Multivariate Statistical Analysis Research Articles

Environmental Management Framework for Road Network Demolition Wastes for Construction Industry of Pakistan

Demolition waste from construction industry, especially from road networks, is one of the most voluminous and harmful categories of waste worldwide; therefore, its proper handling is essential for sustainable waste management for environmental, social, and economic benefits. Prolific and unregulated construction activities, conflicts, and defective works are major reasons. The current work aims to address the issue by presenting a framework for an enhanced understanding of sustainable demolition waste management (DWM). A critical analysis of the literature aided to identify major concerns related to different causes, their impacts, and challenges being faced by the construction industry in such management endeavors. The study adopted questionnaire-based methodology to understand the critical relation among the three variables. The Delphi technique supported by industry professionals and pilot study helped to formulate a realistic questionnaire tool. Using the concept of multivariate statistical analysis, structure equation modeling (SEM) helped to assess the structural relationships between the three variables. The research instrument met the reliability, validity and internal consistency criteria required. Each variable achieved a high effect size, f2, with a value of co-efficient of determination of more than the threshold value of 70%. Thus, this supported the fitness criterion of the SEM-based measurement model. Path coefficients yielded the acceptance of all alternate hypotheses, resulting in a strong positive relationship among the three constructs. Therefore, demolition waste impacts are deemed as an effective mediator when explaining the impact between the other two variables. The developed framework presents a coherent and systematic approach and identifies strategies that could be used to address these issues and lead to DWM, including options available for capacity building and implementation and evaluation for supporting sustainability.

Мultivariate analysis for rapid screening and prediction of solid-state compatibility in pharmaceutical preformulation studies-paving the road for machine learning

Multivariate analysis models were developed to evaluate the results obtained from a compatibility study designed for ibuprofen with a large group of different types of excipients, as a possible approach for rapid screening of the incompatibility between the active pharmaceutical ingredient (API) and excipients. The solid-state characterization of the binary mixtures was performed by Fourier transform infrared spectroscopy (FTIR) and differential scanning calorimetry (DSC). Principal component analysis (PCA) and partial least squares-discriminant analysis (PLS-DA) using SIMCA® software were applied for evaluation of the experimentally obtained results. The optimal PCA model for the FTIR spectra explains 96.2 % of the variations in the dataset with good statistical indicators (R2X = 0.960, Q2 = 0.900), which was also the case for the PCA model for the DSC curves (R2X = 0.981, Q2 = 0.866). The applied PLS-DA models have shown similar behaviour to the PCA. Moreover, the main spectral variations in the FTIR spectra and the thermal events in the DSC data were attributed the highest variable importance for the projection (VIP) scores in the corresponding VIP plots, confirming the model capability for predicting ibuprofen interactions. Furthermore, the prediction power of the optimal models for FTIR and DSC experimental data was evaluated by the root mean square error of prediction (RMSEP) of 0.10 and 0.16, respectively. The obtained results demonstrated the potential of multivariate statistical analysis as a machine learning-based technique for screening and prediction of ibuprofen-excipients solid-state compatibility in the preformulation phase of the pharmaceutical development of dosage forms.

Design, synthesis, antitumor activity and NMR-based metabolomics of novel amino substituted tetracyclic imidazo[4,5-b]pyridine derivatives.

Newly prepared tetracyclic imidazo[4,5-b]pyridine derivatives were synthesized to study their antiproliferative activity against human cancer cells. Additionally, the structure-activity was studied to confirm the impact of the N atom position in pyridine nuclei as well as the chosen amino side chains on antiproliferative activity. Targeted amino substituted regioisomers were prepared by using uncatalyzed amination from corresponding chloro substituted precursors. The most active compounds 6a, 8 and 10 showed improved activity in comparison to standard drug etoposide with IC50 values in a nanomolar range of concentration (0.2 - 0.9 μM). NMR-based metabolomics is a powerful instrument to elucidate activity mechanism of new chemotherapeutics. Multivariate and univariate statistical analysis of metabolic profiles of non-small cell lung cancer cells before and after exposure to 6a revealed significant changes in metabolism of essential amino acids, glycerophospholipids and oxidative defense. Insight into the changes of metabolic pathways that are heavily involved in cell proliferation and survival provide valuable guidelines for more detailed analysis of activity metabolism and possible targets of this class of bioactive compounds.

