Multivariate Statistical 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.

Metabolomic differences between non-hydrothermal treated water-soluble (WSPs) and hydrothermally treated water-insoluble (WIPs) Maitake polysaccharides fermented by Lactobacillus acidophilus and L. plantarum

Bacterial Metabolite through a fermentation process is a growing trend and a promising alternative for use as functional components. Non-hydrothermal water-soluble (WSPs) and hydrothermally treated water-insoluble (WIPs) Maitake polysaccharides were fermented with Lactobacillus acidophilus (LA) and Lactobacillus plantarum (LP). Chemical composition analysis indicated that Maitake polysaccharides contained 58.22 ± 1.35 % total sugar and 31.46 % β-glucan, essential for metabolites production. 6-glucanase was used to degrade the WIPs, and hydrothermally treated WIP fibers exhibited smooth microstructure. Hence, the LA and LP bacteria investigated the potential fermented metabolic activities and differences between WSPs(Sp1)and WIP(Sp3) Maitake polysaccharides using LC-MS, and 887 metabolites were identified. Using Venn, Partial least squares discriminant analysis (PLS-DA), VIP Metabolites, and other multivariate statistical analysis methods, metabolites were expressed differently in all samples. Due to hydrothermal processing, WIP induced the highest growth of LA and LP, with an abundance of isocitrate metabolites. Furthermore, 50 metabolite correlations were identified, leading to the classification of 6 distinct metabolic groups. Thus, the study offers the initial comprehensive analysis of metabolites in Lactobacillus-fermented Maitake polysaccharides, aiding in understanding its metabolic interactions and facilitating progress in food engineering research.

On the Estimation of the Interannual Variability of the Ocean Surface Temperature in the Area of the Peruvian Upwelling

The interannual variability of the ocean surface temperature in the area of the Peruvian upwelling for the period 1980–2022 is considered according to the satellite archive GODAS (Global Ocean Data Assimilation System) using the methods of multivariate statistical analysis. Local foci of significant trends, for average annual Sea Surface Temperature (SST) values, were identified near the Peruvian offshore. Four regions (clusters) were obtained, which describe the variability of SST in front off Peru, which could be used to pretend to develop a prognostic oceanographic model. Furthermore, coincidences of temperature fluctuations were found between the first cluster and the region N3+4

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.

Multi-scale representation of surface-enhanced Raman, spectroscopy data for deep learning-based liver, cancer detection

The early detection of liver cancer greatly improves survival rates and allows for less invasive treatment options. As a non-invasive optical detection technique, Surface-Enhanced Raman Spectroscopy (SERS) has shown significant potential in early cancer detection, providing multiple advantages over conventional methods. The majority of existing cancer detection methods utilize multivariate statistical analysis to categorize SERS data. However, these methods are plagued by issues such as information loss during dimensionality reduction and inadequate ability to handle nonlinear relationships within the data. To overcome these problems, we first use wavelet transform with its multi-scale analysis capability to extract multi-scale features from SERS data while minimizing information loss compared to traditional methods. Moreover, deep learning is employed for classification, leveraging its strong nonlinear processing capability to enhance accuracy. In addition, the chosen neural network incorporates a data augmentation method, thereby enriching our training dataset and mitigating the risk of overfitting. Moreover, we acknowledge the significance of selecting the appropriate wavelet basis functions in SERS data processing, prompting us to choose six specific ones for comparison. We employ SERS data from serum samples obtained from both liver cancer patients and healthy volunteers to train and test our classification model, enabling us to assess its performance. Our experimental results demonstrate that our method achieved outstanding and healthy volunteers to train and test our classification model, enabling us to assess its performance. Our experimental results demonstrate that our method achieved outstanding performance, surpassing the majority of multivariate statistical analysis and traditional machine learning classification methods, with an accuracy of 99.38 %, a sensitivity of 99.8 %, and a specificity of 97.0 %. These results indicate that the combination of SERS, wavelet transform, and deep learning has the potential to function as a non-invasive tool for the rapid detection of liver cancer.

Экономико-математическое обеспечение регионального и отраслевого стратегирования

A development strategy, be it for an enterprise, industry, region, or country, needs to be supplemented with economic and mathematical methods and models. Strategizing uses econometric models of various types, including panel data models, vector autoregressions, methods of multivariate statistical analysis, structural models based on the input-output methodology, and agent-based models. In this study, stages and the categories of “forecast” and “strategy” were interpreted in accordance with the theory of strategy and the methodology of strategizing developed by Professor V.L. Kvint. By studying the methods for forecasting and modeling socio-economic objects, strategists select the necessary tools in line with the strategizing objectives. Quantitative methods and tools have their own application characteristics, depending on whether they are used for forecasting or strategizing. The strategizing process is divided into three stages: analysis, strategizing, and evaluation. Each stage has a specific range of problems, as well as economic and mathematical methods. The author reviewed scientific literature that described the practical experience in applying various methods at each stage. The review revealed no generally accepted classifications and recommendations for mathematical methods to be used to address a certain type of problem or at a specific stage of strategizing. The practice of using economic and mathematical tools in strategizing is just emerging. However, the quality of statistical data is often low, and long-time sets of indicators cannot be compiled with a unified methodology. In general, the use of economic and mathematical models in strategizing requires knowledge of theory, practical experience, and intuition.

