Multivariate Statistical Analysis Research Articles

Hydrogeochemical characteristics of groundwater contamination in Guwahati city, Assam, India: Tracing the elemental Threads

Groundwater serves as an important resource for drinking and agriculture in many countries, including India. Assessing the quality of groundwater is essential for understanding its chemical characteristics and suitability for consumption. This study aims to explore the factors affecting the hydrogeochemical changes in groundwater within Guwahati City, Assam, India. Groundwater samples were collected and analyzed for major and trace elements, as well as anion concentrations. Concentrations of As, Al, Ba, Cu, F−, Fe, Mn, and Pb exceeded the permissible limits set by both World Health Organization (WHO) and Bureau of Indian Standards (BIS), indicating serious health concerns for the local inhabitants. The distribution pattern of trace elements exceeding the guideline values is intricate, suggesting widespread contamination of groundwater throughout the study area. The Heavy Metal Pollution Index (HPI) and Water Quality Index (WQI) revealed that, except for the central zone, groundwater across the entire study area requires intervention. Piper plot illustrated that the groundwater is predominantly of Ca–HCO3 type, indicating the dominance of alkaline earth and weak acids. Groundwater hydrogeochemistry is mainly controlled by rock-water interaction and evolves through silicate weathering, carbonate weathering, and cation exchange processes. Multivariate statistical analysis identified distinct groups of groundwater based on chemical characteristics, emphasizing the role of both natural processes and anthropogenic activities in influencing groundwater quality. Regular monitoring, management, and intervention of groundwater sources throughout the study area are crucial for long-term use. The findings of this study will assist stakeholders, regulators, and policymakers in formulating strategies for the sustainable use of groundwater.

Metabolite Profiling Reveals the Dynamic Changes in Non-Volatiles and Volatiles during the Enzymatic-Catalyzed Processing of Aijiao Oolong Tea.

The enzymatic reaction stage (ECS) of oolong tea processing plays an important role in the formation of the flavor quality of the oolong tea. To investigate the dynamic changes in the volatile and non-volatile components in the leaves of oolong tea during the ECS, metabolomic studies were carried out using the leaf samples collected at different stages of the ECS of Aijiao oolong tea. Out of the identified 306 non-volatile metabolites and 85 volatile metabolites, 159 non-volatile metabolites and 42 volatile metabolites were screened out as key differential metabolites for dynamic changes during the ECS. A multivariate statistical analysis on the key differential metabolites showed that the accumulations of most metabolites exhibited dynamic changes, while some amino acids, nucleosides, and organic acids accumulated significantly after turning-over treatment. The evolution characteristics of 27 key precursors or transformed VOCs during the ECS of Aijiao oolong tea were clarified, and it was found that the synthesis of aroma substances was mainly concentrated in lipids as precursors and glycosides as precursor pathways. The results revealed the dynamic changes in the flavor metabolites in the ECS during the processing of Aijiao oolong tea, which provided valuable information for the formation of the characteristic flavor of Aijiao oolong tea.

Laser-induced breakdown spectroscopy analysis of tourmaline: protocols, procedures, and predicaments

Abstract. Laser-induced breakdown spectroscopy (LIBS) is an appropriate choice of analytical tool for analysis of complex minerals because it is rapid, requires little sample preparation, and acquires major and trace element compositional information on all naturally occurring elements at concentrations above their intrinsic levels of detection for the specific analyte material. Tourmaline, a complex borosilicate mineral supergroup, was chosen as a test mineral due to the complexity of its major and minor element composition. Four analytical issues were investigated during project development: (1) the spacing between analytical laser shots to avoid analysis of the recast from previous laser ablations, (2) the efficacy of using a cleaning shot prior to data acquisition, (3) the number of ablations required to collect an average spectrum that is representative of the tourmaline sample, and (4) the effect of spectrometer drift on principal component analysis (PCA) when using the entire LIBS spectra to model the compositional variations within the sample suite. The minimum spacing between locations of analysis was determined to be 800 µm for the analytical conditions used in this study by examining spectra acquired in a 2×2 grid across a quartz–tourmaline boundary. At a spacing of 100 µm, twice the diameter of the laser beam, the intensity of the boron I peaks at 249.68 and 249.77 nm was very low in the first location (quartz) but quite high in the fourth location (quartz) due to deposition of tourmaline-composition recast by laser shots in the second and third locations (both on tourmaline). Increasing the distance between locations to 800 µm ensured that the area analyzed largely avoided the recast layer from previous ablations. Given that the distribution of recast was taken into account, no cleaning shots were collected. PCA score plots calculated using successively larger numbers of spectra from the same sample show that a total of 64 spots, or 16 2×2 grids, are needed to acquire a representative average analysis of tourmaline. Spectrometer drift was recognized in PCA loading plots by a characteristic splitting of element peaks; half the peak indicates positive loading and the other half of the peak indicates negative loading. Drift correction was aligned by placing the Ca II peak at 393.34 nm in the 393.398 bin; this correction eliminated split peaks in loading plots. The resolution of these issues yielded LIBS spectra suitable for multivariate statistical analysis that can be applied to understanding geologic processes. These results contribute to the application of rapid LIBS analysis of complex geomaterials.