Multivariate statistical analysis of bioavailability of heavy metals and mineral characterization in selected species of coastal flora

This study presents a thorough investigation into the concentration of heavy metals and mineral composition within four distinct coastal flora species: Cyperus conglomeratus, Halopyrum mucronatum, Sericostem pauciflorum, and Salvadora persica. Employing rigorous statistical methodologies such as Pearson coefficient correlation, principal component analysis (PCA), analysis of variance (ANOVA), and interclass correlation (ICC), we aimed to elucidate the bioavailability of heavy metals, minerals, and relevant physical characteristics. The analysis focused on essential elements including copper (Cu), iron (Fe), manganese (Mn), zinc (Zn), magnesium (Mg2+), calcium (Ca2+), sodium (Na+), potassium (K+), and chloride (Cl–), all of which are known to play pivotal roles in the ecological dynamics of coastal ecosystems. Through PCA, we discerned distinctive patterns within PC1 to PC4, collectively explaining an impressive 99.65% of the variance observed in heavy metal composition across the studied flora species. These results underscore the profound influence of environmental factors on the mineral composition of coastal flora, offering critical insights into the ecological processes shaping these vital ecosystems. Furthermore, significant correlations among mineral contents in H. mucronatum; K+ with content of Na+ (r = 0.989) and Mg2+ (r = 0.984); as revealed by ICC analyses, contributed to a nuanced understanding of variations in electrical conductivity (EC), pH levels, and ash content among the diverse coastal flora species. By shedding light on heavy metal and mineral dynamics in coastal flora, this study not only advances our scientific understanding but also provides a foundation for the development of targeted environmental monitoring and management strategies aimed at promoting the ecological sustainability and resilience of coastal ecosystems in the face of ongoing environmental challenges.

The perceived impacts of short-term rental platforms: Comparing the United States and United Kingdom

Short-term rental platforms such as Airbnb have enjoyed considerable success in recent years. However, critics accuse the platforms of having negative impacts, leading to gentrification, disruption, and increased rent and house prices. While research has investigated actual impacts of short-term rental platforms, we lack systematic, generalizable, and comparative evidence on the perceived impacts of such platforms, especially from a social exchange perspective and on a country level. To address these shortcomings, we conducted a representative survey in the US and UK with a holistic set of perceived impacts. Using social exchange theory (SET) and applying a range of multi-variate statistical analyses, such as exploratory factor analysis, cluster analysis and discriminant analysis, we systematically compare these two contexts. The findings indicate that US residents assess short-term rental platforms more positively than UK residents, especially for recreational, amenities-oriented and economic impacts. Among respondents who have used short-term rental platforms as guests, the perceptions are more alike between the two countries, suggesting a homogenization effect. We discuss the theoretical and practical implications of the results.

A practical, step-by-step, guide to taxonomic comparisons using Procrustes geometric morphometrics and user-friendly software (part A): introduction and preliminary analyses

Taxonomy lays the foundations for the study of biodiversity and its conservation. Procrustean geometric morphometrics (GMM) is a most common technique for the taxonomic assessment of phenotypic population differences. To measure biological variation and detect evolutionarily significant units, GMM is often used on its own, although it is much more powerful with an integrative approach, in combination with molecular, ecological and behavioural data, as well as with meristic morphological traits. GMM is particularly effective in taxonomic research, when applied to 2D images, which are fast and low cost to obtain. Yet, taxonomists who may want to explore the usefulness of GMM are rarely experts in multivariate statistical analyses of size and shape differences. In these twin papers, I aim to provide a detailed step-by-step guideline to taxonomic analysis employing Procrustean GMM in user-friendly software (with tips for R users). In the first part (A) of the study, I will focus on preliminary analyses (mainly, measurement error, outliers and statistical power), which are fundamental for accuracy, but often neglected. I will also use this first paper, and its appendix (Appendix A), to informally introduce, and discuss, general topics in GMM and statistics, that are relevant to taxonomic applications. In the second part (B) of the work, I will move on to the main taxonomic analyses. Thus, I will show how to compare size and shape among groups, but I will also explore allometry and briefly examine differences in variance, as a potential clue to population bottlenecks in peripheral isolates. A large sample of North American marmot mandibles provides the example data (available online, for readers to replicate the study and practice with analyses). However, as this sample is larger than in previous studies and mostly unpublished, it also offers a chance to further explore the patterns of interspecific morphological variation in a group, that has been prominent in mammalian sociobiology, and whose evolutionary divergence is complex and only partially understood.

