Principal Component Analysis Biplot Research Articles

Parametric and Non-Parametric Measures to Identify Stable and Adaptable Cotton (Gossypium Hirsutum L.) Genotypes

ABSTRACT The study, comprised seventeen genotypes, was conducted during 2016 to 2019 growing seasons. The design was Randomized Complete Block Design (RCBD) with three replications to identify adaptable cotton genotypes. Parametric and non-parametric measures, principal component analysis (PCA) and correlation among the ranks of the parameters computed using R software. The environment, genotype and genotype-environment interaction contributed 79.58, 3.2 and 15.46% of the variation, respectively, and were significant (p < 0.001). The highest (1510.7 kg/ha) and lowest (1080.1 kg/ha) seed cotton yield was recorded from G12 and G8 genotypes respectively. Coefficients of determination (R2) and superiority index (Pi) recognized G13, G11, G10 and G11, G12, G7 as the top three stable genotypes correspondingly. G10 was selected three times as superior by stability variance (σ2), wricke’s ecovalence (Wi) and deviation from regression (S2di). Seed cotton yield positively correlated with GAI, Kang’s rank-sum (KRS), Huhn and Nassar S(1), S(6) S(3) while negatively correlated with Thennarasu stability measures NP(2), NP(3) and NP(4 . The first, second and third clusters of the PCA biplot comprised, 4, 4 and 10 parameters respectively. G12, with highest seed cotton yield and good stability, was the best genotype and recommended for variety verification.

Multivariate analysis of yield and quality traits in sweet potato genotypes (Ipomoea batatas L.)

Improving sweet potato traits and genotype selection based on specific traits of interest is crucial to motivate farmers and rural households to enhance their livelihoods by increasing income and improving nutritional intake. This research offers a thorough analysis of genotype selection guidelines considering multiple traits of sweet potatoes, including vine length (VL), average root length (ARL), average root diameter (ARD), marketable storage root number (MRN), marketable storage root yield (MRY), non-marketable root number (NMRN), non-marketable root weight (NMRW), dry weight of root (DW), beta-carotene content (BC), vitamin C content (VC), starch content (ST), total sugar content (TS), total soluble solid (TSS), and titratable acidity (ACD). The findings showed that the genotype Moz1.15 achieved the highest MRY at 46.46 t/ha, H5.ej.10 displayed the top BC level at 48.94 mg/100 g, and Moz1.9 had the highest VC (23.89 mg/100 g). Furthermore, there was a significant positive correlation between VL and DW, TSS, ST, TS, and VC. Additionally, ARL, ARD, and MRN showed significant positive correlations with MRY. The principal component analysis (PCA) biplot further confirmed how traits were connected with each other. There were four separate clusters of genotypes found, each having its specific significant trait representation. The distance between these clusters ranged from 21.79 to 54.13, while within each cluster, the distances varied from 0 to 17.45. The factor analysis using the multi-trait genotype-ideotype distance index (MGIDI) showed that sweet potato traits played a substantial role in three distinct factors during both growing seasons (2020-21 and 2021-22). This allowed for the selection of genotypes capable of achieving favorable genetic gains ranging from 1.86% to 75.4%, alongside a broad-sense heritability (h2b) spanning from 64.9% to 99.8%. Through the application of the MGIDI index, a number of potential genotypes were identified, among which BARI Mistialu-12 and H9.7.12 stood out as consistently chosen across both years. These promising genotypes exhibited high performance while displaying their strengths and weaknesses in terms of their contributions to the identified factors. This thorough research offers valuable insights into the complex relationship between different traits in sweet potatoes and how genotype selection based on a multi-trait index can significantly assist in shaping upcoming breeding and cultivation strategies.

Uncovering Key Factors in Graphene Aerogel-Based Electrocatalysts for Sustainable Hydrogen Production: An Unsupervised Machine Learning Approach.

