Multivariate Statistical Tools Research Articles

Assessment of ecological status of Uppanar and Vellar estuaries through multivariate pollution indices

Multivariate pollution degree indices were utilized to evaluate the environmental condition of the Uppanar and Vellar estuaries. The Trophic Index (TRIX) indicates a state of “moderate eutrophication” with a value of 4.92, while the Trophic State Index (TSI) ranged from 40.3 to 57.2, categorizing the trophic states from “oligotrophic” to “eutrophic”. The Comprehensive Pollution Index (CPI) showed a range of 0.13 to 0.94, classifying pollution levels from “unpolluted” to “slightly polluted”. The study revealed that the Uppanar and Vellar estuaries underwent seasonal variations, transitioning from an oligotrophic state during the post-monsoon and summer periods to a eutrophic state in the pre-monsoon and monsoon seasons. The application of multivariate statistical tools allowed the identification of pollution indicator species to assess the estuarine systems. The insights gained from this study can be valuable for assessing other ecosystems facing similar anthropogenic activities, providing a basis for informed management and conservation strategies.

Portable spectroscopy, digital imaging colorimetry and multivariate statistical tools in contaminant identification: A case study of mint (Mentha) and basil (Ocimum basilicum)

The advent of portable Fourier-Transform Infrared (FTIR) and Raman spectrometers has revolutionized analysis capabilities, presenting the possibility of on-site contaminant identification without the need for specialized laboratory settings. Compared to laboratory instrumentation, portable spectroscopy is more prone to noise, and appropriate spectral processing procedures need to be established. This paper introduces a comprehensive methodology that integrates acquisition techniques, spectral analysis, and mathematical tools necessary for utilizing handheld spectrometers to diagnose plant contamination. It focuses on determining the efficacy of handheld FTIR, Raman spectroscopy, and digital imaging for detecting contaminants in two food plants, Basil (Ocimum basilicum) and Mint (Mentha). The study examines the impact of three pollutants: iron (II) sulphate (FeSO4), zinc (II) sulphate (ZnSO4), and copper (II) sulphate (CuSO4), on these plants, but also the necessary amount of measurements to spot the pollutants' effects. Measurements were conducted at the start, after 24 hours, and after 48 hours of exposure, on both fresh and dried plant leaves, as well as in solution. Spectral effects of each of the pollutants were identified with the use of multivariate statistical process control techniques. With the help of the developed methodologies, researchers can identify in-situ contaminant effects, exposure times and run diagnostics directly on the leaf both in alive and dried plants.

Exploring the Performance of Linear and Nonlinear Models of Time-of-Flight Secondary Ion Mass Spectrometry Spectra.

Multivariate statistical tools and machine learning (ML) techniques can deconvolute hyperspectral data and control the disparity between the number of samples and features in materials science. Nevertheless, the importance of generating sufficient high-quality sample replicates in training data cannot be overlooked, as it fundamentally affects the performance of ML models. Here, we present a quantitative analysis of time-of-flight secondary ion mass spectrometry (ToF-SIMS) spectra of a simple microarray system of two food dyes using partial least-squares (PLS, linear) and random forest (RF, nonlinear) algorithms. This microarray was generated by a high-throughput sample preparation and analysis workflow for fast and efficient acquisition of quality and reproducible spectra via ToF-SIMS. We drew insights from the bias-variance trade-off, investigated the performances of PLS and RF regression models as a function of training data size, and inferred the amount of data needed to construct accurate and reliable regression models. In addition, we found that the spectral concatenation of positive and negative ToF-SIMS spectra improved the model performances. This study provides an empirical basis for future design of high-throughput microarrays and multicomponent systems, for the purpose of analysis with ToF-SIMS and ML.

