Multivariate Analysis Methods Research Articles

Multivariate statistical methods for analysis of physicochemical and microbiological parameters of well water from the village M’Pody

The pollution of surface water and groundwater is a real public health problem that is currently receiving particular attention throughout the world. The use of water for food or hygiene purposes requires excellent physicochemical and microbiological quality. Well water is used for many purposes by the inhabitants of M’pody, a village in the Anyama district of Côte d’Ivoire. In this village, an epidemic of diarrhoea was detected in January 2020. This epidemic claimed sixty-nine (69) victims. This study aims to evaluate well water quality controls using the methods of principal component analysis (PCA), correspondence factorial analysis (CFA), analysis of variance (ANOVA) and self-organizing map (SOM) algorithm. The parameters studied were, turbidity (Tur), conductivity (Cond), hydrogen potential (pH), temperature (T), nitrate (NO3-\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$NO_{3^{-}}$$\\end{document}), nitrite (NO2-\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$NO_{2^{-}}$$\\end{document}), ammonium (NH4+\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$NH_{4^{+}}$$\\end{document}), phosphates (PO43-\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$PO_{4}^{3^{-}}$$\\end{document}), chlorides (Cl-)\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$(Cl^{-})$$\\end{document}, total hydrotimetric degree (DHT), sulfates (SO42-\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$SO_{4}^{2^{-}}$$\\end{document}), bicarbonate (HCO3-\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$HCO_{3^{-}}$$\\end{document}), total alkalinity contents (TAC), escherichia coli (E.coli), thermotolerant coliforms (CTH) and enterococcus faecalis (E.faecalis). Data were collected from seventy-two (72) wells in the village during four campaigns in 2020. Physicochemical parameters were determined by electrochemical and spectrophotometric methods. Microbiological analysis was carried out using membrane filtration technique. Descriptive statistics revealed that Tur, Cond, pH and T parameters did not meet world health organization (WHO, 2017) standards. However, the parameters NO3-\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$NO_{3^{-}}$$\\end{document}, NO2-\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$NO_{2^{-}}$$\\end{document}, NH4+\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$NH_{4^{+}}$$\\end{document}, PO43-\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$PO_{4}^{3^{-}}$$\\end{document}, Cl-\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$Cl^{-}$$\\end{document}, DHT, TAC, SO42-\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$SO_{4}^{2^{-}}$$\\end{document} and HCO3-\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$HCO_{3^{-}}$$\\end{document} comply with WHO standards. The results of bacteriological analyzes confirm the presence in very significant numbers of indicators of faecal contamination (CTH, E.coli and E.faecalis) in all wells. The logical explanations for faecal pollution would come from the infiltration of septic tanks located near the wells and the run-off of waste water carrying human and animal faecal matter. The diarrhea epidemic would therefore be caused by the consumption of this polluted water. PCA, FCA and hierarchical cluster analysis (HCA) were jointly employed to identify the structure of wells and deduce the principal factors controlling the parameters of these well waters. ANOVA revealed the effect of human-induced activities as the main factors influencing the physicochemical and microbiological parameters of the studied well waters. Further studies should focus on multivariate statistical techniques for effective forecasting and monitoring of emerging pollution for improved water quality.

Characterization of olive (Olea europaea L.) cultivars; colour properties, biochemical contents, antioxidant activity and nutrient contents

AbstractThe olive tree, scientifically known as Olea europaea L., is an evergreen xerophytic tree that defines the natural flora, historical background, and cultural essence of the Mediterranean Basin. This study aimed to establish relationships using multivariate analysis methods between the nutrient content of soil conditions in which olive cultivars grown in Türkiye are cultivated and the nutrient element content, phytochemical contents, antioxidant activity, total chlorophyll amount, and leaf colors of these cultivars. All data sets used in the study were analyzed in 2022 and 2023, and average values were used in the research. According to Tukey's comparison result, it was determined that the soil structure of the garden was homogeneous in terms of nutrients it contained. In nutrient analysis conducted on leaves, in the ‘Çelebi’ cultivar, Al (59.25 mg kg−1), B (6.53 mg kg−1), Cu (48.36 mg kg−1), Fe (69.34 mg kg−1), K (1438.11 mg kg−1), Na (197.12 mg kg−1) nutrients are the highest; in the ‘Gemlik-21’ cultivar, Ca (5485.03 mg kg−1) nutrient is the highest; in the ‘Sarı Haşebi’ cultivar, Mg (928.11 mg kg−1), Mn (19.71 mg kg−1), S (632.77 mg kg−1) nutrients are the highest; and in the ‘Tavşan Yüreği’ cultivar, Ni (1.71 mg kg−1), Zn (9.76 mg kg−1) nutrients are the highest. The L* (49.19), b* (29.43) color values are highest in the ‘Sarı Yaprak’ cultivar, while the a* (28.84) value is highest in the ‘Girit Zeytini’ cultivar. The highest leaf chlorophyll content was determined in the ‘Girit Zeytini’ cultivar (95.57). Total phenolics, total flavonoids, antioxidant capacity were determined to be highest in the ‘Manzanilla’ (151.49 mg GAE/100 g), ‘Edincik Su’ (39.01 mg QE/100 g), ‘Nizip Yağlık’ (91.18%) cultivars, respectively. According to the principal component analysis, the first three principal components accounted for 82% of the total variation. The correlation matrix analysis revealed that high levels of certain minerals in the soil led to an increase in the leaves, resulting in positive correlation, while the opposite was true for negative correlation. According to heat map analysis, mineral elements in the leaf were in the same group, while other data sets were in different groups. The data obtained will shed light on future research on similar topics.