The application of principal component analysis (PCA) as an unsupervised learning method has been used in uncovering correlations among diverse features of aerogel-based electrocatalysts. This analytical approach facilitates a comprehensive exploration of catalytic activity, revealing intricate relationships with various physical and electrochemical properties. The first two principal components (PCs), collectively capturing nearly 70% of the total variance, attested the reliability and efficacy of PCA in unveiling meaningful patterns. This study challenges the conventional understanding that a material's reactivity is solely dictated by the quantity of catalyst loaded. Instead, it unveils a complex perspective, highlighting that reactivity is intricately influenced by the material's overall design and structure. The PCA bi-plot uncovers correlations between pH and Tafel slope, suggesting an interdependence between these variables and providing valuable insights into the complex interactions among physical and electrochemical properties. Tafel slope stands to be positively correlated with PC1 and PC2, showing an evident positive correlation with the pH. These findings showed that the pH can have a positive correlation with the Tafel slope, however, it does not necessarily reflect a direct positive correlation with the overpotential. The impact of pH on current density (j)and Tafel slope underscores the importance of adjusting pH to lower overpotential effectively, enhancing catalytic activity. Surface area (from 30 to 533 m2 g-1) emerges as a key physical property, inclusively inverse correlation with overpotential, indicating its direct role in lowering overpotential and increasing catalytic activity. The introduction of PC3, in conjunction with PC1, enriches the analysis by revealing consistent trends despite a slightly lower variance (60%). This reinforces the robustness of PCA in delineating distinct characteristics of graphene aerogels, affirming their potential implications in diverse electrocatalytic applications. In summary, PCA proves to be a valuable tool for unraveling complex relationships within aerogel-based electrocatalysts, extending insights beyond catalytic sites to emphasize the broader spectrum of material properties. This approach enhances comprehension of dataset intricacies and holds promise for guiding the development of more effective and versatile electrocatalytic materials.

Advanced Mutant Line Developed from Fatemadhan Shows Salinity Tolerance at both Seedling and Reproductive Stages

The generation of high-yielding rice mutants and their assessment under salt stress offers a great possibility to isolate salt tolerant line(s) with desired trait of interest. Two separate experiments were conducted at the seedling and reproductive stages of rice to assess the level of salinity tolerance of few advanced high-yielding rice mutants. In the first experiment, rice seedlings were grown under hydroponic conditions and 14-day-old seedlings were subjected to salt stress (EC=10 dS/m; 7 days). Salt stress caused significant reduction in root and shoot length and biomass and leaf chlorophyll content; however, a little reduction was found in the mutant Line-1. In contrast, a sharp increase in shoot Na+/K+ ratio was found in all the genotypes except, Binadhan-10, FL-478 and the mutant Line-1, which exhibited little increased ratio. The second experiment involved exposure of plant to salt stress (EC=10 dS/m) for three weeks at the late booting stage in a sizable plastic tub filled with field soil. Salt stress resulted in a significant decrease in yield and yield attributing traits in all the genotypes except Binadhan-10. Grain yield per panicle was found significantly positive correlation with panicle length, the number of filled grains per panicle, and 100-seed weight under both control and salt stress conditions. Based on the studied traits and stress tolerance indices, Binadhan-10 and mutant Line-1 categorized as salt tolerant and rest of the genotypes were categorized as susceptible, which is also evident from the biplot of principal component analysis. Considering the results from both of the experiments, mutant Line-1 was found tolerant genotype at both seedling and reproductive stage. However, further studies are required to determine the genetic issues controlling the salinity tolerance in mutant Line-1 and the high-yield potential of mutant Line-65 under control condition in a way to develop salt tolerant and high-yielding rice varieties, respectively.

Legume Fingerprinting through Lipid Composition: Utilizing GC/MS with Multivariate Statistics.