Non-invasive methodology for the ecotoxicological sampling of anatids in urban and peri-urban areas

Urban waterbirds serve as crucial bioindicators for evaluating environmental pollution in urban areas, necessitating a focus on tailored non-invasive ecotoxicological biomonitoring methods. This paper adopts a multidisciplinary perspective to underscore the pivotal role of biomonitoring in gauging environmental quality and identifying threats. Emphasizing the versatility and ease of sampling urban birds, we highlight their correlation with human exposure to pollutants, alongside the significant role of wetlands, particularly Ramsar sites, in conservation endeavors and as habitats for migratory birds. Additionally, the paper delves into the relevance of urban birds in monitoring pollutants, showcasing their efficacy in reflecting urbanization or pollution gradients. Proposed non-invasive biomonitoring techniques, including feather and feces analysis, offer practical means to assess heavy metal, microplastics, and other pollutant concentrations without harming the avian species. A comprehensive methodology proposal for waterbird sampling, encompassing pre-sampling activities, basic and specialized laboratory characterization, and data collection in a Central Mexican Ramsar site, is detailed as examples and experiences into these considerations. Concluding insights underscore the impact of emerging pollutants, such as microplastics, on waterbird health and nutritional status, advocating for multivariate statistical analysis to elucidate correlations between morphological characteristics, elemental composition, and pollutant presence.

Landslide monitoring and prediction system using geosensors and wireless sensor network

Landslides in hilly regions are a frequently occurring natural phenomenon which takes a heavy toll of human lives and causes damage to different properties. Hence, prediction of landslide is essential for averting its deleterious effects by providing early warning to neighboring residents about the impending hazard of landslide. A landslide monitoring system has been designed using geosensors and high-range wireless network for on-line monitoring and prediction of landslides with the application of multivariate statistical analysis of prevailing site parameters. The system consists of various geosensors, wireless sensor network, server, and landslide monitoring and forecasting software. Crackmeter, in-place inclinometer, raingauge, tiltmeter, piezometer and other sensors are set up in the selected hill slope prone to landslide for continuous monitoring of influencing parameters. The system measures real-time landslide parameters using the said geosensors connected with wireless nodes and establishing a dynamic wireless network to overcome redundancy issue using wireless nodes of around 1200 m communication range using a high performance low power microcontroller, integrated solar panel and additional external omni-directional antenna for monitoring landslide in a large and hazardous hilly region from a distant safer location. The application software consists of different modules, namely data monitoring, analysis, storing, viewing, prediction, and generation of audio-visual, SMS and email alerts for 3 levels of landslides situations. The paper enumerates the system architecture and the application software details.Graphical

Assessment of Groundwater Quality Using the Pollution Index of Groundwater (PIG), Nitrate Pollution Index (NPI), Water Quality Index (WQI), Multivariate Statistical Analysis (MSA), and GIS Approaches: A Case Study of the Mnasra Region, Gharb Plain, Morocco