The impact of nutrition on psycho-affective status in an older Cretan population: a cross-sectional study.

Cognitive and mood status influence both personal and social daily activities, with great impact on life quality, particularly among the elderly population. This cross-sectional study aimed to investigate the psycho-affective status concerning eating habits within an elderly population of the Chania area in Crete, Greece. Cognitive status was assessed in 101 elderly subjects through the Mini-Mental State Examination (MMSE), and mood was evaluated using the Hospital Anxiety and Depression Scale (HADS). Nutritional status was assessed using a validated food frequency questionnaire. Multivariable statistical analysis, after adjustment for age, marital status, education, and comorbidity, highlighted among males a positive association of the MMSE score with vegetable consumption (RR 1.18; 95%CI 1.03‒1.34) and a negative association with potato consumption (RR 0.83; 95%CI 0.72‒0.95). Conversely, among females, no statistically significant association was observed for any food. Further, among males, a protective effect on affective status was identified for chicken meat (RR 0.45; 95%CI 0.27‒0.77), fish (RR 0.41; 95%CI 0.21‒0.82), fruit (RR 0.70; 95%CI 0.52‒0.94), cereals (RR 0.67; 95%CI 0.53‒0.87), and cheese (RR 0.78; 95%CI 0.63‒0.97) consumption. Among females, the adjusted model showed a significant detrimental effect of vegetable consumption (RR 1.33; 95%CI 1.02‒1.73). A predominantly vegetable-based diet-with the notable exception of fruits and legumes-was associated with better cognitive status in males, albeit not in females. A higher intake of fruit, as well as fish, chicken meat, and cheese among males was associated with a better affective status, indicating that adequate protein supply may play a role in maintaining emotional balance.

A study protocol to characterise pathophysiological and molecular markers of rheumatic heart disease and degenerative aortic stenosis using multiparametric cardiovascular imaging and multiomics techniques.

Rheumatic heart disease (RHD), degenerative aortic stenosis (AS), and congenital valve diseases are prevalent in sub-Saharan Africa. Many knowledge gaps remain in understanding disease mechanisms, stratifying phenotypes, and prognostication. Therefore, we aimed to characterise patients through clinical profiling, imaging, histology, and molecular biomarkers to improve our understanding of the pathophysiology, diagnosis, and prognosis of RHD and AS. In this cross-sectional, case-controlled study, we plan to recruit RHD and AS patients and compare them to matched controls. Living participants will undergo clinical assessment, echocardiography, CMR and blood sampling for circulatory biomarker analyses. Tissue samples will be obtained from patients undergoing valve replacement, while healthy tissues will be obtained from cadavers. Immunohistology, proteomics, metabolomics, and transcriptome analyses will be used to analyse circulatory- and tissue-specific biomarkers. Univariate and multivariate statistical analyses will be used for hypothesis testing and identification of important biomarkers. In summary, this study aims to delineate the pathophysiology of RHD and degenerative AS using multiparametric CMR imaging. In addition to discover novel biomarkers and explore the pathomechanisms associated with RHD and AS through high-throughput profiling of the tissue and blood proteome and metabolome and provide a proof of concept of the suitability of using cadaveric tissues as controls for cardiovascular disease studies.

Archaeobotanical evidence supports indigenous cucurbit long-term use in the Mesoamerican Neotropics

The squash family (Cucurbitaceae) contains some of the most important crops cultivated worldwide and has played an important ecological, economic, and cultural role for millennia. In the American tropics, squashes were among the first cultivated crop species, but little is known about how their domestication unfolded. Here, we employ direct radiocarbon dating and morphological analyses of desiccated cucurbit seeds, rinds, and stems from El Gigante Rockshelter in Honduras to reconstruct human practices of selection and cultivation of Lagenaria siceraria, Cucurbita pepo, and Cucurbita moschata. Direct radiocarbon dating indicates that humans started using Lagenaria and wild Cucurbita starting ~ 10,950 calendar years before present (cal B.P.), primarily as watertight vessels and possibly as cooking and drinking containers. A rind directly dated to 11,150–10,765 cal B.P. represents the oldest known bottle gourd in the Americas. Domesticated C. moschata subsequently appeared ~ 4035 cal B.P., followed by domesticated C. pepo ~ 2190 cal B.P. associated with increasing evidence for their use as food crops. Multivariate statistical analysis of seed size and shape show that the archaeological C. pepo assemblage exhibits significant variability, representing at least three varieties: one similar to present-day zucchini, another like present-day vegetable marrow, and a native cultivar without modern analogs. Our archaeobotanical data supports the hypothesis that Indigenous cucurbit use started in the Early Holocene, and that agricultural complexity during the Late Holocene involved selective breeding that encouraged crop diversification.