This study presents a tentative analysis of the lipid composition of 47 legume samples, encompassing species such as Phaseolus spp., Vicia spp., Pisum spp., and Lathyrus spp. Lipid extraction and GC/MS (gas chromatography with mass spectrometric detection) analysis were conducted, followed by multivariate statistical methods for data interpretation. Hierarchical Cluster Analysis (HCA) revealed two major clusters, distinguishing beans and snap beans (Phaseolus spp.) from faba beans (Vicia faba), peas (Pisum sativum), and grass peas (Lathyrus sativus). Principal Component Analysis (PCA) yielded 2D and 3D score plots, effectively discriminating legume species. Linear Discriminant Analysis (LDA) achieved a 100% accurate classification of the training set and a 90% accuracy of the test set. The lipid-based fingerprinting elucidated compounds crucial for discrimination. Both PCA and LDA biplots highlighted squalene and fatty acid methyl esters (FAMEs) of 9,12,15-octadecatrienoic acid (C18:3) and 5,11,14,17-eicosatetraenoic acid (C20:4) as influential in the clustering of beans and snap beans. Unique compounds, including 13-docosenoic acid (C22:1) and γ-tocopherol, O-methyl-, characterized grass pea samples. Faba bean samples were discriminated by FAMEs of heneicosanoic acid (C21:0) and oxiraneoctanoic acid, 3-octyl- (C18-ox). However, C18-ox was also found in pea samples, but in significantly lower amounts. This research demonstrates the efficacy of lipid analysis coupled with multivariate statistics for accurate differentiation and classification of legumes, according to their botanical origins.

Physicochemical homogeneity of stingless bee honey (Heterotrigona itama) produced in the west coast, east coast and inland area of Peninsular Malaysia.

The nutritional composition of stingless bee honey (SBH) can be affected by different climates and soil composition across different geographical areas. However, the range of attributes set for a honey quality standard should be inclusive. This study analysed the sugar profile's physiochemical properties, including quantifying the rare sugar trehalulose, organic acid and mineral composition of SBH collected from inland, and west and east coasts of Peninsular Malaysia. Forty-three SBH (Heterotrigona itama) samples were collected and labelled as <20 and <40 West Coast (<20WC, <40WC), <20 and <40 East Coast (<20EC, <40EC) and Inland, according to their distance from the coasts. The moisture, pH and sugar composition of all SBH samples adhered to the Malaysian Kelulut Honey Standard (MS2683:2017) but not to the International Codex Standard (CODEX) for honey. Trehalulose presence in all samples, regardless of geographical area, was predominant alongside fructose and glucose. Only hydroxymethylfurfural (HMF) content and electrical conductivity (EC) results complied with both standards. The principal component analysis biplot showed that the discrimination of SBH according to the five different areas was not feasible, indicating sample homogeneity. The physicochemical evaluation of SBH from Peninsular Malaysia shows mainly homogeneous attributes of samples across geographical locations. These findings demonstrated that the current MS2683:2017 is relevant and accommodates all SBH of H. itama species produced in Peninsular Malaysia. Furthermore, the trehalulose range calculated in this study can be implemented as a new benchmark for the indicator of SBH honey quality standard by national and international food standard committees. © 2023 Society of Chemical Industry.

Cardiovascular risk and systemic inflammation in male professional rugby: a cross-sectional study

ObjectiveTo investigate cardiovascular risk factors’ prevalence and association with systemic inflammation in professional male rugby players (RP).MethodsA cross-sectional investigation of 46 professional male RP (26.1±4.1 years) cardiovascular risk factors were...

Phytochemical composition, anti-amylase and anti-acetylcholinesterase activities of leaf extracts and herbal tea of Dolichandrone serrulata (DC.) seem

The number of elderly population is continually growing in Thailand. Major health problems mostly occurred in the elder are non-communicable diseases namely Alzheimer's disease and type 2 diabetes mellitus. In current study, phytochemical composition, anti-amylase and anti-acetylcholinesterase activities of leaf extracts and herbal tea from Dolichandrone serrulata (DC.) Seem were determined. The result showed that extracts and herbal tea samples of D. serrulata leaves had effective in inhibition of amylase (an effective concentration at half maximum (EC50) = 1.21 to 23.80 mg/ml) and acetylcholinesterase activities (EC50 = 2.63 to 49.55 mg/ml). The effect of infusion time on the inhibition of amylase indicated that the best infusion time for preparing herbal tea beverage was 3 min, while the inhibition of acetylcholinesterase was 5 min. The fourier transform infrared spectroscopy (FTIR) and gas chromatograph - mass spectrometer (GC–MS) data indicated the presence of phytochemicals in the extracts and herbal tea samples related to phenolic compounds and flavonoids. The principal component analysis (PCA) result from FTIR data could discriminate herbal tea samples from the ethanol and aqueous extracts better than the PCA biplot of anti-amylase activity and anti-acetylcholinesterase activity.