A comprehensive multivariate investigation of the water quality of Kallada River in Kerala, India

The Kallada River is a remarkable water resource in Kerala and reviving several villages through its glorifying journey. An investigation was directed to evaluate the spatial and temporal status of water quality in Kallada River, Kerala using multivariate statistical tools from February 2019 to January 2021. The status of fifteen environmental variables was detected by using standard methodology. The increased levels of conductivity, total dissolved solids, biochemical oxygen demand, hardness, alkalinity, calcium, and magnesium were detected during the post-monsoon (PoM) season and those parameters were tend to increase towards the downstream stations. Some other variables such as turbidity, dissolved oxygen, NO3−, SiO2, and PO43− were higher during the monsoon (MoN) season. Temperature, pH, and chemical oxygen demand were higher during the pre-monsoon (PrM) season. Furthermore, multivariate statistical tools like principal component analysis (PCA), agglomerative cluster analysis (AHC), and pearsons correlation were used to assess spatio-temporal changes in water quality to reveal pollution. Contrarily, the midstream stations such as K6, K10, and the entire downstream area (K11 to K15) of the Kallada River is under the threats of many anthropogenic activities such as sewage effluents, sand mining and agricultural runoff when compared to the entire upstream stations (K1 to K5), and three midstream stations (K7 to K9). The presence of various organic and inorganic pollutants begins from K6 and drastically rises at K11. Hence, efforts are required to prevent the contamination in the Kallada River.

Towards designer polyolefins: highly tuneable olefin copolymerisation using a single permethylindenyl post-metallocene catalyst.

Using a highly active permethylindenyl-phenoxy (PHENI*) titanium catalyst, high to ultra-high molecular weight ethylene-linear-α-olefin (E/LAO) copolymers are prepared in high yields under mild conditions (2 bar, 30-90 °C). Controllable, efficient, and predictable comonomer enchainment provides access to a continuum of copolymer compositions and a vast range of material properties using a single monomer-agnostic catalyst. Multivariate statistical tools are employed that combine the tuneability of this system with the analytical and predictive power of data-derived models, this enables the targeting of polyolefins with designer properties directly through predictive alteration of reaction conditions.

A statistical method for source approtionment of soil pollution

The present study was conducted to evaluate the source apportionment of soil pollution and its impact on soil quality using multivariate statistical analysis tools. Multivariable statistical techniques such as cluster analysis (CA), principal component analysis (PCA), factor analysis (FA) and correlation offers superior interpretation of complicated data sets to better understand the soil quality. Soil samples were analyzed for physicochemical parameters (pH, temperature, electrical conductivity, organic carbon, moisture content, phosphate, potassium and sulphar) and for some important heavy metals (zinc, iron, manganese and copper). Sampling stations have been classified into three groups using Cluster Analysis (CA) in a convincing way. Results revealed that among all the sampling stations, three significantly different groups: (1) stations having highest pollution sources (HW and MW), (2) Moderate polluted area (SW and IW) & (3) control site (VW) of the study stretch. The PCA generated 2 significant factors having eigenvalues >1 which explain 82.78% of the variance in the dataset. Each soil quality parameter with strong correlation coefficient value was considered to be significant (p<0.01). Therefore, the present study revealed that mining activities, dumping of municipal sewage, disposal of biomedical waste and industrial effluent including solid waste (e.g fly ash) have greater influence on the soil quality. Results also showed that the concentrations of heavy metals (Cu, Mn and Zn) in highly polluted soil are beyond the permissible limit. The pollutant levels were significantly different in the three groups of soil pollution character samples, which were confirmed by ANOVA analysis.

Assessment of potentially hazardous elements in soils of the Boyacá industrial corridor (Colombia) using GIS, multivariate statistical analysis, and geochemical indexes