Survival analysis and prognostic model establishment of secondary osteosarcoma: a SEER-based study.

Surgical excision is considered one of the most effective treatments for secondary osteosarcoma (SO). It remains unclear whether the survival of patients with secondary osteosarcoma (SO) could be associated with their surgical willingness. The statistics of the patients diagnosed with SO between 1975 and 2008 were gathered from the surveillance epidemiology and end results (SEER) database. The patients were divided into three subgroups according to their surgical compliance. The authors used the multivariable Logistic regression analysis and cox regression method to reveal the influence of surgical compliance on prognosis and the risk factors of surgical compliance. Additionally, the authors formulated a nomogram model to predict the overall survival (OS) of patients. The concordance index (C-index) was used to evaluate the accuracy and practicability of the above prediction model. Sixty-three (9.2%) of the 688 patients with SO who were recommended for surgical treatment refused to undergo surgery. Lower surgical compliance can be ascribed to an earlier time of diagnosis and refusal of chemotherapy. The lower overall survival (OS) {[hazard ratio (HR)] 1.733, [CI] 1.205-2.494, P value [P]=0.003} of not surgical compliant patients was verified by the multivariate cox regression method, compared with surgical compliant patients. In addition, the discernibility of the nomogram model was proven to be relatively high (C-index=0.748), by which we can calibrate 3-year- and 5-year OS prediction plots to obtain good concordance to the actual situation. Surgical compliance was proved to be an independent prognostic factor in the survival of patients with SO.

Analysis of the Relationship between Micro, Small and Medium Enterprises Sharia Compliance with Increasing Leverage on the Impact of Covid-19 (Case Study of MSMEs in Bandung City, Indonesia)

The purpose of this study was to identify the importance of shari'a compliance in the context of MSMEs, MSME's reputation, access to funding, and the impact on the role of shari'a compliance during increased leverage, to determine whether shari'a compliance is associated with increased leverage in MSMEs. is to analyze the impact discussed. This study takes a descriptive-analytical approach to verificative how Shariah compliance affects MSMEs' ability to utilize resources. Primary data sources and the technique for collecting data in this research uses a questionnaire technique. The analysis used in this research is likert scale analysis, hypothesis testing design using confirmatory factor analysis is a method of one of the multivariate analysis methods part of SEM (Structural Equation Modeling) whose function is to confirm or test the model to be hypothesized. To test the hypothesis model, this research uses the outer model for the measurement model and the inner model for the structural model. The Micro, SME population of Islamic Banking customers is his 156 companies and consists of his sample of 50 respondents using non-probability sampling. The study found a correlation and impact between Shariah compliance and leverage in MSMEs in Bandung. Findings from the study highlight the application of Shariah principles in the business activities of MSMEs, in particular the ban on Riba, transparency and fairness, social and environmental responsibility, and resilience to challenges in applying Shariah elements to their business activities. is shown. Shariah compliance impacts MSMEs' reputation, investor confidence, and access to finance

Chemometrics as a tool for monitoring corrosion degradation of the selected alloys in real conditions

AbstractMonitoring of the corrosion process of alloys in real conditions often results in extensive data, which is characterized by complex interdependence, but by a large degree of mutual deviation. First of all, the large dispersion of the obtained results makes it very difficult to draw accurate conclusions about the real influence of the tested parameters on the corrosion behavior of alloys. On the other hand, in many cases, the high interdependence between the corrosion factors can also greatly burden the analyzed system and thus make it significantly difficult to recognize the main influence. Multivariate analysis, especially the principal component analysis, is becoming increasingly popular in processing of this type of data, due to its ability to recognize and eliminate redundant data. The aim of this study was to examine the possibility of using multivariate analysis methods in the processing of the corrosion test results obtained under real conditions. Based on the obtained results, it can be concluded that used multivariate method in combination with energy dispersive spectrometer analysis can be successfully used to identify the most important corrosion factors (type of corrosion environment, exposure time and technological production processes), as well as their influence on the degradation of the tested TiNi alloys under the given conditions.