A system for concurrent on-the-go soil apparent electrical conductivity and gamma-ray sensing in micro-irrigated orchards

On-the-go soil apparent electrical conductivity (ECa) sensors are great tools for mapping and monitoring soil properties such as water content, texture, and salinity. ECa maps and surveys are most useful and reliable when obtained in uniformly wet fields. However, soil moisture in micro-irrigated (e.g., drip or micro-sprinklers) orchards is typically non-uniform, with moist soil along tree and irrigation lines, and dry soil between tree rows. We developed a mobile platform and data post-processing algorithm to facilitate geospatial ECa measurements along or near driplines. Gamma-ray (γ-ray) spectrometry is commonly used for clay content and type mapping. Fusion between ECa and γ-ray is often reported to increase the accuracy of field-scale soil maps. However, contrarily to ECa, γ-ray spectrometry is best suited for sensing soils in dry conditions. Micro-irrigated orchards are ideal environments for the combined application of these two sensor technologies. The fusion of topsoil (top 0.5 m) ECa (measured along the driplines) and γ-ray total counts (TC) (measured between the tree rows) data was tested at a 0.4-ha sandy loam citrus orchard in Southern California. Here, we discuss sensor data acquisition, data processing, sensor-directed sampling scheme delineation, and characterization of field-scale soil particle size fraction (0–0.4 m soil profile) spatial variability. Pearson correlation coefficients between sand and silt content with both ECa and TC were significant (p < 0.05). A principal component analysis biplot suggested strong positive relationship with TC and clay content. Backwards stepwise multiple linear regression predicted sand content using TC, elevation, and spatial coordinates as explanatory variables with mean absolute error (MAE) of 3.06 %. Silt content was predicted (MAE=1.55 %) using ECa, elevation, and spatial coordinates. The development of this platform enables better characterization of soil properties in micro-irrigated orchard systems using on-the-go sensing technology.

Quantification of hormones and profiling of metabolites from vegetative to reproductive growth phase shift in Lagenaria siceraria (Molina) Standl

In the world of agricultural research, there has been a lot of emphasis on increasing insight into the decline of crop yield gap levels. Metabolomics is a relatively recent approach that aims to better understand the metabolic networks and is extensively being applied to agricultural research for its divergent growth and productivity by enhancing the behavior of plants under various stress. Lagenaria siceraria (Bottle gourd – Salad variety) has commonly been used because of its enormous nutritional, commercial, and medicinal benefits. The purpose of this investigation was to quantify the level of endogenous hormones and metabolites profiling at the vegetative and reproductive phase from various tissues of L. siceraria by liquid chromatography-tandem mass spectrometry (LC-MS/MS) and gas chromatography-mass spectrometry (GC–MS). The field-grown plant samples such as vegetative, male, and female flower-initiated parts including leaf, stem, tendril, and shoot apex were used for this study. The vegetative tissues had the highest level of trans zeatin and indole-3-acetic acid whereas indole-3-propanoic acid, trans zeatin, and gibberellic acid-3 were seen highest in the male flower-initiated tissues, and the level of IPA alone was found highest in the female flower-initiated tissues quantified by LC-MS/MS method. Additionally, N-Methyl-N-trimethylsilyl-trifluoroacetamide derivatized tissues showed the presence of astaxanthin and 1H-Indole, 2,3,5-trimethyl-1-(trimethylsilyl)- in vegetative tissues. On the contrary, kaur-16-ene-(8β,13β), an intermediate of GAs synthesis, gibberellic acid, and fumaric acid were recorded in reproductive tissues by GC–MS analysis. The first principal component analysis (PCA) biplot revealed two principal components that together accounted for 34.59 % and 22.96 % of the variance in the tissues of vegetative and reproductive phases respectively. In the second PCA biplot, the male and female flower-initiated tissues showed principal component levels of 42.94 % and 26.48 %, respectively. Also, the heat map analysis showed the fluctuation of phytohormone levels during both phases.