In the industrial corridor of Boyacá, Colombia, population growth is accompanied by anthropogenic activities such as industrial operations, vehicle exhaust fumes, mining, smelting, atmospheric deposition, and excessive use of chemical products to promote crop growth. These activities are known to have a significant impact on urban and rural soils, contributing significantly to elevated concentrations of potentially hazardous elements in the environment. This industrial corridor is an area of economic and social development that needs to provide reference information that will allow us to know the state of soil quality to preserve and manage the public and geoenvironmental health of this region. Anthropogenic activities have contributed to the accumulation of potentially hazardous elements in the environment, affecting various levels of life and creating risks with economic and social implications. However, igneous activity or detrital deposition also enriches soils and creates geochemical anomalies in specific locations. In these cases, the identification of potentially hazardous elements involves the determination of likely sources of contamination and their relationship to the geological setting. In this study, the concentrations of As, Cd, Pb, Mn, Fe, Zn, Hg, Cu and Ni were determined in eighty-one soil samples from the Boyacá industrial corridor (Colombia). The sequential trend of the concentrations of potentially hazardous elements was as follows: Fe > Mn > Zn > Ni > Cu> Pb > As > Cd > Hg. Furthermore, the application of spatial analysis criteria in GIS software with multivariate statistical tools and geochemical indices allowed the identification of anthropogenic and geogenic sources. Most of the potentially hazardous elements were found in soils exposed to industrial and agricultural activities, except for iron. This element showed low variability in all samples, regardless of the geological formations. Due to the lack of reference values for potentially hazardous elements in Colombia, the concentrations were compared with the environmental standards of the Environmental Protection Agency (EPA) and the Ecuadorian Ministry of Environment, Water and Ecological Transition (MAE). The results demonstrate the complexity of the soil and represent the first exploratory study of potentially hazardous elements in this industrial corridor. These results are the starting point for the establishment of geochemical background lines in Colombia and for inspection policies for areas where productive activities converge.

New interactive functional indicator approach for river health assessment in an Asian temperate river: Comprehensive analysis of water chemistry, physical habitat, land use, and the biological disturbance of invasive alien species

Elucidating the responses of aquatic communities to environmental (e.g., chemical, habitat, and flow regime) and biological disturbances is essential to the maintenance and restoration of healthy ecosystems. This study investigated the interactions between environmental and biological disturbance factors and functional indicators of ecological health in 99 sampling sites along a temperate river. Variance tests and multivariate statistical tools, such as non-metric multidimensional scaling (NMDS) and generalized linear models (GLMs), were employed to evaluate the responses of functional indicator guilds and ecological river health to various in-stream chemical, physical, habitat, and land-use pattern factors along with the biological impact of invasive alien species (IAS). Variance testing revealed significant variation (χ2 > 6.2 and p < 0.05) in common species abundance and ecological functional guilds among spatial groups according to chemical pollution levels, land-use intensity, and land-use patterns. NMDS analysis and GLMs based on trophic, habitat, and tolerance guilds, and the results of the multi-metric index of biological integrity for fish, indicated statistically significant patterns in response to environmental disturbance, particularly chemical degradation associated with nutrient (nitrogen and phosphorus) enrichment and organic pollution, loss of gravel and cobble substrates, and intensified agricultural land use. The spatial pattern of IAS abundance was also significantly correlated with the alteration in fish community structures. This study underscores the often-unnoticed impact of high IAS abundance on river health, and its interactions with chemical and physical degradation resulting from agricultural and urban land uses. In summary, this study highlights the importance of addressing multiple environmental and biological disturbances to maintain and conserve the health of river systems.

Exploring The Genetic Diversity Studies In Little Millet (Panicum Sumatrense) Using Multivariate Tools – Principal Component Analysis And Cluster Analysis

The depletion of biodiversity not only distracts the process of plant development designed for genetic enrichment but also disrupts the ultimate services that the ecosystem offered to humanity. Evaluation of variability is a multidimensional problem. The multivariate statistical tools aids in a comparative evaluation of genetic variability. The availability of access to diverse genetic material is important to be successful in any plant breeding effort. Sixty-three little millet genotypes were evaluated for determining the genetic divergence. Observations were recorded on days to fifty percent flowering, days to maturity, plant height, flag leaf length, flag leaf width, peduncle length, peduncle exertion, length of inflorescence, and grain yield per plant. Analysis of variance imparted significant differences for most of the characters studied. The first three principal components having Eigen value more than one are cumulatively contributing 67.97% to the total variability. PC1 has the contribution from the traits viz., days to fifty percent flowering (0.39), days to maturity (0.39), flag leaf width (0.22), and grain yield (0.33) which accounted for 36.90 % of total variability indicating these traits contributed more to the total variance. Cluster analysis revealed that the little millet genotypes were grouped into four clusters based on hierarchical clustering. Cluster II comprised the highest number of (21) genotypes whereas Cluster III consisted of the lowest number of (10) genotypes. This analysis reveals the presence of wide genetic variance in little millet breeding lines