Prediction of technological properties of wheat flour by combination of UV-VIS-NIR spectroscopy and multivariate analysis methods

Over the last decades, optical spectroscopy methods that do not require complex sample preparation have been widely used to identify and control the composition of food products. In the present study, the possibility of using UV-VIS-NIR spectroscopy combined with multivariate analysis for grading wheat flour into groups differing in technological properties was analyzed. UV-VIS-NIR spectra contain information on the combination and intensity of absorption bands assigned to functional groups of the composition components and determining the technological properties of wheat flour. The database of UV-VIS-NIR spectra of wheat flour samples differing by technological properties was formed into three groups: the first group — wheat flour samples with good baking properties, the second group — with reduced baking properties, the third group — with low baking properties. The visible range of UV-VIS-NIR diffuse reflectance spectrum was used to calculate the color coordinates in the CIE colorimetric system L*a*b*. The greatest difference among the groups in the color coordinates of the samples was found for the coordinate b*, which is associated with the different content of coloring pigments. The spectra database was used to build a classification model for grading wheat flour into quality groups using a combination of principal component analysis and linear discriminant analysis (PCA-LDA) methods. The achieved results indicate that the classification model built on the training sample is able to distinguish wheat flour spectra by quality groups with an accuracy of 96.49%. The effective- ness of the model is verified using a test set of spectra of wheat flour samples. The present study confirms that the combina- tion of UV-VIS-NIR spectroscopy in conjunction with the PCA-LDA method has significant potential for determining a quality group of wheat flour based on technological properties.

Multivariate logistic regression analysis of risk factors for birth defects: a study from population-based surveillance data

ObjectiveTo explore risk factors for birth defects (including a broad range of specific defects).MethodsData were derived from the Population-based Birth Defects Surveillance System in Hunan Province, China, 2014–2020. The surveillance population included all live births, stillbirths, infant deaths, and legal termination of pregnancy between 28 weeks gestation and 42 days postpartum. The prevalence of birth defects (number of birth defects per 1000 infants) and its 95% confidence interval (CI) were calculated. Multivariate logistic regression analysis (method: Forward, Wald, α = 0.05) and adjusted odds ratios (ORs) were used to identify risk factors for birth defects. We used the presence or absence of birth defects (or specific defects) as the dependent variable, and eight variables (sex, residence, number of births, paternal age, maternal age, number of pregnancies, parity, and maternal household registration) were entered as independent variables in multivariate logistic regression analysis.ResultsOur study included 143,118 infants, and 2984 birth defects were identified, with a prevalence of 20.85% (95%CI: 20.10–21.60). Multivariate logistic regression analyses showed that seven variables (except for parity) were associated with birth defects (or specific defects). There were five factors associated with the overall birth defects. The risk factors included males (OR = 1.49, 95%CI: 1.39–1.61), multiple births (OR = 1.44, 95%CI: 1.18–1.76), paternal age < 20 (OR = 2.20, 95%CI: 1.19–4.09) or 20–24 (OR = 1.66, 95%CI: 1.42–1.94), maternal age 30–34 (OR = 1.16, 95%CI: 1.04–1.29) or > = 35 (OR = 1.56, 95%CI: 1.33–1.81), and maternal non-local household registration (OR = 2.96, 95%CI: 2.39–3.67). Some factors were associated with the specific defects. Males were risk factors for congenital metabolic disorders (OR = 3.86, 95%CI: 3.15–4.72), congenital limb defects (OR = 1.34, 95%CI: 1.14–1.58), and congenital kidney and urinary defects (OR = 2.35, 95%CI: 1.65–3.34). Rural areas were risk factors for congenital metabolic disorders (OR = 1.21, 95%CI: 1.01–1.44). Multiple births were risk factors for congenital heart defects (OR = 2.09, 95%CI: 1.55–2.82), congenital kidney and urinary defects (OR = 2.14, 95%CI: 1.05–4.37), and cleft lip and/or palate (OR = 2.85, 95%CI: 1.32–6.15). Paternal age < 20 was the risk factor for congenital limb defects (OR = 3.27, 95%CI: 1.10–9.71), 20–24 was the risk factor for congenital heart defects (OR = 1.64, 95%CI: 1.24–2.17), congenital metabolic disorders (OR = 1.56, 95%CI: 1.11–2.21), congenital limb defects (OR = 1.61, 95%CI: 1.14–2.29), and congenital ear defects (OR = 2.13, 95%CI: 1.17–3.89). Maternal age < 20 was the risk factor for cleft lip and/or palate (OR = 3.14, 95%CI: 1.24–7.95), 30–34 was the risk factor for congenital limb defects (OR = 1.37, 95%CI: 1.09–1.73), >=35 was the risk factor for congenital heart defects (OR = 1.51, 95%CI: 1.14–1.99), congenital limb defects (OR = 1.98, 95%CI: 1.41–2.78), and congenital ear defects (OR = 1.82, 95%CI: 1.06–3.10). Number of pregnancies = 2 was the risk factor for congenital nervous system defects (OR = 2.27, 95%CI: 1.19–4.32), >=4 was the risk factor for chromosomal abnormalities (OR = 2.03, 95%CI: 1.06–3.88) and congenital nervous system defects (OR = 3.03, 95%CI: 1.23–7.47). Maternal non-local household registration was the risk factor for congenital heart defects (OR = 3.57, 95%CI: 2.54–5.03), congenital metabolic disorders (OR = 1.89, 95%CI: 1.06–3.37), congenital limb defects (OR = 2.94, 95%CI: 1.86–4.66), and congenital ear defects (OR = 3.26, 95%CI: 1.60–6.65).ConclusionIn summary, several risk factors were associated with birth defects (including a broad range of specific defects). One risk factor may be associated with several defects, and one defect may be associated with several risk factors. Future studies should examine the mechanisms. Our findings have significant public health implications as some factors are modifiable or avoidable, such as promoting childbirths at the appropriate age, improving the medical and socio-economic conditions of non-local household registration residents, and devoting more resources to some specific defects in high-risk groups, which may help reducing birth defects in China.