Incipient fault detection based on ensemble learning and distribution dissimilarity analysis in multi-feature processes

Timely and accurate detection of incipient faults has attracted considerable attention and research interest in recent years, due to its potential for the prevention of serious safety incidents and for supporting preventive maintenance. However, most existing methods use single detection model, neglecting the coexistence of multiple features and the local data distribution information found in industrial scenes. To overcome this problem, an incipient fault detection method named multiple model ensemble and distribution dissimilarity analysis (MME-DISSIM) is proposed. First, various multivariate statistical analysis methods are employed as basic detectors to comprehensively capture the feature information hidden in the process data. Second, DISSIM analysis is performed to evaluate the dissimilarity between the current sliding window and each training subset. This evaluation allows for the calculation of weighting factors for each basic detector, which helps to preserve the local distribution information of the current sliding window. Third, ensemble learning is utilized to integrate the statistics from all basic detectors into two detection indices to determine the operation status of the system. In addition, two measurement metrics are defined to quantitatively analyze the fault level of incipient faults. Finally, several experiments on a numerical case, Tennessee Eastman process, and actual PROcess NeTwork Optimization are presented to verify the efficacy and superiority of the proposed method.

Volatile-compound fingerprinting and discrimination of positional isomers in stamp-pad ink tracing using HS-GC-IMS combined with multivariate statistical analysis.

The rising crime rate associated with document forgery has a significant impact on public safety and social stability. In document fraud cases, determining the origin of a particular stamp-pad ink is the most important objective. In this study, a comprehensive analysis of the volatile compounds in quick-drying stamp-pad inks from six commonly used brands were performed for the first time, utilizing a combination of headspace-gas chromatography-ion mobility spectrometry (HS-GC-IMS) and multivariate statistical analysis methods. Visual and comparative analysis of the differential volatile components among different stamp-pad ink samples was conducted using fingerprints and volcano plots. A total of 127 volatile compounds were accurately identified, with ketones, esters, alcohols, and aldehydes being the most abundant compounds in the stamp-pad inks. Hierarchical clustering analysis (HCA), including dendrograms and clustering heatmaps, was utilized to explore the correlations between these compounds and the samples. Additionally, the precise identification of positional isomers and functional group isomers of aliphatic compounds was achieved. To achieve accurate discrimination of various stamp-pad ink samples, a multivariate statistical analysis method was utilized to establish a classification model for them. Based on the results obtained from HS-GC-IMS, effective discrimination among different brands of stamp-pad ink samples was achieved through principal component analysis (PCA) and orthogonal partial least squares discriminant analysis (OPLS-DA). The model exhibited excellent performance, with the fit index of dependent variables (R2Y) and the predictive index of the model (Q2) values of 0.99 and 0.984, respectively. These results provided significant theoretical evidence for the application of HS-GC-IMS as an efficient technique in the analysis of volatile compounds, identification of positional isomers and functional group isomers, as well as tracing the origin of stamp-pad ink and analyzing the formation time of documents.

Construction of Physical Education Teaching Reform Innovation Guarantee System Based on Multivariate Statistical Analysis

Abstract To promote the innovative reform of physical education teaching, this paper adopts the multivariate statistical analysis method to construct a set of multivariate evaluation system. A comprehensive assessment and classification of teaching quality was realized through principal component analysis, flower-pollinated cluster analysis and robust factor analysis model. The teaching quality assessment system of the physical education option course designed in this study includes four primary indicators and 13 secondary indicators. The results showed significant differences in teaching quality among teachers, and the three retained principal factors could reflect 78.15241% of the student performance information, which provided a vital quality control reference for physical education teaching.

Determination of phenolic compounds in water using a multivariate statistical analysis method combined with three-dimensional fluorescence spectroscopy.

Phenolic compounds are toxic chemical pollutants present in water. Three-dimensional fluorescence spectroscopy analysis is an effective and rapid method for real-time phenol monitoring in aquatic environments. However, similar chemical structures of phenols result in highly overlapping three-dimensional fluorescence spectra. Therefore, it is extremely difficult to analyze and quantify the concentration of components in a mixture system that includes two or more phenolic compounds. In this article, we study the mixed phenol system containing phenol, o-cresol, p-cresol, m-cresol, catechol, and resorcinol combined with excitation-emission matrix (EEM) fluorescence data. A multivariate statistical method called best linear unbiased prediction (BLUP) is proposed to analyze the spectra with the aim to achieve quantitative results and a trilinear decomposition algorithm called parallel factor analysis (PARAFAC) was used for comparison. Two experiments with different calibration samples were set to validate the effectiveness of BLUP through recovery, ARecovery (Average Recovery), AREP (Average Relative Error of Prediction), and RMSE (Root Mean Square Error). Overall, the average recovery of each component in experiment 1 and experiment 2 ranged from 95.91% to 111.62% and 82.91% to 129.02%, respectively. Based on the results of the experiments, the concentration of phenolic compounds in water can be quantitatively determined by combining three-dimensional fluorescence spectroscopy with the BLUP method.