Potential Toxic-Element Pollution in Surface Water and Its Implications for Aquatic and Human Health: Source–Pathway–Receptor Model

This study focused on assessing the extent of pollution in both flowing and non-flowing surface water within the Matjhabeng mining area of South Africa, with particular emphasis on the substantial impact of gold mine tailings. A comprehensive analysis of physical water-quality attributes, including potentially toxic elements (PTEs), and relevant pollution risk indices was undertaken. To comprehensively elucidate the potential risks to aquatic organisms and human health, a risk assessment framework predicated upon the source–pathway–receptor model was developed. Principal Component Analysis (PCA) was employed as a multivariate statistical tool to discern the potential origins of PTE contamination within the environment. The results substantiate pronounced pollution manifestations within the surface water milieu of the Matjhabeng mining area. Specifically, concentrations of critical PTEs, such as arsenic, cobalt, copper, iron, selenium, and zinc, exhibited transgressions of the regulatory thresholds stipulated by both the South African Department of Water Affairs and Forestry (DWAF) and the Canadian Council of the Ministers of the Environment (CCME). Additionally, concentrations of the aforementioned elements exceeded the stipulated DWAF guidelines for irrigation water usage. Pollution indices, encompassing the Single-Factor Pollution Index and the Nemerow Integrated Pollution Index, discerned moderate contamination stemming from As, while remarkably elevated pollution levels were identified for selenium. PCA elucidated 94.5% of the aggregate variance, revealing cobalt, copper, nickel, and zinc as coalescing within PC1, indicative of a common anthropogenic provenance that is conceivably linked to historical gold mine tailings. PC2 exhibited an aggregation of chromium, iron, and lead, reaffirming this shared anthropogenic etiology. The third PCA component was characterized by selenium, followed by arsenic and magnesium in the fourth. The resultant PTE contamination underscores a profound ecological and public health risk, impacting both the aquatic ecosystems and the local community within the precincts of the Matjhabeng Local Municipality (MLM) area, with consequential amplification of susceptibilities to deleterious health consequences. Urgent and concerted interventions are imperative to ameliorate the emergent decline in surface-water quality within the MLM locale. The adoption of nature-based remediation paradigms holds promise for efficaciously elevating water quality, ameliorating community health, and underpinning the long-term economic viability of the region.

Discrimination of Filter Coffee Extraction Methods of a Medium Roasted Specialty Coffee Based on Volatile Profiles and Sensorial Traits.

An untargeted gas chromatography-mass spectrometry (GC-MS) approach combined with sensory analysis was used to present the effects of different extraction methods (i.e., Pure Brew, V60, AeroPress, and French Press) on specialty graded Coffea arabica from Kenya. Partial Least Square Discriminant analysis and hierarchical clustering were applied as multivariate statistical tools in data analysis. The results showed good discrimination and a clear clustering of the groups of samples based on their volatile profiles. Similarities were found related to the filter material and shape used for the extraction. Samples extracted with paper filters (V60 and AeroPress) resulted in higher percentages of caramel-, and flowery-related compounds, while from metal filter samples (Pure Brew and French Press), more fruity and roasted coffees were obtained. Discriminant analysis allowed the identification of eight compounds with a high VIP (variable important in projection) discriminant value (i.e., >1), with 2-furanmethanol being the main feature in discrimination. Sensorial analyses were carried out through an expert panel test. The main evaluations revealed the French Press system as the lowest-scored sample in all the evaluated parameters, except for acidity, where its score was similar to V60. In conclusion, the data obtained from GC-MS analyses were in line with the sensorial results, confirming that the extraction process plays a fundamental role in the flavor profile of filter coffee beverages.