Groundwater, an invaluable resource crucial for irrigation and drinking purposes, significantly impacts human health and societal advancement. This study aims to evaluate the groundwater quality in the Mnasra region of the Gharb Plain, employing a comprehensive analysis of thirty samples collected from various locations, based on thirty-three physicochemical parameters. Utilizing tools like the Pollution Index of Groundwater (PIG), Nitrate Pollution Index (NPI), Water Quality Index (WQI), Irrigation Water Quality Index (IWQI), as well as Multivariate Statistical Approaches (MSA), and the Geographic Information System (GIS), this research identifies the sources of groundwater pollution. The results revealed Ca2+ dominance among cations and Cl− as the primary anion. The Piper and Gibbs diagrams illustrated the prevalent Ca2+-Cl− water type and the significance of water–rock interactions, respectively. The PIG values indicated that 86.66% of samples exhibited “Insignificant pollution”. NPI showed notable nitrate pollution (1.48 to 7.06), with 83.33% of samples rated “Good” for drinking based on the WQI. The IWQI revealed that 80% of samples were classified as “Excellent” and 16.66% as “Good”. Spatial analysis identified the eastern and southern sections as highly contaminated due to agricultural activities. These findings provide valuable insights for decision-makers to manage groundwater resources and promote sustainable water management in the Gharb region.

A statistical evaluation of the morphological variability of shortening landforms on Mercury

Observations of Mercury from both the Mariner 10 and MESSENGER missions showed that Mercury has a global population of shortening landforms, with several thousands of individual structures identified to date. The accommodation of widespread tectonic shortening is widely regarded to be the result of global contraction—the long, sustained cooling of the interior that has caused the planet to shrink. Shortening landforms on Mercury have been traditionally categorized into three distinct categories: lobate scarps, wrinkle ridges, and high-relief ridges. Although the clearest examples of shortening landforms at the time were used to describe and define these categories qualitatively, later studies showed that shortening landforms on Mercury display morphological characteristics that do not make for a ready classification into one of these “traditional” groups. More recently, other studies have classified shortening landforms based on the terrain that those landforms reside in to avoid generalizing morphology. In this study, we quantitatively assess the shape of shortening landforms by measuring and compiling a suite of 12 morphological parameters for 100 such structures across the planet. These parameters were evaluated for their importance in defining categories using two multivariate statistical analyses, a Principal Component Analysis (PCA) and Linear Discriminant Analysis (LDA). These methods allow us to assess any correlation that the traditional categories, terrain types, or alternative classification schemes have with the variation observed across our set of measurements. Our results show that the morphologic characteristics of shortening landforms on Mercury are not accurately captured by traditionally recognized groups. Instead, shortening landforms fall along a morphological spectrum, where only a few ideal examples of lobate scarps or wrinkle ridges provide clear endmembers. Therefore, despite the frequent use of the terms “lobate scarps” and “wrinkle ridges” in works regarding planetary tectonics, we find that such terminology does not appropriately define the morphology of shortening landforms found on Mercury and may lead to the generalization, or misinterpretation of landforms described as accommodating shortening on Mercury's surface. Future studies should test if a distinction between the landforms is found in the underlying thrust fault systems.

A fresh-cut papaya freshness prediction model based on partial least squares regression and support vector machine regression

This study investigated the physicochemical and flavor quality changes in fresh-cut papaya that was stored at 4 °C. Multivariate statistical analysis was used to evaluate the freshness of fresh-cut papaya. Aerobic plate counts were selected as a predictor of freshness of fresh-cut papaya, and a prediction model for freshness was established using partial least squares regression (PLSR), and support vector machine regression (SVMR) algorithms. Freshness of fresh-cut papaya could be well distinguished based on physicochemical and flavor quality analyses. The aerobic plate counts, as a predictor of freshness of fresh-cut papaya, significantly correlated with storage time. The SVMR model had a higher prediction accuracy than the PLSR model. Combining flavor quality with multivariate statistical analysis can be effectively used for evaluating the freshness of fresh-cut papaya.