Multivariate data analysis on multisensor measurement for inline process monitoring of adenovirus production in HEK293 cells.

In the era of Biopharma 4.0, process digitalization fundamentally requires accurate and timely monitoring of critical process parameters (CPPs) and quality attributes. Bioreactor systems are equipped with a variety of sensors to ensure process robustness and product quality. However, during the biphasic production of viral vectors or replication-competent viruses for gene and cell therapies and vaccination, current monitoring techniques relying on a single working sensor can be affected by the physiological state change of the cells due to infection/transduction/transfection step required to initiate production. To address this limitation, a multisensor (MS) monitoring system, which includes dual-wavelength fluorescence spectroscopy, dielectric signals, and a set of CPPs, such as oxygen uptake rate and pH control outputs, was employed to monitor the upstream process of adenovirus production in HEK293 cells in bioreactor. This system successfully identified characteristic responses to infection by comparing variations in these signals, and the correlation between signals and target critical variables was analyzed mechanistically and statistically. The predictive performance of several target CPPs using different multivariate data analysis (MVDA) methods on data from a single sensor/source or fused from multiple sensors were compared. An MS regression model can accurately predict viable cell density with a relative root mean squared error (rRMSE) as low as 8.3% regardless of the changes occurring over the infection phase. This is a significant improvement over the 12% rRMSE achieved with models based on a single source. The MS models also provide the best predictions for glucose, glutamine, lactate, and ammonium. These results demonstrate the potential of using MVDA on MS systems as a real-time monitoring approach for biphasic bioproduction processes. Yet, models based solely on the multiplicity and timing of infection outperformed both single-sensor and MS models, emphasizing the need for a deeper mechanistic understanding in virus production prediction.

Contribution of soil sustainable management to improve coffee nutrition and mitigate water scarcity

The soil can be managed to improve plant nutrition and resilience under water scarcity. The objective of this study was to evaluate the contribution of soil conditioners, controlled-release fertilizers, and water availability levels to mitigate water scarcity and improve the nutrition of coffee plants. The experiment was conducted in a greenhouse under a randomized block design with four replicates in a factorial scheme (2 irrigation levels × 2 fertilizer types × 5 soil conditioners) of analysis of variance. The factors under study were two irrigation levels (low—40% of water holding capacity, and high—80% of water holding capacity), fertilizer type (conventional and controlled-release), and soil conditioners (coffee husk, gypsum, water-retaining polymer, organic compost, and control without soil conditioner), The characteristics assessed were soil fertility, soil moisture, and nutrient foliar contents. Multivariate analytical methods were used to interpret the results. Soil moisture, chemical attributes, and leaf nutrient contents were analyzed. There was a similarity in the clusters formed by the controlled-release fertilizer treatments, which contribute to plant nutrition. Even under water restriction, the treatments with coffee husk and organic compost were clustered along with soil moisture, favoring the absorption of nutrients. Soil cover with organic compounds improved soil moisture, even under low water availability, which favored plant fertility and nutrition.