A Practical Nanoplasmonic SERS Substrate for Differential Diagnosis of Lung Normal and Cancer Cells through Multivariate Statistical Analysis

AbstractLung cancer ranks first for cancer‐related mortalities primarily due to late diagnosis. Though Surface‐Enhanced Raman spectroscopy (SERS) is a popular bioanalytical technique, its direct application to diagnosis is impeded by low data reproducibility. Colloidal nanoparticles suffer from SERS intensity fluctuations due to unavoidable aggregation, and Brownian and diffusion motions in biological samples. The processes for solid‐state SERS substrates are either sophisticated or difficult to reproduce. Herein, we revisit the well‐established thermal evaporation process for the easy and reproducible preparation of silver nanoparticles loaded SERS glass substrates. The static mode of thermal evaporation yielded closely packed and uniformly distributed silver nanoparticles. The properties of these nanoparticles are tuned for the best performance by controlling the thermal evaporation process. And SERS substrate exhibited a reasonably good enhancement factor of ~105 with uniformity and reproducibility &lt;6 % RSD over a large area. It was utilized for label‐free SERS fingerprinting of lung adenocarcinoma cells A549 and normal lung fibroblast cells, WI‐38. The obtained data shows a slight distinction of Raman fingerprints in terms of certain biomolecules like nucleic acids, proteins, and lipids. Further multivariate statistical tools have been utilized which ensures a clear divergence between the cancerous cells and normal cells.

PARAMETRIC STUDY OF A CNC TURNING PROCESS USING DISCRIMINANT ANALYSIS

In the present day manufacturing scenario, computer numerical control (CNC) technology has evolved out as a cost effective process to perform repetitive, difficult and unsafe machining tasks while fulfilling the dynamic requirements of high dimensional accuracy and low surface finish. Adoption of CNC technology would help an organization in achieving enhanced productivity, better product quality and higher flexibility. In this paper, an endeavor is put forward to apply discriminant analysis as a multivariate statistical tool to investigate the effects of speed, feed, depth of cut, nose radius and type of the machining environment of a CNC turning center on surface roughness, tool life, cutting force and power consumption. Simultaneous discrimination analysis develops the corresponding discriminant function for each of the responses taking into account all the input parameters together. On the contrary, step-wise discriminant analysis develops the same functions while considering only those significant input parameters influencing the responses. Higher values of hit ratio and cross-validation percentage prove the application of both the discriminant functions as effective prediction tools for achieving enhanced performance of the considered CNC turning operation.

Determination of heavy metals in vended Indian street foods: Application of advanced multivariate analysis

The consumption of street foods is a popular and convenient option in India. However, street foods are often prepared in unhygienic conditions and are susceptible to contamination by heavy metals (HMs), which can be harmful to human health. Therefore, this study aimed to determine the levels of HMs in Indian street foods and assess the associated health risks. Additionally, we employed multivariate statistical tools to categorize the samples based on their contamination levels. The results revealed high levels of HMs in street foods, except for arsenic (As), which was found to be below the limit of detection (≤10 µg/kg). The estimated daily intake (mg/kg/day) of HMs in street-vended food items ranged from 0.0052 to 0.4244. The target hazard quotient and hazard index values of the analyzed metals in the street-vended food items were all found to be < 1, indicating no significant health threat to consumers. In conclusion, regular monitoring of heavy metal concentrations in street-vended food items is necessary to identify potential health risks and ensure that consumers are not exposed to any carcinogenic hazards.

Unveiling the Elemental Composition and Categorization of Sikkim Hot-Spring Water with Laser-Induced Breakdown Spectroscopy and Advanced Analytical Techniques