GENETIC ARCHITECTURE OF NEW PLANT TYPE RICE (ORYZA SATIVA L.) LINES BASED ON A 12-YEARS MULTI-EXPERIMENT

New plant-type (NPT) rice can increase grain yield globally. Although many studies have shown the association between harvest and yield-related traits in rice, it remains obscure in the case of NPT rice. This study aimed to elucidate the relationship between agronomic and yield features in new plant-type rice based on a 12-years multi-experiment. A large dataset of 704 NPT rice lines of various generations (F4 to F10), derived from 24 different populations, underwent study. The analysis of variance showed a highly significant population effect (P &lt; 0.01) on all traits studied. The NPT rice populations had groupings into four clusters, with a highly substantial cluster effect (P &lt; 0.01) on the number of total tillers (NTT), number of total grains (NTG), number of filled grains (NFG), and thousand-grain weight (TGW). Standard best NPT lines from each experiment achieved an average of 1.67 t ha-1 or 26% higher yield than the Ciherang variety. Determining the yield advantage of NPT lines was by higher plant height (PH) (r = 0.37), panicle length (PL) (r = 0.15), NTG (r = 0.28), NFG (r = 0.28), TGW (r = 0.10), and lower days to harvest (DTH) (r = -0.10). The principal component analysis biplot revealed that NFG could better serve as a selection characteristic for enhancing grain yield in developing NPT rice adapted to irrigated tropical ecosystems.

Formulation of a virgin coconut oil based spicy spread and its physico- chemical properties

Virgin coconut oil (VCO) fits the designation of a very important functional food and is expected to show a dramatic growth in the Indian market. This study aims to evaluate the physico- chemical characteristics of a spicy fat spread, formulated using virgin coconut oil, trans- free fat (TFF) and spice mixtures along with emulsifiers. The effect of varying the VCO: fat ratio (100:00; 30:70; 50:50; and 70:30) on the quality parameters of the spicy spread such as moisture, fat, protein, and ash were studied. The phase separation, colour characteristics, rheological quality, melting profiles by differential scanning calorimeter (DSC), and textural properties (spreadability) were also studied. Results show that the moisture of the spicy spreads ranged between 1.59% and 1.72%, fat 49–50%, protein 18–20%, and ash content ranged between 0.21% and 0.22%. The prepared spreads showed marginal oil separation at 25 °C (ambient) and higher amounts at 37 °C (accelerated) temperatures. The consistency or hardness measured in Newton (N) showed an increase with an increase in the amount of fat and simultaneous decrease with increased amounts of VCO. Similar trends were observed with the spicy spread's viscosity, yield stress and consistency index. Principal component analysis (PCA) indicated the presence of two clusters positioned in nearby locations of the PCA biplot. The first cluster comprised of attributes like spreadability and VCO aroma while the second had glossiness and smoothness. PCA and sensory analysis results showed that sample containing virgin coconut oil and TFF in equal proportions (50:50) was the most acceptable spread with respect to spreadability and overall sensory acceptability.

The Optimization of Medium Conditions and Auxins in the Induction of Adventitious Roots of Pokeweed (Phytolacca americana L.) and Their Phytochemical Constituents.