DAMPAK GAYA KEPEMIMPINAN BERORIENTASI PRESTASI DAN IKLIM ORGANISASI TERHADAP KOMITMEN AFEKTIF DAAITV MELALUI KEPUASAN KERJA

This study aims to ascertain the impact of independent factors like achievement-oriented leadership style and organizational climate. Affective commitment is a dependent variable, whereas job satisfaction is an intervening variable. The primary source of data for this quantitative study was an instantaneous Likert scale questionnaire delivered to each research participant. Employees of PT. X Jakarta and Medan make up the research population. A sample of 136 participants was selected using non-probability sampling techniques in conjunction with the purposive sampling approach. This study makes use of multivariate data analysis methods with the statistical model SEM-PLS. The results of the analysis show that there is a positive and significant effect of achievement-oriented leadership style on job satisfaction, affective commitment and affective commitment through job satisfaction. There is also a positive and significant effect of organizational climate on job satisfaction, affective commitment and affective commitment through job satisfaction. And there is a positive and significant effect of job satisfaction on affective commitment. These results provide managerial implications regarding how to increase employee affective commitment by paying attention to the achievement-oriented leadership style of the leaders and the organizational climate formed and increasing employee job satisfaction.

Automatic selection of sites for drilling candidate injection wells

Background: The increasing difficulty in finding sites for drilling injection wells at the later stages of field development by NC “KazMunayGas” JSC, due to infill drilling of the grid of existing wells and uneven reserve production, is a pressing problem today. Developments in geospatial analysis and artificial intelligence have stimulated the search for new approaches to solve this problem.&#x0D; Aim: The research is aimed at developing an innovative approach to automatically identifying the most promising sites for drilling injection wells, based on comprehensive analysis of large volumes of data using advanced algorithms.&#x0D; Materials and methods: The work uses methods for collecting and analyzing production and geological data, uses spatial algorithms for multivariate analysis and data normalization methods, including the adjusted interquartile range to determine outliers.&#x0D; Results: Results are described showing the ranking of cells by drilling potential based on comprehensive analysis, as well as the assignment of unique codes to each cell to improve decision-making accuracy.&#x0D; Conclusion: Directions for further research are noted, including analysis of data inaccuracies, consideration of additional parameters, identification of effective interlayers, application of machine learning methods, and expansion of testing of the approach in other fields.

Assessment of carcinogenic and non-carcinogenic risk of exposure to potentially toxic elements in tea infusions: Determination by ICP-OES and multivariate statistical data analysis

BackgroundThe perennial evergreen tea (Camellia sinensis) plant is one of the most popular nonalcoholic drinks in the world. Fertilizers and industrial, agricultural, and municipal activities are the usual drivers of soil contamination, contaminating tea plants with potentially toxic elements (PTEs). These elements might potentially accumulate to larger amounts in the leaves of plants after being taken up from the soil. Thus, frequent monitoring of these elements is critically important. MethodsThe present study intended to determine PTEs (Al, Cr, Mn, Fe, Co, Ni, Cu, Zn, Cd, and Pb) in both tea leaves and infusions using ICP-OES. Various multivariate data analysis methods such as principal component analysis (PCA) and hierarchical cluster analysis (HCA) were employed to elucidate the potential sources of PTEs contamination, whether from anthropogenic activities or natural origins. Additionally, Pearson's correlation coefficient (PCC) was calculated to assess the relationships between the variables under study. ResultsThe mean contents (mg/L) of all studied elements in tea infusions decreased in order Mn (150.59 ± 1.66) > Fe (11.39 ± 0.99) > Zn (6.62 ± 0.89) > Cu (5.86 ± 0.62) > Co (3.25 ± 0.64) > Ni (1.69 ± 0.23) > Pb (1.08 ± 0.16) > Cr (0.57 ± 0.09) > Cd (0.46 ± 0.09) > Al (0.05 ± 0.008), indicating that Mn exhibits the highest abundance. The mean concentration trend in tea leaf samples mirrored that of infusions, albeit with higher concentrations of PTEs in the former. The tolerable dietary intake (TDI) value for Ni and provisional tolerable monthly intake (PTMI) value for Cd surpassed the standards set by the WHO and EFSA. Calculated hazard index (HI < 1) and cumulative cancer risk (CCR) values suggest negligible exposure risk. ConclusionElevated levels of PTEs in commonly consumed tea products concern the public and regulatory agencies.