Mechanism of Jinteng Qingbi granules in the treatment of rheumatoid arthritis using metabolomics analysis.

This study investigated the differential metabolites after rheumatoid arthritis (RA) rats were treated with Jinteng Qingbi granules. Collagen-induced arthritis rats were divided into three groups, namely normal group, model group, and Jinteng Qingbi granules group. Serum compounds were identified, annotated, and classified using metabolomics to explain the physicochemical properties and biological functions. The metabolites were screened using univariate and multivariate statistical analyses. There were differences in serum metabolites between RA and normal rats; Jinteng Qingbi granules improved RA and recovered the metabolite levels to normal. Compared to the normal group, 51 differential ions were screened, and 108 ions were changed in the Jinteng Qingbi granules group compared to the RA model. Eight metabolites were upregulated in the RA model group compared to the normal group, whereas 10 metabolites were downregulated. Treatment with Jinteng Qingbi granules increased the levels of 12 metabolites such as cinnamate and decreased the levels of 16 metabolites such as allamandin in the RA model. Differential ion enrichment was mainly related to the histidine metabolic pathway in amino acid metabolism. Jinteng Qingbi granules resulted in improvements in the RA model, which were mainly associated with lipids and lipid-like molecules, organic acids, and derivatives, providing a new possibility and basis for screening biomarkers for the diagnosis and treatment of RA.

Organs-specific metabolomics and anticholinesterase activity suggests a trade-off between metabolites for therapeutic advantages of Trillium govanianum Wall. ex D. Don

Trillium govanianum is traditionally used to treat innumerable alignments like sexual disorders, cancer, inflammation etc. Mainly rhizomes of T. govanianum have been explored for phytochemical profiling but comprehensive metabolomics of other parts has not been yet deeply investigated. Thus, current study was aimed for organs-specific (roots, rhizomes, rhizomatous buds, stems, leaves, and fruits) phytochemical profiling of T. govanianum via metabolomics approach. Targeted (steroidal saponins and free sugars) and non-targeted metabolomics were performed by UPLC-PDA/ELSD & UHPLC-Q-TOF-IMS. Among steroidal compounds, 20-hydroxyecdysone, pennogenin-3-O-β-chacotrioside, dioscin were found predominantly in all samples while diosgenin was identified only in rhizomes. Further, four free sugars viz. 2-deoxyribose (116.24 ± 1.26 mg/g: leaves), fructose (454.76 ± 12.14 mg/g: rhizomes), glucose (243.21 ± 7.53 mg/g: fruits), and galactose (69.06 ± 2.14 mg/g: fruits) were found significant in respective parts of T. govanianum. Elemental analysis of targeted samples was determined by atomic absorption spectrophotometer. Heavy metals (Cd, Hg, Pd, As) were absent while micro- (Mn, Na, Zn, Cu) and macro- (Ca, Fe, Mg, K) elements were found in all samples. Furthermore, UHPLC-Q-TOF-IMS had identified 103 metabolites based on their mass fragmentation patterns and 839 were tentatively predicted using METLIN database. The multivariate statistical analysis showed organs specific clustering and variance of metabolites. Apart from this, extracts were evaluated for in vitro anticholinesterase activity, and found potentials inhibitors with IC50 values 2.02 ± 0.15 to 27.65 ± 0.89 mg/mL and 3.58 ± 0.12 to 16.81 ± 2.48 mg/mL of acetylcholinesterase (AChE) and butyrylcholinesterase (BChE) enzyme, respectively. Thus, comprehensive metabolomics and anti-cholinesterase activity of different parts of T. govanianum would lay the foundation for improving medicinal importance and health benefits of T. govanianum.