Pokeweed, Phytolacca americana L., is considered a widely spreading invasive plant, while saponin contents accumulated in the roots have pharmaceutical uses, such as rheumatism treatments and anti-inflammation. Adventitious root cultures are an important source of diverse secondary metabolites, which have significant applications in various fields. This study focused on the optimization of parameters for root induction using different medium states and treatments with auxins on a pokeweed leaf. Semisolid and liquid MS (Murashige and Skoog, 1962) media were supplemented with indole-3-butyric acid (IBA) and 1-naphthylacetic acid (NAA) at 0.5, 1, 2, and 4 mg/L. Root growth parameters, e.g., induction percentage, root numbers, length, and weight, were measured to determine the adventitious root induction efficiency. Total phenolic content, total flavonoid content, total saponin content, and antioxidant activity were recorded. Results showed that adventitious roots induced in semisolid MS medium supplemented with 0.5 mg/L NAA exhibited a high density of lateral roots. Appropriate medium state and auxin for adventitious root induction in pokeweed were determined as semisolid medium supplemented with 2 mg/L NAA. Considering phytochemicals, adventitious roots induced in liquid medium containing 0.5-1 mg/L NAA had the highest yield extract percentage. Additionally, adventitious roots cultivated in a liquid medium enriched with 1 mg/L NAA exhibited the highest phenolic and saponin contents. A principal component analysis (PCA) biplot and hierarchical cluster analysis (HCA) heatmap demonstrated different response patterns between semisolid and liquid media applied with NAA. The results of the semisolid media were grouped together due to high expression levels of the root induction parameters, while elevated phytochemical values were observed in the liquid media treatments. The results suggested two different media that provide the highest adventitious root induction efficiency and the greatest phytochemical contents: semisolid medium with 2 mg/L NAA and liquid medium with 1 mg/L NAA, respectively. These culture media can be applied to optimize adventitious root culture of pokeweed and in vitro phytochemical production.

Mineral prospectivity mapping using machine learning techniques for gold exploration in the Larder Lake area, Ontario, Canada

A mineral prospectivity map (MPM) focusing on gold mineralization in the Larder Lake region of Northern Ontario, Canada, has been produced in this study. We have used the Random Forest (RF) algorithm to use 32 predictor maps integrating geophysical, geochemical, and geological datasets from various sources that represent vectors to gold mineralization. It is evident from the efficiency of classification curves that MPMs generated are robust. The unsupervised algorithms, K-means and principal component analysis (PCA) were used to investigate and visualize the clustering nature of large geochemical and geophysical datasets. We used RQ-mode PCA to compute variable and object loadings simultaneously, which allows the displays of observations and the variables at the same scale. PCA biplots of the Larder Lake geochemical data show that Au is strongly correlated with W, S, Pb and K, but inversely correlated with Fe, Mn, Co, Mg, Ca, and Ni. The known gold mineralization locations were well classified by RF with the accuracy of 95.63 %. Furthermore, partial least squares-discriminant analysis (PLS-DA) model combines 3D geophysical clusters and geochemical compositions, which indicates the Au-rich areas are characterized with low to mid resistivity – low susceptibility properties. We conclude that the Larder Lake-Cadillac deformation zone (LLCDZ) is relatively more fertile than the Lincoln-Nipissing shear zone (LNSZ) with respect to gold mineralization due to deeper penetrating faults. The intersection of the LLCDZ and network of high-angle NE-trending cross faults acts as key conduits for gold endowments in the Larder Lake area. This study innovatively combined multivariate geological, geochemical, and geophysical datasets via machine learning algorithms, which improves identification of geochemical anomalies and interpretation of spatial features associated with gold mineralization.

Genome Analysis of Potential Probiotic Levilactobacillus brevis AcCh91 Isolated from Indian Home-Made Fermented Milk Product (Chhurpi).

Consumption of naturally fermented milk (NFM) products is the dietary culture in India. The mountainous people of Arunachal Pradesh in India prepare the assorted artisanal home-made NFM products from cow and yak milk. Previously, we isolated and identified 76 strains of lactic acid bacteria (LAB) from NFM products of Arunachal Pradesh, viz. mar, chhurpi and churkam. We hypothesized that some of these LAB strains may possess probiotic potentials; hence, we investigated the probiotic potentials of these strains. On the basis of in vitro and genetic screening for probiotic attributes including haemolytic ability, 20 LAB strains were selected out of 76 strains, for further analysis. Using in silico analysis, viz. multivariate heatmap and PCA (principal component analysis) biplot, Levilactobacillus brevis AcCh91 was selected as the most promising probiotic strain, which was further characterized by the whole-genome analysis. Lev. brevis AcCh91 showed the highest survival rate of 93.38% in low pH and 86.68 ± 2.69% in low bile and the highest hydrophobicity average of 86.34 ± 5.53%. This strain also showed auto-aggregation and co-aggregation with antimicrobial properties against the pathogens, showed ability to produce beta-galactosidase and cholesterol reduction property and, most importantly, produced GABA, an important psychobiotic element. Genomic analysis of Lev. brevis AcCh91 showed the presence of genes corresponding to GABA, vitamins, amino acids, cholesterol reduction, immunomodulation, bioactive peptides and antioxidant activity. The absence of antimicrobial-resistant genes and virulence factors was observed. Hence, genome analysis supports the probiotic potentials of Lev. brevis AcCh91, which may be further investigated to understand its health-promoting properties.