The research investigates the elemental composition of hot-spring water in Sikkim, specifically in Yumthang, Tarum, and Reshi, using laser-induced breakdown spectroscopy. This technique involves exciting the sample with a high-energy laser pulse, causing it to emit light that can be analyzed to determine its elemental composition. The results of the study indicate that the hot-spring water in Sikkim is of meteoric origin, meaning it originated from precipitation. To understand the abundance of elements in the hot-spring water samples, the Sulphur/Nitrogen, Chlorine/Nitrogen, and Sulphur/Chlorine ratios were calculated using the ratiometric method. These ratios were then used to classify the samples using univariate and multivariate statistical tools. The box plot was used for the univariate analysis, while the bivariate normal distribution method was used for the multivariate analysis. We have also discussed the limitations of overlapping emission peaks on the classification of the samples. The study found that the abundance of nitrogen in the samples played a significant role in their classification. Principal component analysis was also employed to classify the samples. This technique involves identifying the most significant variables contributing to the variation in the data and using them to classify the samples. Overall, the study provides important insights into the elemental composition of hot-spring water in Sikkim and highlights the usefulness of laser-induced breakdown spectroscopy in analyzing such samples.

Dereplication of Lantana trifolia L. leaves and fruits by UFLC-DAD-(+)-ESI-MS/MS and its antifungal and cytotoxic activities.

Lantana trifolia L. (Verbenaceae) is a shrubby plant. In folk medicine, its leaves are used in the form of infusions and syrups to treat angina, coughs, and colds; they are also applied as tranquilizer. Previous studies have reported the antimicrobial potential of the compounds present in L. trifolia leaves. To report the anti-Candida activities of the fractions obtained from the fruits and leaves of two L. trifolia specimens. The L. trifolia fractions were submitted to UFLC-DAD-(+)-ESI-MS/MS, and the data were analyzed by using multivariate statistical tools (PCA, PLS-DA) and spectral similarity analyses based on molecular networking, which aided dereplication of the bioactive compounds. Additionally, NMR analyses were performed to confirm the chemical structure of some of the major compounds in the fractions. The ethyl acetate fractions presented MIC values lower than 100µg mL-1 against the three Candida strains evaluated herein (C. albicans, C. tropicalis, and C. glabrata). Fractions FrPo AcOEt, FrPe AcOEt, and FrPe nBut had MIC values of 1.46, 2.93, and 2.93µg mL-1 against C. glabrata, respectively. These values resembled the MIC value of amphotericin B, the positive control (0.5-1.0µg mL-1), against this same strain. Cytotoxicity was measured and used to calculate the selectivity index. On the basis of our data, the most active fractions in the antifungal assay were more selective against C. glabrata than against non-infected cells. The analytical approach adopted here allowed us to annotate 29 compounds, nine of which were bioactive (PLS-DA results) and belong to the class of phenolic compounds.

A model based on a multivariate classification for assessing impacts on water quality in a DOCE river watershed after the Fundão tailings dam failure

The main purpose of this study was to build multivariate classification models using water quality monitoring data for the hydrographic basin of the Gualaxo do Norte River, Minas Gerais state, Brazil, which was impacted in 2015 by the rupture of a containment structure for iron ore tailings. A total of 27 points were evaluated, covering areas affected and unaffected by the disaster, with monitoring of chemical, physical, and microbiological variables during the period from July 2016 to June 2017. Multivariate classification techniques were applied to the data, with the aim of developing models to determine when the impacted locations would present characteristics equivalent to those existing prior to the rupture. Classification models constructed using PLS-DA and LDA were able to predict three classes: unaffected main river, affected main river, and tributaries. The first technique was able to clearly differentiate the three classes for the data evaluated, achieving averages corresponding to 90% accuracy. The second method was consistent with the first, identifying the chloride content, conductivity, turbidity, and alkalinity as discriminatory variables, among those monitored, with the relationships among the parameters being coherent with the environmental conditions of the region. The model, with a correct classification rate of 91.67%, enabled identification of the behavior of new samples, using only these easily measured variables. In summary, application of the multivariate statistical tools allowed the development of models capable of providing information about the recovery process of an ecosystem impacted by the greatest environmental disaster to have occurred in Brazil.

