and free sugar during kimchi fermentation.

Effect of capsaicinoids in hot pepper powder (HP) contains various chemical compounds, including capsaicin and dihydrocapsaicin, which are the main ingredients of the spicy taste. To evaluate the effect of HP on the microbial community in kimchi fermentation, kimchi [kimchi-HP, kimchi-HPE and kimchi-HPER made by adding HP, HP alcohol extract (HPE) and HPE residues (HPER)] was fermented at 4°C for 28 days. The pH and titratable acidity of the samples and the number of bacteria changed with fermentation time. Kimchi-HPER had significantly higher total viable and lactic acid bacteria (LAB) than other samples after 28 days of fermentation. The capsaicinoids content did not differ before and after fermentation, whereas the major free sugar content decreased, and the mannitol content increased. The principal component analysis (PCA) biplots showed similar patterns between kimchi-HP and -HPE. It was confirmed that Leuconostoc and Weissella were related to the initial fermentation, and Lactobacillus was involved in late fermentation. Kimchi-HP and kimchi-HPE increased the ratio of Lactobacillus sakei and decreased that of Leuconostoc mesenteroides compared to kimchi-HPER. Overall, these results revealed that capsaicinoids contained in HP affected Lactobacillus proliferation and mannitol increase during kimchi fermentation.

Categorical CVA biplots

Techniques to visualise and understand large amounts of data are of paramount importance. In most settings, this data is usually multivariate, which further stresses the need for effective visualisation techniques. Multivariate visualisation techniques such as canonical variate analysis (CVA) biplots allow for simultaneous lower-dimensional visualisation and data classification by incorporating class-specific data. CVA biplots, however, are currently restricted to numerical data. Through combining concepts from both CVA and non-linear principal component analysis (PCA) biplots, a new biplot construction methodology that improves on the traditional CVA biplot by allowing for categorical variables is proposed. This technique, named CVA(Hr), is showcased using the established mushroom data set, which contains a mix of categorical and ordinal variables. This novel method improves upon existing biplot construction in terms of classification accuracy and class separation.

Identification of high-yielding wheat genotypes resistant to Pyrenophora tritici-repentis (tan spot)

Tan spot, caused by Pyrenophora tritici-repentis, is a major foliar disease of wheat in Kazakhstan. This study evaluated 103 winter wheat genotypes for tan spot resistance and high-productivity traits in the field in two years. Entries were also characterized using the molecular marker Xfcp623, indicative of Tsn1 gene conferring sensitivity to Ptr ToxA. The proportion of entries insensitive to Ptr ToxA was relatively high (71%). Forty-eight entries with the lowest tan spot severity in the field were confirmed to be insensitivity to Ptr ToxA in the molecular screening. Entries which were resistant under field conditions had similar level of seedling resistance. Principal component analysis biplots showed, that all spike productivity traits were highly correlated. Days to heading (DH) and tan spot severity, as well as the normalized difference vegetation index and DH were strongly correlated. Thousand kernels weight (TKW) and plant height were highly correlated and both were negatively correlated with DH and tan spot severity, indicating that taller cultivars tended to be more resistant. The Xfcp623 negative entries had lower disease severity (by about 40%), flowered up to 2 days later than entries with Tsn1 and had significantly more spikelets per spike, number of grains per spike, TKW and grain weight/spike. The results of the study will be important for increasing the efficiency of breeding based on the elimination of the genotypes with dominant allele Tsn1. Of the 103 entries evaluated, 28 can be directly used in breeding programs to improve tan spot resistance and productivity of winter wheat.