Metabolomics analysis using matrix-assisted diffusion-ordered spectroscopy (DOSY) and its application in acrylamide-induced cardiovascular toxicity

Untargeted NMR-based metabolomics strategies are increasingly applied for metabolite screening in a wide variety of medical conditions. However, the identification of metabolites remains a great challenge in NMR metabolomics research. Here, we used matrix-assisted diffusion-ordered spectroscopy (DOSY) to identify new biomarkers in body fluids. DOSY-based metabolomics can exploit the unique strengths of NMR and, thanks to improvements in its resolution, can be successfully used to address a significantly wider range of biological issues. In this study, a metabolite model consisting of ten common metabolites was employed to optimize the matrix-assisted DOSY experimental parameters. Polyvinylpyrrolidone (PVP), a high-molecular-weight polymer, was found to be the most appropriate matrix with which to accomplish the separation of the mixture components. 1H NMR and PVP-assisted DOSY analyses were performed to characterize the metabolic profile of serum samples from acrylamide (ACR)-treated rats and study the mechanism of ACR-induced cardiovascular toxicity. Biomarker identification by 1H NMR and PVP-assisted DOSY yielded similar results. However, multivariate statistical analysis showed that, compared with 1H NMR, PVP-assisted DOSY achieved a better discrimination of the serum samples obtained from rats treated with different doses of ACR. A total of 11 differential metabolites were identified. An enrichment analysis of the metabolic pathways showed that, in rats treated with a high dose of ACR, the glutamate and glutathione metabolic pathways and the energy metabolism pathway of the tricarboxylic acid cycle were mainly affected.

Characterization of heavy metal contamination in wetland sediments of Bosten lake and evaluation of potential ecological risk, China

In November 2023, twenty-two sediment samples were collected from the Bosten Lake wetland in Xinjiang to determine the concentrations of eight heavy metals: arsenic (As), cadmium (Cd), chromium (Cr), copper (Cu), mercury (Hg), nickel (Ni), lead (Pb), and zinc (Zn). This data was used to assess heavy metal contamination and potential ecological risks in the sediments using the Pollution Load Index (PLI) and the Potential Ecological Risk Index (RI). Additionally, multivariate statistical analysis and Positive Matrix Factorization (PMF) were employed to elucidate potential sources and their contributions to contamination. The following are the main conclusions: (1) average concentrations of Zn, Cd, Cr, Pb, and Hg in the sediments surpassed Xinjiang soil background levels by factors of 1.01, 3.58, 1.32, 1.94, and 1.53, respectively. (2) Sediments demonstrated severe pollution with Cd, slight pollution with Zn, Cr, Pb, and Hg, while Cu and Ni indicated mild pollution and As showed non-polluted levels. The overall PLI average (1.01) suggested slight contamination. (3) The descending order of average single ecological risk values were Cd, Hg, Pb, Cu, Ni, Cr, As, and Zn, with a comprehensive RI averaging at 184.07, signaling a moderate ecological risk. (4) Source apportionment revealed that Zn, Cu, Pb, and Ni were influenced by transportation and household waste emissions, while Cr and As were dictated by natural background levels. Hg predominately originated from fossil fuel combustion and Cd from agricultural activities. (5) Mixed sources accounted for the following percentage contributions to sediment heavy metal content: transportation and domestic waste (30.41%), natural background (25.88%), fossil fuel combustion (22.40%), and agricultural activities (21.31%). With anthropogenic inputs exceeding those of natural origins, it is imperative to prioritize the management of Cd, Hg, and Pb as primary pollutants within the region.

The Potential Anti-psoriatic Effects of Andrographolide: A Comparative Study to Topical Corticosteroids.

Andrographolide (AP), a bioactive anti-inflammatory compound of Sambiloto, inhibits NF-κB, TNF-α, and interleukin IL-6. Nowadays, molecular docking simulation between AP and dexamethasone against NF-κB receptor presented the energy AP higher than dexamethasone. This becomes a potential treatment for psoriasis. This manuscript reported the effectiveness of AP from Sambiloto in treating psoriasis compared to topical steroids. This study conducted TLC analysis of AP content and its metabolite impurities, emulgel formulation, molecular docking, in-silico skin toxicity study, and in-vivo anti-psoriatic activity. This was a combination study of an in-silico study and an in-vivo study. This in-silico study was analyzed through multivariate statistical analysis (PCA) to elucidate the data constellation relationship of andrographolide derivatives with several target proteins. The intervention was performed in seven days. The PASI score, molecular parameters (IL-6, IL-17, VEGF, and TNF-a levels), and histopathological findings were assessed. Molecular docking results revealed andrographolide to exhibit a relatively high binding affinity towards IL-6, NF-kB, and TNF-α which is comparable to the corticosteroids, andrographolide also shares similar residue interaction profile with each of the respective protein's native ligand. In the in-vivo study, we found several parameters statistically significantly different regarding the intervention, including final PASI score (p = 0.017), redness (p = 0.017), scale (p = 0.040), thickness (p = 0.023), total histopathology of psoriasis score (p = 0.037), keratin layer score (p = 0.018). Emulgel AP 0.1% could lower the anti-inflammatory agent, which is vital to psoriasis progression.