Characterization of Nepalese Bread Wheat Landraces Based on Morpho-Phenological and Agronomic Traits

Due to the presence of valuable genes that contribute to a variety of functional traits, landraces kept in Genebank can be extremely important in wheat breeding. A study was conducted based on agro-morphological traits of Nepalese bread wheat landraces to explore genetic diversity among them. Using a replicated rod row design, 200 landraces were evaluated during the winter season of 2018 and 2019 at Khumaltar, Lalitpur, Nepal. The degree of variations among landraces was determined using univariate and multivariate statistical tools. The Shannon-Weaver diversity index (H’) showed a wide range of variations among the studied landraces, ranging from 0.55 to 0.91 in quantitative traits and 0.63 to 0.85 in qualitative traits.Principal component (PC) analysis with an eigenvalue greater than 1 reveals that 68% of the variability for quantitative traits is contributed by the first five principal components whereas 67% of the variability of qualitative traits is governed by the first four principal components. UPGMA (Unweighted pair-groups methods through arithmetic average) clustered 202 landraces into five groups according to quantitative charterers.Identified advantageous adaptive traits through the analysis of variability within the accessions, will be used by breeders for crosses in the breeding or used directly by farmers.

Profiling of the Polyphenol Content of Honey from Different Geographical Origins in the United States.

The presence of phenolic compounds in honey can serve as potential authenticity markers for honey's botanical or geographical origins. The composition and properties of honey can vary greatly depending on the floral and geographical origins. This study focuses on identifying the specific markers that can distinguish honey based on their geographical areas in the United States. The main approach presented in this study to identify the geographic origins of honey involves chemometric methods combined with phenolic compound fingerprinting. Sample clean-up and phenolic compound extraction was carried out using solid phase extraction (SPE). Reversed phase liquid chromatography in combination with tandem mass spectrometry were utilized for the separation of the compounds. The honey physicochemical qualities were predominantly determined via spectrophotometric methods. Multivariate statistical tools such as principal component analysis (PCA), analysis of variance (ANOVA), and partial-least squares discriminant analysis (PLS-DA) were employed as both classification and feature selection tools. Overall, the present study was able to identify the presence of 12 potential markers to differentiate the honey's geographical origins. The total phenolic content ranged from 81.6 to 105.7 mg GAE/100 g corresponding to honey from Colorado and Washington, respectively (GAE: gallic acid equivalents). The regression analysis shows a tendency for the total phenolic content of honey to increase as the color of honey increases. The most important result obtained in this study is the demonstration that the geographical origin of honey plays a critical role in predicting the physical properties and phenolic composition of honey.

Univariate versus multivariate spectrophotometric data analysis of triamterene and xipamide; a quantitative and qualitative greenly profiled comparative study

Triamterene (TRI) and xipamide (XIP) mixture is used as a binary medication of antihypertension which is considered as a major cause of premature death worldwide. The purpose of this research is the quantitative and qualitative analysis of this binary mixture by green univariate and multivariate spectrophotometric methods. Univariate methods were zero order absorption spectra method (D0) and Fourier self-deconvolution (FSD), as TRI was directly determined by D0 at 367.0 nm in the range (2.00–10.00 µg/mL), where XIP show no interference. While XIP was determined by FSD at 261.0 nm in the range (2.00–8.00 µg/mL), where TRI show zero crossing. Multivariate methods were Partial Least Squares, Principal Component Regression, Artificial Neural Networks, and Multivariate Curve Resolution-Alternating Least Squares. A training set of 25 mixtures with different quantities of the tested components was used to construct and evaluate them, 3 latent variables were displayed using an experimental design. A set of 18 synthetic mixtures with concentrations ranging from (3.00–7.00 µg/mL) for TRI and (2.00–6.00 µg/mL) for XIP, were used to construct the calibration models. A collection of seven synthetic mixtures with various quantities was applied to build the validation models. All the proposed approaches quantitative analyses were evaluated using recoveries as a percentage, root mean square error of prediction, and standard error of prediction. Strong multivariate statistical tools were presented by these models, and they were used to analyze the combined dosage form available on the Egyptian market. The proposed techniques were evaluated in accordance with ICH recommendations, where they are capable of overcoming challenges including spectral overlaps and collinearity. When the suggested approaches and the published one were statistically compared, there was no discernible difference between them. The green analytical method index and eco-scale tools were applied for assessment of the established models greenness. The suggested techniques can be used in product testing laboratories for standard pharmaceutical analysis of the substances being studied.