Incorporation of hydrogen-producing magnesium into minced beef meat protects the quality attributes and safety of the product during cold storage

The impact of hydrogen (H2) producing magnesium (Mg) incorporation into minced beef meat (MBM) on the quality and safety of the product was investigated. The H2-producing Mg (H2-P-Mg)-incorporated MBMs were vacuumed (VP) and stored at 4 °C for 12 days. Other MBMs were vacuumed and gassed with H2 or N2. At the end of storage, the lowest browning index values were for H2 and H2-P-Mg samples. H2- PMg and VP methods generally decreased the counts of mesophilic and psychrotrophic bacteria and yeast molds and restricted the formation of thiobarbituric acid reactive substances and biogenic amines. Heat mapping, PCA, and multivariate analysis methods confirmed chemical analysis results. The volatile compounds were at their highest levels in the control samples at the end of storage, followed by H2, N2, H2-P-Mg, and VP samples. Using the H2-P-Mg method in MBM preparation could protect the quality characteristics and safety of the product during cold storage.

Prognostic Risks for Tracheostomy in Pierre Robin Sequence: A Cohort From a Tertiary Hospital in Thailand.

Pierre Robin sequence (PRS), characterized by micrognathia, glossoptosis, cleft palate, and obstructed airways, is one of the craniofacial conditions challenging various approaches to managing compromised airways, ranging from conservative measures to invasive airway surgery. This study was conducted to identify predictive risk factors for tracheostomy in the PRS. A retrospective chart review was conducted at a tertiary referral hospital in Thailand. Children diagnosed with PRS from January 2010 to December 2021 were enrolled. Univariate and multivariate analysis methods were used to identify the risk factors. Sixty-five patients with PRS were identified in electronic medical records, but 6 were excluded. Thirteen of the remaining 59 patients underwent tracheostomy. There were no significant differences in sex, preterm gestational age, cleft palate, associated syndromes, chromosome abnormalities, or cardiac or neurological involvement between patients who received tracheostomy and those who did not. However, patients with oropharyngeal dysfunction who received tracheostomy tended to use a nasogastric tube or percutaneous gastrostomy 92.3% of the time (P = 0.043). In addition, patients with abnormal airways from endoscopy were more likely to undergo tracheostomy (odds ratio, 2.17; 95% confidence interval [CI], 1.20-3.90). Interestingly, patients with a sum of Apgar scores at 1 and 5 minutes <15 were more likely to undergo tracheostomy (adjusted odds ratio, 9.91; 95% CI, 1.32-74.52). Furthermore, patients with at least 3 identified comorbidities were more likely to undergo tracheostomy (adjusted odds ratio, 11.34; 95% CI, 1.16-111.15). The need for tracheostomy depends on comorbidities, Apgar scores, and abnormal airway anatomy. Feeding methods can become more complex with tracheostomy. Prognostic risk factors can help guide treatment and counseling for health care workers and caregivers.

A Multi-Scale Convolutional Neural Network Combined with a Portable Near-Infrared Spectrometer for the Rapid, Non-Destructive Identification of Wood Species

The swift and non-destructive classification of wood species holds crucial significance for the utilization and trade of wood resources. Portable near-infrared (NIR) spectrometers have the potential for rapid and non-destructive wood species identification, and while several studies have explored related methodologies, further research on their practical application is needed. To address this research gap, this study proposes a multi-scale convolutional neural network (CNN) combined with a portable NIR spectrometer (wavelengths range: 908 to 1676 nm) for wood species identification. To enhance the capability of directly extracting robust features from NIR spectral data collected by a portable spectrometer, the Gramian angular field (GAF) method is introduced to transform 1-dimensional (1D) NIR spectral data into 2-dimensional (2D) data matrices. Furthermore, a multi-scale CNN model is utilized for direct feature extraction. The representation by 2D matrices, instead of 1D NIR spectral data, aligns with 2D convolutional operations and enables a more robust extraction of discriminative features. In the experimental phase, eight wood species were identified using the proposed method, alongside commonly used multivariate data analysis and machine learning (ML) methods. The StratifiedGroupKFold dataset partitioning approach and five-fold cross-validation were used. Additionally, nine spectral preprocessing methods were compared, and principal component analysis (PCA) was used for feature extraction in the ML method. Evaluation metrics, such as accuracy, precision, and recall, were adopted to assess the performance of the methods. The proposed multi-scale CNN model, in combination with 2D GAF matrices of the 1D spectral data, yielded the most accurate results with a mean accuracy of 97.34% in the five-fold validation. These findings present a new approach for the construction of a rapid, non-destructive, and automatic wood species identification method using a portable NIR spectrometer.