An exploratory metabolomic study reveals the Dipsacus asper-Achyranthes bidentate herb pair against osteoarthritis by modulating imbalance in polyunsaturated fatty acids and energy metabolism

Osteoarthritis (OA) is a degenerative joint disease primarily affecting the cartilage. The therapeutic potential of the Dipsacus asper-Achyranthes bidentate herb pair for OA has been acknowledged, yet its precise mechanism remains elusive. In this study, we conducted a comprehensive analysis of metabolomic changes and therapeutic outcomes in osteoarthritic rats, employing a gas chromatography-mass spectrometry-based metabolomics approach in conjunction with histopathological and biochemical assessments. The rats were divided into six groups: control, model, positive control, Dipsacus asper treated, Achyranthes bidentata treated, and herb pair treated groups. Compared to the model group, significant reductions in levels of tumor necrosis factor-α (TNF-α), interleukin-1β (IL-1β), interleukin-6 (IL-6), and iNOS were observed in the treated groups. Multivariate statistical analyses were employed to investigate metabolite profile changes in serum samples and identify potential biomarkers, revealing 45 differential biomarkers, with eighteen validated using standard substances. These analytes exhibited excellent linearity across a wide concentration range (R2>0.9990), with intra- and inter-day precision RSD values below 4.69% and 4.83%, respectively. Recoveries of the eighteen analytes ranged from 93.97% to 106.59%, with RSD values under 5.72%, underscoring the method's reliability. Treatment with the herbal pair effectively restored levels of unsaturated fatty acids such as linoleic acid and arachidonic acid, along with glucogenic amino acids. Additionally, levels of phosphoric acid and citric acid were reversed, indicating restoration of energy metabolism. Collectively, these findings highlight the utility of metabolomic analysis in evaluating therapeutic efficacy and elucidating the underlying molecular mechanisms of herb pairs in OA treatment.

Mapping soil trace metal distribution using remote sensing and multivariate analysis.

Trace metal soil contamination poses significant risks to human health and ecosystems, necessitating thorough investigation and management strategies. Researchers have increasingly utilized advanced techniques like remote sensing (RS), geographic information systems (GIS), geostatistical analysis, and multivariate analysis to address this issue. RS tools play a crucial role in collecting spectral data aiding in the analysis of trace metal distribution in soil. Spectroscopy offers an effective understanding of environmental contamination by analyzing trace metal distribution in soil. The spatial distribution of trace metals in soil has been a key focus of these studies, with factors influencing this distribution identified as soil type, pH levels, organic matter content, land use patterns, and concentrations of trace metals. While progress has been made, further research is needed to fully recognize the potential of integrated geospatial imaging spectroscopy and multivariate statistical analysis for assessing trace metal distribution in soils. Future directions include mapping multivariate results in GIS, identifying specific anthropogenic sources, analyzing temporal trends, and exploring alternative multivariate analysis tools. In conclusion, this review highlights the significance of integrated GIS and multivariate analysis in addressing trace metal contamination in soils, advocating for continued research to enhance assessment and management strategies.

The influence of drying and storage conditions on the volatilome and cannabinoid content of Cannabis sativa L. inflorescences.