Geological and anthropogenic contributions of metal(loid)s in the artisanal and small-scale mining-impacted Ocoña watershed of Arequipa, Peru

Strategies to resolve conflicts around water supply and quality in mining-impacted communities include a better understanding of the role and mitigation of mining activities proximal to surface water bodies. The Ocoña watershed of Arequipa (Peru) is one such region where there are conflicts between artisanal miners, fishers, and agriculture over local water resources. The aim of this study is to develop a workflow that leverages existing water quality monitoring and geological mapping to identify major processes that adversely impact water quality with a focus on the relative contribution of mining-related activities on the Ocoña watershed. This study integrates a statistical evaluation of water chemistry data, concentration-discharge relationships, and detailed mineralogy with geochemical modeling to identify sources and mechanisms of contaminant contributions to surface waters in the Ocoña watershed. This revealed that primary deposit mineralogy and seasonal variations exert direct effects on water quality in this mining-active watershed. Multivariate statistical analysis reveals two distinct hydrogeochemical signatures in Ocoña River water that likely represent natural geothermal activity in the northern portion and acid mine drainage contribution in the southern portion of the watershed. In both cases, anthropogenic activities such as mining and agricultural may increase contaminant mobilization. Interestingly, despite perceived challenges, water quality in the Ocoña watershed is better than that of neighbors potentially due to pH-controlled precipitation of Fe and resultant sorption of metals onto precipitants. However, copper released from acid mine drainage, and in particular peak concentrations during the high flow season, exert a potential for adverse ecotoxicological effects. This workflow could potentially be extrapolated to other regions with similar available data to better identify the relative contributions of natural and anthropogenic pressures to the release of toxic compounds in watersheds of concern.

Analysis of phytocannabinoids in hemp seeds, sprouts and microgreens

Hemp-sprouts are emerging as a new class of attractive functional food due to their numerous health benefits when compared to other sprout species. Indeed, the high content of beneficial components including polyphenols and flavonoids makes this type of food a promising and successful market. However, the available literature on this topic is limited and often conflicting as regards to the content of phytocannabinoids. High-performance liquid chromatography coupled to high-resolution mass spectrometry (HPLC-HRMS) was applied in an untargeted metabolomics fashion to extracts of hemp seeds, sprouts and microgreens of nine different genotypes. Both unsupervised and supervised multivariate statistical analysis was performed to reveal variety-specific profiles of phytocannabinoids with surprisingly remarkable levels of phytocannabinoids even in chemotype V samples. Furthermore, a targeted HPLC-HRMS analysis was carried out for the quantitative determination of the major phytocannabinoids including CBDA, CBD, CBGA, CBG, CBCA, CBC, THCA, and trans-Δ9-THC. The last part of the study was focused on the evaluation of the enantiomeric composition of CBCA in hemp seeds, sprouts and microgreens in the different varieties by HPLC-CD (HPLC with online circular dichroism). Chiral analysis of CBCA showed a wide variability of its enantiomeric composition in the different varieties, thus contributing to the understanding of the intriguing stereochemical behavior of this compound in an early growth stage. However, further investigation is needed to determine the genetic factors responsible for the low enantiopurity of this compound.

Diagnostic efficacy and clinical impact of image-guided core needle biopsy of suspected vertebral osteomyelitis

ObjectivesThe diagnostic yield and clinical impact of image-guided core needle biopsy (ICNB) of suspected vertebral osteomyelitis in adults is heterogenous in published studies owing to small sample sizes, indicating the need for large cohort studies. MethodsA retrospective analysis of ICNBs was performed from 2010 to 2021 for patients with imaging findings consistent with vertebral osteomyelitis. For each biopsy, a series of factors were analyzed, as well as if histopathology was diagnostic of osteomyelitis and if microbiological cultures were positive. In addition, it was recorded in what way biopsy influenced clinical management regarding antimicrobial treatment. A multivariate statistical analysis was performed to evaluate the factors associated with yield. ResultsA total of 570 biopsies performed on 527 patients were included. A histopathologic diagnosis of osteomyelitis was made in 68.4% (359 of 525) of biopsies, and microbiological cultures were positive in 29.6% (169 of 570). Elevated erythrocyte sedimentation rate was positively associated with a histopathologic diagnosis of osteomyelitis (odds ratio [OR] =1.96, P = 0.007) and positive cultures from bone cores (OR = 1.02, P ≤0.001) and aspirate (OR = 1.02, P ≤0.001). Increased total core length was positively associated with a histopathologic diagnosis of osteomyelitis (OR = 1.81, P = 0.013) and positive cultures from bone cores (OR = 1.65, P = 0.049). Clinical management was affected by ICNB in 37.5% (214 of 570) of cases. ConclusionsIn this large cohort, ICNB yielded approximately 30% positive cultures and changed clinical management in over one-third of the patients.