Comparative analysis of the application efficiency of gold nanoparticles-based SERS in four foodborne pathogenic microbes

It has been widely reported that SERS can detect foodborne pathogenic bacteria, but SERS has only been limited to the detection of purified single bacteria. However, there’s few single bacterial infections in the natural state were found at the most time, and rare comparative investigations of different Gram-negative and Gram-positive bacteria by SERS exist. At the present study, we are aiming at the comparative analysis of the detection efficiency toward different foodborne pathogenic microbes by a conventional citrate-reduced gold colloid, including Escherichia coli, Salmonella enteritidis, Staphylococcus aureus and Listeria monocytogenes. The SERS spectra of samples were collected with sterile 0.5% NaCl solution as aggregating compound. First, the stability of each bacterial were detected with gold nanoparticles. Afterward, SERS signals of the four bacteria were compared at different concentrations, and it was found that Gram-positive and Gram-negative bacteria have different minimum sensitivity and detection stability. Next, the mixed bacterial SERS detection was applied to classify and identify the similar Raman spectra. Spectral data were analyzed using multivariate data analysis methods, including principal component analysis (PCA) and hierarchical cluster analysis (HCA). The SERS procedure we proposed, which utilized gold nanoparticles adsorbing on bacteria and multivariate analysis, allowed differentiation of species. The results of this pilot study demonstrate the potential of SERS to rapidly identify Gram-negative and Gram-positive foodborne pathogens.

Rural household resilience to food insecurity in Majang Zone, Southwestern Ethiopia

Background Due to shocks and stressors brought on by both natural and man-made events, households that depend on subsistence agriculture frequently experience uncertainty about guaranteeing their food security. The modern strategy for achieving food security in the face of shocks depends on identifying the variables that affect resilience and working to increase rural households’ capacity to withstand shocks related to food insufficiency. The goal of this study was to evaluate how resilient households were to food insecurity and its determinants. Methods From primary and secondary sources, cross-sectional data of both quantitative and qualitative types were gathered. The data acquired through surveys from 320 households was analyzed using a multivariate analytical method that included principal component analysis (PCA) and OLS regression. To bolster the findings, data from focus group discussions (FGDs) and key informant interviews (KIIs) was substantially utilized. Results According to the findings of the analysis, 40% and 60% of households, respectively, become resilient and non-resilient. All but the stability parameter significantly impacted resilience. Resilience to food insecurity is significantly increased by an increase of 2.128, 1.697, 0.648, 0.363, and 0.158 units in the adoption of agricultural technology, asset ownership, access to essential services, and adaptive capacity, respectively. On the other hand, the stability dimension negatively impacts resilience, with each additional unit lowering households’ capacity for resilience by 0.155 units. Conclusions The study suggests that to reduce both short-term and long-term shocks and stresses of food insecurity and to increase the capacity for resilience, the government’s policies and programs and those of other development partners must focus on building the main components of resilience.

[Qualitative and semiquantitative analyses of the chemical components of the seed coat and kernel of Ziziphi Spinosae Semen].