The increasing interest in hemp and cannabis poses new questions about the influence of drying and storage conditions on the overall aroma and cannabinoids profile of these products. Cannabis inflorescences are subjected to drying shortly after harvest and then to storage in different containers. These steps may cause a process of rapid deterioration with consequent changes in precious secondary metabolite content, negatively impacting on the product quality and potency. In this context, in this work, the investigation of the effects of freeze vs tray drying and three storage conditions on the preservation of cannabis compounds has been performed. A multi-trait approach, combining both solid-phase microextraction (SPME) two-dimensional gas chromatography coupled to mass spectrometry (SPME-GC × GC-MS) and high-performance liquid chromatography (HPLC), is presented for the first time. This approach has permitted to obtain the detailed characterisation of the whole cannabis matrix in terms of volatile compounds and cannabinoids. Moreover, multivariate statistical analyses were performed on the obtained data, helping to show that freeze drying conditions is useful to preserve cannabinoid content, preventing decarboxylation of acid cannabinoids, but leads to a loss of volatile compounds which are responsible for the cannabis aroma. Furthermore, among storage conditions, storage in glass bottle seems more beneficial for the retention of the initial VOC profile compared to open to air dry tray and closed high-density polyethylene box. However, the glass bottle storage condition causes formation of neutral cannabinoids at the expenses of the highly priced acid forms. This work will contribute to help define optimal storage conditions useful to produce highly valuable and high-quality products.

Differentiation of mezcales from four agave species using FT-MIR and multivariate statistical analysis

Fourier Transform Mid-Infrared (FT-MIR) spectroscopy and multivariate statistical analysis were used to differentiate mezcales elaborated with four agave species. The FT-MIR data matrix was subjected to spectral transformations using first and second derivatives. The Partial Least Squares (PLS)-Discriminant Analysis (DA) with the matrix transformed by the first and second derivative allowed the differentiation of mezcales. While Orthogonal Partial Least Squares-Discriminant Analysis (OPLS-DA) was more robust when it was analyzed with second-derivative data. Pairwise comparisons by OPLS-DA allowed mezcales to be correctly discriminated, mainly between Agave karwinskii and Agave potatorum (Q2 = 0.654 and p – value < 0.01; R2Y = 0.985 and p-value < 0.01) and between Agave angustifolia and Agave karwinskii (Q2 = 0.563 and p-value = 0.01; R2Y = 0.989 and p-value = 0.01). FT-MIR spectrophotometry and the PLS-Regression (PLS-R) were applied to predict the ethanol percentage (% v/v) of mezcales collected in 2022 based on the PLS-R model previously run on samples evaluated in 2021.

Review of soil environment quality in India near coal mining regions: current and future predictions.

Production and utilization of coal are one of the primary routes of accumulation of Toxic Elements (TEs) in the soil. The exploration of trends in the accumulation of TEs is essential to establishing a soil pollution strategy, implementing cost-effective remediation, and early warnings of ecological risks. This study provides a comprehensive review of soil concentrations and future accumulation trends of various TEs (Cr, Ni, Pb, Co, Cu, Cd, Zn, Fe, Mn, and As) in Indian coal mines. The findings revealed that average concentrations of Cr, Mn, Ni, Cu, Zn, Pb, and Co surpass India's natural background soil levels by factors of 2, 4.05, 5.32, 1.77, 9.6, and 6.15, respectively. Geo-accumulation index values revealed that 27.3%, 14.3%, and 7.7% of coal mines are heavily polluted by Ni, Co, and Cu, respectively. Also, the Potential Ecological Risk Index indicates that Cd and Ni are primary contaminants in coal mines. Besides, the health risk assessment reveals oral ingestion as the main exposure route for soil TMs. Children exhibit a higher hazard index than adults, with Pb and Cr being major contributors to their non-carcinogenic risk. In addition, carcinogenic risks exist for females and children, with Cr and Cu as primary contributors. Multivariate statistical analysis revealed that TEs (except Cd) accumulated in the soil from anthropogenic sources. The assessment of future accumulation trends in soil TE concentrations reveals dynamic increases that significantly impact both the ecology and humans at elevated levels. This study signifies a substantial improvement in soil quality and risk management in mining regions.

Analysis of characteristic aromas of buckwheat with different germplasm using gas chromatography–mass spectrometry combined with chemometrics and multivariate statistical analysis

In this study, the volatile components in 40 samples of Tartary buckwheat and common buckwheat from 6 major producing areas in China were analyzed. A total of 77 volatile substances were identified, among which aldehydes and hydrocarbons were the main volatile components. Odor activity value analysis revealed 26 aromatic compounds, with aldehydes making a significant contribution to the aroma of buckwheat. Seven key compounds that could be used to distinguish Tartary buckwheat from common buckwheat were identified. The orthogonal partial least squares-discriminant analysis was effectively used to classify Tartary buckwheat and common buckwheat from different producing areas. This study provides valuable information for evaluating buckwheat quality, breeding high-quality varieties, and enhancing rational resource development.