Chemical Characterization of the Essential Oil Compositions of Mentha spicata and M. longifolia ssp. cyprica from the Mediterranean Basin and Multivariate Statistical Analyses.

This present study aims to characterize the essential oil compositions of the aerial parts of M. spicata L. and endemic M. longifolia ssp. cyprica (Heinr. Braun) Harley by using GC-FID and GC/MS analyses simultaneously. In addition, it aims to perform multivariate statistical analysis by comparing with the existing literature, emphasizing the literature published within the last two decades, conducted on both species growing within the Mediterranean Basin. The major essential oil components of M. spicata were determined as carvone (67.8%) and limonene (10.6%), while the major compounds of M. longifolia ssp. cyprica essential oil were pulegone (64.8%) and 1,8-cineole (10.0%). As a result of statistical analysis, three clades were determined for M. spicata: a carvone-rich chemotype, a carvone/trans-carveol chemotype, and a pulegone/menthone chemotype, with the present study result belonging to the carvone-rich chemotype. Carvone was a primary determinant of chemotype, along with menthone, pulegone, and trans-carveol. In M. longifolia, the primary determinants of chemotype were identified as pulegone and menthone, with three chemotype clades being pulegone-rich, combined menthone/pulegone, and combined menthone/pulegone with caryophyllene enrichment. The primary determinants of chemotype were menthone, pulegone, and caryophyllene. The present study result belongs to pulegone-rich chemotype.

Characterisation of differential aroma markers in roasted coffee powder samples by GC×GC- TOF- MS and multivariate statistical analysis

The composition of volatile organic compounds (VOCs) of roasted coffee powder is important for the influence of coffee flavor. In this study, HS-SPME combined with GC × GC- TOF- MS was used to analyze the VOCs of 7 roasted coffee powder with different bean species, origins and roasting degree. The results of VOCs of different samples were classified into two categories (same origin and roasted degree but different bean species, same bean species and roasted degree but different origins) by PCA and HCA, furthermore through OPLS-DA, differential compounds with VIP values greater than 2 were identified, so 8 common aroma markers with greater influence on coffee powder flavor through validation analysis were identified: furan-2-ylmethanol, 3-methylcyclopentane-1,2-dione, 4-ethyl-2-methoxyphenol, 5-methylfuran-2-carbaldehyde, 2-(methylsulfanylmethyl)furan, 1-methylpyrrole-2-carbaldehyde, 3-methylpentanoic acid, and 4-(furan-2-yl)butan-2-one. And characteristic volatile aroma components were identified for distinguishing between different bean species, that is for Arabica coffee bean: 1-methylpyrrole-2-carbaldehyde, furan-2-ylmethanol and 3-methylpentanoic acid; and for Robusta coffee bean: 4-ethyl-2-methoxyphenol. The results provide further reference for the study of coffee flavor of different bean species and different roasting degrees.

Evaluating performance of miniaturized spectrometers in predicting soil properties using multivariate statistical analysis

Despite the increasing use of portable, low-cost spectrometers in estimating soil properties, there is lack of documentation regarding the factors contributing to the lower performance of these spectrometers when compared to conventional ones. This study investigates potential factors influencing performance of the Nanoquest, a low-cost spectrometer, in estimating soil organic carbon (SOC) and total nitrogen (TN). To conduct the study, five different models (cubist, partial least squares regression, support vector machines, random forest, and generalised boosted models) were tested for the estimation SOC and TN and a fivefold cross-validation analysis was conducted for model hyperparameter optimization. The Nanoquest achieved a Lin’s concordance correlation coefficient (CCC) value of 0.84 and an R2 value of 0.74 for SOC. For TN, CCC values of 0.86 and an R2 value of 0.78 were obtained. To understand the impact of the spectral range and spectral resolution on SOC and TN estimation, the ASD spectra were digitally resampled to match the Nanoquest spectral range and resolution. This resampling resulted in a slight decrease in model performance for the spectral range and a more pronounced decrease for the spectral resolution.