Assessment of Genetic Correlation, Path Coefficient Analysis and Principal Component Analysis in Horse Gram under Water Stress

A field experiment was conducted to evaluate the correlation and path analysis in thirty horse gram germplasm lines under moisture-stressed conditions using various selected characters. The association studies revealed a significant positive correlation of seed yield with relative water content, number of pods per plant, number of seeds per pod, total chlorophyll content, harvest index, proline content, root dry weight, root length and leaf area index. While days to 50% flowering, days to maturity, plant height and number of primary branches showed a significant negative correlation with yield. Path coefficient analysis revealed a high positive direct effect of the number of pods per plant on seed yield and the number of seeds per pod recorded exhibited the highest indirect effect on seed yield via the number of pods per plant under moisture stress conditions in horse gram. So, selection based on the number of pods per plant will be more beneficial under water stress conditions. The exploitation of genetic diversity is key to a successful breeding program. The principal component analysis using selected fifteen characters revealed the existence of adequate genetic variability among genotypes under study. First four principal components accounted for a cumulative variance of 82.47%. The principal component analysis (PCA) biplot revealed genotypes viz. IC22785, IC392329, HG 26L and IC20753 to highly diverse and these genotypes may be employed for enhancement of various traits in horse gram.

The Effect of Response Conditions on Food Images-Evoked Emotions Measured Using the Valence × Arousal Circumplex-Inspired Emotion Questionnaire (CEQ).

In 2020, a single-response-based, valence × arousal circumplex-inspired emotion questionnaire (CEQ) was developed. Using a between-participants design, previous studies have found that a multiple response (MR) condition better discriminated test samples (e.g., written food names) based on their evoked emotions than a single response (SR) condition. This research, comprising Studies 1 and 2, aimed to determine the effect of response conditions (i.e., SR vs. MR) on emotional responses to food image samples, using a within-participants design. In Study 1, 105 Korean participants were asked to select a pair of emotion terms (i.e., SR condition) or select all pairs representing their evoked emotions (i.e., MR condition) from a list of 12 pairs of emotion terms of the CEQ, in response to the 14 food images. Both SR and MR conditions were tested within a remote (online) session. To minimize both a potential carry-over effect of the "within-participants design" and an influence of environmental factors in the remote testing, Study 2 asked 64 U.S. participants to do so over two separated sessions on two different days in a controlled laboratory setting. In both Studies 1 and 2, participants selected the CEQ's emotion-term pairs in the MR condition more frequently than in the SR condition, leading to the MR condition's higher capacity to discriminate test samples. While the configurations of the correspondence analysis biplots drawn in the SR and MR conditions were similar, those in the MR condition were more likely to be similar to the configurations of the principal component analysis biplots drawn from the ratings of valence and arousal for food image samples. In conclusion, this study provides robust empirical evidence that the MR condition can perform better in capturing sample differences in food-evoked emotions, while the SR condition is also effective in characterizing emotional profiles of test samples. Our findings will provide practical insights to sensory professionals, enabling them to effectively leverage the CEQ or its variants when measuring food-evoked emotions.