Ziziphi Spinosae Semen refers to the dried seed of Ziziphus jujuba Mill. var. spinosa (Bunge) Hu ex H. F. Chou. The seed is composed of a reddish brown coat and a yellow kernel. A comparative study was conducted to investigate differences in the chemical composition and their relative contents between the seed coat and kernel of Ziziphi Spinosae Semen. First, the chemical compounds found in the seed coat and kernel were characterized and identified using ultra performance liquid chromatography-quadrupole time-of-flight mass spectrometry (UPLC-Q-TOF/MS). The analytical results tentatively identified 57 chemical compounds based on reference-compound comparison, literature retrieval, and chemical-database (e. g., MassBank) searches; these compounds included 14 triterpenes, 23 flavonoids, 7 alkaloids, 6 carboxylic acids, and 7 other types of compounds. The mass error of the identified compounds was within the mass deviation range of 5×10-6 (5 ppm). Next, two methods of multivariate statistical analysis, namely, principal component analysis (PCA) and orthogonal partial least squares-discriminant analysis (OPLS-DA), were used to compare the differential compounds between the two seed parts. A total of 17 differential compounds were screened out via OPLS-DA based on a variable importance in projection (VIP) value of >5. The results revealed that betulinic acid, betulonic acid, alphitolic acid, and jujuboside Ⅰ mainly existed in the seed coat whereas the 13 other compounds, such as spinosin, jujuboside A, and 6‴-feruloylspinosin, mainly existed in the seed kernel. Therefore, these 17 differential compounds can be used to distinguish between the two seed parts. Finally, a semiquantitative method was established using UPLC and a charged aerosol detector (CAD) with inverse gradient compensation in the mobile phase. Six representative compounds with different types were selected to examine the CAD response consistency: magnoflorine (alkaloid), spinosin (flavone), 6‴-feruloylspinosin (flavone), jujuboside A (triterpenoid saponin), jujuboside B (triterpenoid saponin), and betulinic acid (triterpenoid acid). The results showed that the relative standard deviation (RSD) of the average response factors at different levels of these six compounds was 7.04% and that their response intensities were similar. Moreover, each compound in the fingerprint demonstrated good response consistency, and the peak areas obtained directly reflected the contents of each compound. Based on the semiquantitative fingerprints obtained, betulinic acid and oleic acid were considered the main components of the seed coat. The betulinic acid content in the seed coat was approximately 7 times higher than that in the seed kernel. Spinosin, jujuboside A, linoleic acid, betulinic acid, and oleic acid were the main components of the seed kernel. The spinosin content in the seed kernel was 18 times higher than that in the seed coat. In addition, the jujuboside A content in the seed kernel was 24 times higher than that in the seed coat. The proposed method can accurately determine the main components and compare the relative contents of these components in different seed parts. In summary, this study identified the differences in chemical components between the seed coat and kernel of Ziziphi Spinosae Semen and clarified the main components and their relative contents in these parts. The findings can not only provide a basis for the identification of chemical compounds and quality research on different parts of Ziziphi Spinosae Semen but also promote the development and utilization of this traditional Chinese medicine.

Comprehensive Performance Evaluation of an Unmanned Excavator Based on Signal Stability Extraction

The comprehensive performance of unmanned excavators is crucial for the development and optimization of the field of construction machinery. To effectively improve the unmanned excavator to meet the needs of the market, it is imperative to quantify the evaluation method of the comprehensive performance of unmanned excavators. In this study, an evaluation method combining a fuzzy analytic hierarchy process and multivariate image area analysis method is proposed. Firstly, based on the feature extraction of the signal stability of the unmanned excavators, fifteen evaluation indexes were proposed. Then, the case study is used to obtain the scores corresponding to these indexes. The fuzzy analytic hierarchy process is applied to determine the relative weight of the selected evaluation criteria, in which the uncertain and imprecise judgments of decision makers are converted into fuzzy numbers. At the same time, the braking performance of the three types of unmanned excavators was comprehensively evaluated and ranked using the multivariate image area analysis method as an empirical example. Finally, a weight analysis is performed to check the robustness of the ranking results. The results show that the proposed method is effective and feasible. It provides a reference for the performance improvement and efficiency optimization of unmanned excavators.

Trends in health inequalities in childhood and adolescence in Germany: Results of the HBSC study 2009/10 - 2022.

Many studies have identified health inequalities in childhood and adolescence. However, it is unclear how these have developed in recent years, particularly since the COVID-19 pandemic. Analyses are based on the German data from the international Health Behaviour in School-aged Children (HBSC) study from 2009/10 (n = 5,005), 2013/14 (n = 5,961), 2017/18 (n = 4,347), and 2022 (n = 6,475). A total of 21,788 students aged approximately between 11 and 15 years were included. Socioeconomic status (SES) was assessed using the Family Affluence Scale (FAS). Several health indicators were analysed stratified by gender using bivariate and multivariate analysis methods. In 2022, there are clear socioeconomic inequalities in life satisfaction, self-rated health, fruit and vegetable consumption, and physical activity. These inequalities remained largely constant or increased between 2009/10 and 2022. Between 2017/18 and 2022, no significant changes in inequalities were found. Health inequalities are persistent and reduce the chances of growing up healthy. There is no evidence that inequalities in the analysed outcomes have changed during the pandemic period (between 2017/18 and 2022). Rather, the changes in the health indicators seem to affect all adolescents in a similar way.