Comprehensive investigation on non-volatile and volatile compounds in Auricularia auricula from different regions by UPLC-MS/MS-based metabolomics and GC-IMS

Auricularia auriculata from two regions of China were studied for their metabolite fingerprints, metabolic pathways, and volatile organic compounds (VOCs) using UPLC-MS/MS and GC-IMS techniques in conjunction with multivariate statistical analysis approaches. A total of 881 metabolites were identified by UPLC-MS/MS, of which 39 compounds displayed significant differences. Acetyl-l-carnitine, N-acetyl-l-alanine, PC (18:3(9Z,12Z,15Z)/0:0), 4-fumaryl-acetoacetate, S-2-hydroxybutanedioic acid, and 4-guanidinobutyric acid belonged to the characteristic non-volatile metabolites. The umami-tasted amino acid, essential amino acid was abundant in Heilongjiang and Shaanxi province, respectively. KEGG pathway analysis indicated that differential metabolites were mainly enriched in lipid and amino acid metabolism. The GC-IMS study revealed 64 different types of VOCs, including acids, alcohols, aldehydes, esters, ketones, and other compounds. A. auricula from Shaanxi province was abundant in 2,3-pentanedione, 1-octen-3-one (dimer), 2-acetylfuran, beta-pinene (monomers, dimers) and alpha-pinene. While the primary distinctive VOCs of A. auricula from Heilongjiang province were butyl acetate, methyl acetate and propyl acetate. PCA and OPLS-DA allowed for clear differentiation between the A. auricula samples from the two regions. This research established foundations for investigating potential biomarkers for A. auricula and has important applications values for the creation and implementation of traceability systems within the edible mushroom industry.

Quality assessment using water quality indicators with geospatial analysis of groundwater quality El-Oued region, Northern Sahara, Algeria

ABSTRACT The present study was carried out to assess groundwater quality in two layers (Albian and complex terminal) intended for irrigation, to identify their original chemical composition and overall risk by different attack techniques in the province of El-Oued. Eleven physico-chemical parameters were measured at 16 locations. Emphasis was placed on the selection of irrigation water quality index (IWQI) inputs through the application of multivariate statistical analysis, the estimation of various classical irrigation indices, and the integration of geographic information systems. The results of the IWQI, sodium adsorption ratio, Kelly index, permeability index, residual sodium carbonate and magnesium hazard showed that the majority of groundwater samples were classified for irrigation purposes in the following categories: severely restricted (100%), low sodium and excellent (100%), good (93.75%), marginally safe (100%), good (100%) and suitable (81.25%), respectively. In addition, the majority of selected samples were classified as “good to eligible for irrigation” based on %Na as shown in the distribution map. Whereas the principal component analysis results show that the four factors together explain 72.74% of the total variance according to the Kaiser rule. Cluster analysis divides the parameters into three groups. In summary, the combination of the geographic system and irrigation quality indices is useful for identifying irrigation zones.

Mass spectrometry-based pseudotargeted metabolomics reveals metabolic variations in a2-induced gastric cancer cell.

Gastric cancer (GC) is one of the most malignant tumors with high morbidity and mortality in the world. Compound a2, a Jiyuan oridonin derivative, exhibited excellent anti-proliferative activity against GC cells. To investigate the gastric cellular response to a2 therapy as a novel drug candidate, we adopted a pseudotargeted metabolomics method to explore metabolic variation in a2-induced MGC-803 gastric cells using liquid chromatography tandem mass spectrometry combined with multivariate statistical analysis. The results showed that a2 treatment induced significant metabolic changes in the levels of aminoacyl-tRNA biosynthesis, alanine, aspartate and glutamate metabolism, pyrimidine metabolism, and tricarboxylic acid cycle, approximately 80% of the metabolites were down-regulated in the low-dose and high-dose groups including aspartate, tryptophan, sedoheptulose 7-phosphate, succinate, 2'-deoxyadenosine, uridine, cytidine, etc. which can provide evidence for a new therapy of GC.