The phylum Chloroflexi and their SAR202 clade dominate the microbiome of two marine sponges living in extreme environmental conditions

AbstractThe capacity of marine sponges to cope with environmental changes is often attributed to the specific composition of their bacterial communities. In this study, we assessed the bacterial microbiome of two dominant sponges, Rhabdastrella globostellata (Rg) and Hyrtios erectus (He), living in the Bouraké lagoon (New Caledonia), where abiotic conditions daily fluctuate according to the tide. Sponge specimens, sediment and seawater samples were collected at 2–3 m depth. The bacterial communities were assessed using 16S rRNA metabarcoding, and variations between the two sponges were compared using PCA biplots. Chloroflexi was the dominant phyla in both He and Rg with an average relative abundance of 41.2% and 53.2%, respectively, while it was absent in sediment and seawater. Among the phylum Chloroflexi, SAR202 clade was dominant in both sponges, reaching an average relative abundance of 53.2% (He) and 78.7% (Rg). Principal component analysis (PCA) was used to identify the main variables driving the bacterial community structure in both sponges. The results indicated that the bacterial community structure in both sponges was strongly associated with Chloroflexi (70.9% of the phyla variance) and SAR202 clade (86.6% of the clade variance). The high relative abundance of the phylum Chloroflexi and the SAR202 clade observed in this study is the highest reported so far in the literature in marine sponges. Such a high relative abundance of these bacteria could suggest their involvement in the well‐being of sponges in the extreme environmental conditions of Bouraké.

Monitoring the dynamics of acid mine drainage affected stream surface water hydrochemistry at Jaintia Hills, Meghalaya, India.

Streams are pristine natural life-thriving water sources for people living in the mountainous proximity of NE India, where water scarcity is a common occurrence in most villages and towns. In the last few decades, factors like coal mining had drastically reduced the usability of stream water in the region; as such, an attempt has been made to assess the spatiotemporal variation of stream water chemistry affected by acid mine drainage (AMD) at Jaintia Hills, Meghalaya. The water variables were subjected to a multivariate statistical technique of principal component analysis (PCA) to understand their condition at each sampling point while comprehensive pollution index (CPI) and water quality index (WQI) was incorporated to assess the quality status. Maximum WQI was recorded in S4 (541.14) during summer, while minimum value was estimated in winter at S1 (14.65). Throughout the seasons, the WQI revealed "Good" quality in S1 (unimpacted stream), while the impacted streams (S2, S3, and S4) exhibited a "Very poor" to "Water unsuitable for drinking" status. Similarly, in S1, the CPI showed a ranged value of 0.20 to 0.37, presenting a water quality status of "Clean to Sub-Clean," whereas, CPI of the impacted streams indicated "Severely polluted" status. In addition, PCA bi-plot presented higher affinity of free CO2, Pb, SO42-, EC, Fe, and Zn in AMD-impacted streams than in unimpacted streams. The result demonstrates the environmental issues induced by coal mine waste and in particular, stream water being severely affected by AMD in mining areas of Jaintia Hills. Thus, measures to stabilized the mine repercussions and cumulative effects on the water bodies need to be formulated by the government, as stream water will remain the primary water source for the tribal communities in this region.

Application of an Electronic Nose as a New Technology for Rapid Detection of Adulteration in Honey

This work demonstrates the application of an electronic nose (e-nose) for discrimination between authentic and adulterated honey. The developed e-nose is based on electrodes covered with ionogel (ionic liquid + gelatin + Fe3O4 nanoparticle) films. Authentic and adulterated honey samples were submitted to e-nose analysis, and the capacity of the sensors for discrimination between authentic and adulterated honey was evaluated using principal component analysis (PCA) based on average relative response data. From the PCA biplot, it was possible to note two well-defined clusters and no intersection was observed. To evaluate the relative response data as input for autonomous classification, different machine learning algorithms were evaluated, namely instance based (IBK), Kstar, Trees-J48 (J48), random forest (RF), multilayer perceptron (MLP), naive Bayes (NB), and sequential minimal optimization (SMO). Considering the average data, the highest accuracy was obtained for Kstar: 100% (k-fold = 3). Additionally, this algorithm was also compared regarding its sensitivity and specificity, both being 100% for both features. Thus, due to the rapidity, simplicity, and accuracy of the developed methodology, the technology based on e-noses has the potential to be applied to honey quality control.