Radiometric and health risk assessment of building materials collected from Tamil Nadu using gamma-ray spectrometry

ABSTRACT The radiometric analysis was carried out on different building materials collected from Tamil Nadu, using gamma-ray spectroscopy with NaI(Tl) detector. The mean activity concentrations of 226Ra (38 Bq kg−1) and 40K (643 Bq kg−1) in all building materials were higher than the world-populated weighted average of 33 Bq kg−1 and 420 Bq kg−1, respectively, while for 232Th (28 Bq kg−1), it was lower than 45 Bq kg−1, as reported by UNSCEAR (2000). These results were compared with similar studies carried out around the world. The calculated radiological parameters, radium equivalent activity (127 ± 11 Bq kg−1), absorbed dose rate (61 ± 5 nGy h−1), annual effective dose equivalent (0.30 ± 0.02 mSv y−1), internal and external hazard index (0.45 ± 0.04 and 0.34 ± 0.03) and activity utilisation index (0.74 ± 0.08), are within the world-populated weighted average and safest limits. Moreover, the radiogenic heat production rate (RHP) due to the natural radionuclides in the building materials was calculated, and the total RHP was 0.94 µWm−3. Multivariate statistical analyses, such as Pearson correlation analysis, hierarchical cluster analysis and factor analysis, were implemented on activity concentration and radiological parameters to evaluate the interrelationship between them.

Morpho-Anatomical Characteristics and Volatile Profiles of Pinus nigra J.F.Arnold from the Balkan Peninsula and Southern Carpathians

This is the first report on morpho-anatomical and phytochemical differentiation of 19 native populations representing different Pinus nigra J.F.Arnold subspecies (banatica (Borbás) Novák, dalmatica (Vis.) Franco, nigra, and pallasiana (Lamb.) Holmboe) in the Balkans and Southern Carpathians. The 9 morpho-anatomical characteristics and 10 headspace volatiles of needles were analyzed with multivariate statistical analyses. The combination of results from all multivariate analyses with both types of markers revealed that P. nigra is differentiated into three groups within the studied area (the Dalmatian coast, Greece, and the rest of the Balkans with the Southern Carpathians). The first group included the population from an island in Dalmatia that corresponds to P. nigra subsp. dalmatica. The third group consisted of populations from continental Croatia, Bosnia and Herzegovina, Serbia, Romania, and Bulgaria, which corresponds to P. nigra subsp. nigra. In light of the recent molecular data that indicated that the Greek populations (the second group) represent a distinct genetic lineage of P. nigra placed between the populations from the principal area (P. nigra subsp. nigra) and Turkey (P. nigra subsp. pallasiana), one can speculate that there is one more subspecies of P. nigra in this region that corresponds to populations from Greece. Extending our analyses to Asia Minor and Crimea could bring additional results that would be valuable for clarifying the intriguing issue of the diversification of P. nigra in the eastern part of its range.

Optimization of ultrasound-assisted extraction conditions for active components and anti-alzheimer activities from mentha cordifolia

Mentha cordifolia .(Lamiaceae) or Mint, is distributed all over the world. Essential oils found are menthol, menthone, 1,8-cineole, pinene, myrcene, and borneol. M. cordifolia. most popular herb, is widely used in cooking, cosmetics, complementary therapy, and pharmaceuticals for anti-carcinogen, gastro protective, antimicrobial, and antivirus purposes. The manufactory extracted essential oil method used steam distillation and hydro-distillation, which decomposed the components. The aim of this work was to study the innovative technologies of chemical constituents’ extraction by ultrasound-assisted extraction (UAE) that include high yields, short-extraction times, and energy savings. The extraction of Mentha cordifolia was carried out at 20 KHz ultrasonic frequencies. Sonicated times were 1.5 h. Antioxidant studies of essential oils by UAE extraction were 2, 2-diphenyl-1-1-picrylhydrazyl (DPPH), 2,2-azino-bis-3-ethylbenzothiazoline-6-sulfonic acid (ABTS), and pharmacological activity was anti-Alzheimer (Ellman’s method). The UAE method used hexane, ethyl acetate, and ethanol as solvents and set the time to 1.0 h; in the second experiment, it set the time to 1.5h in the same system. Crude hexane (F1) extracted gave terpene and essential oils; crude ethyl acetate (F2) extracted gave flavonoids; and crude ethanol (F3) extracted gave phenolic compounds. The UAE method gave fractions F1, F2, and F3, which showed the DPPH activity was IC50 259, 229, and 74 mg/mL; ABTS radical scavenging was IC50 129, 103, and 127 mg/mL; and anti-acetylcholinesterase was IC50 128, 126, and 137mg/mL, respectively. The antioxidants tested were treated by applying multivariate statistical analysis.