ANALISIS MULTIVARIAT UNTUK PEMETAAN KARAKTERISTIK MUTU PENDIDIKAN SEKOLAH MENENGAH KEJURUAN (SMK) DI INDONESIA

Evaluation of the education quality in Indonesia, especially in Vocational High Schools (VHS), carried out through the Presidential Instruction 2016. The implementation of these instructions is not easy because of the different characteristics of each region both geographically and socio-culturally. This study aims to analyze related to the mapping of the characteristics of the quality of vocational education from 34 provinces in Indonesia. The methods used are correspondence analysis, biplot analysis, and cluster analysis which are multivariate statistical analysis methods. The correspondence analysis results show that there are differences in the characteristics of the quality of vocational education from each province based on school accreditation. The biplot analysis show that Teacher Quality is greatest diversity variable compared to other variables. In addition, the provinces of Aceh, East Nusa Tenggara, West Sulawesi and Central Kalimantan have a quality score below the average and are dominated by non-accredited VHSs based on the results of the analysis. The k-means cluster analysis with 4 optimal clusters gives the result that cluster 2 is the cluster with the best quality value while cluster 4 is the cluster with the lowest quality value. Three analytical concluded that provinces with very good quality scores such as DKI Jakarta, Bali and DI Yogyakarta can be used as pilot provinces for other provinces. Meanwhile, provinces with VHS quality scores below the provincial average as a whole should receive attention in order to further improve the quality of their VHSs

Quality markers of Guchang Zhixie pills based on multicomponent qualitative and quantitative analysis combined with network pharmacology and chemometric analysis

Traditional Chinese medicine Guchang Zhixie pills(GCZX) is one of the famous varieties of "Qin medicine" that has been extensively applied to treating irritable bowel syndrome(IBS). However, despite the acknowledged clinical advantages of GCZX there are significant constraints on its quality control and evaluation. The present study utilized UHPLC-Q-Exactive-Orbitrap-MS to analyze the chemical composition of GCZX. Additionally, network pharmacology approaches were utilized to explore the underlying mechanism by which blood components exert therapeutic effects in the treatment of IBS. Furthermore, the GCZX samples were evaluated for their quality on the basis of the qualitative results obtained from 25 batches of GCZX samples using fingerprinting; subsequently, multivariate statistical analysis methods were employed for further analysis. The results indicated the presence of 198 individual components. Among them, 17 prototype compounds were detected in the serum of rats that were administered with GCZX. The potential therapeutic mechanism of GCZX in the treatment of IBS may be associated with the modulation of the neurological system, the immunological system, and the inflammatory response. Moreover, a total of seven prominent peaks were identified after fingerprint analysis. The range of fingerprint similarity among the 25 batches of samples varied from 0.843 to 1.000. The application of chemometrics analysis successfully facilitated the categorical classification of 25 batches of GCZX into three distinct groups. Seven components hold significant importance and should be duly considered during the quality control process of GCZX. The present study can establish the Q-Markers of GCZX for IBS, thereby providing a foundation for investigating the theoretical underpinnings and elucidating the mechanisms underlying the therapeutic effects of GCZX in the treatment of IBS.

Research on Digital Construction and Design of Minority Clothing Based on Multivariate Statistical Analysis

Abstract Digitizing minority costumes is a modern way to protect non-heritage. In this paper, after analyzing the Hu invariant moment features and HOG features, we propose a feature extraction method for ethnic minority costumes that integrates the fusion features of HOG features and Hu invariant moment features. On this basis, the structure of the extracted multivariate feature data is analyzed, and the KPCA method of multivariate statistical analysis is adopted to process the multivariate data of ethnic minority costumes and digitally construct and design the ethnic minority costumes. In addition, experiments were conducted to explore the effect of minority costumes on feature extraction and the effect of digitization construction and design. The results show that the extraction accuracy of six ethnic costumes is stable at 0.75, 0.45, 0.50, 0.75, 0.72, and 0.68, respectively, and its Top-10 accuracy reaches 0.61 at the highest level, while the accuracy of retrieving only some of them is less than 0.40, and the effects of constructed ethnic costume outlines are all around 0.9. The output results of ethnic dresses constructed based on this research are roughly similar to the original ethnic dresses, which is conducive to the inheritance of ethnic dress culture.

The effectiveness of improving plant selection in obtaining potato minitubers

The results of improving selection of the early ripeness potato plants varieties - the Red Scarlett, the Latona, the Impala in the original seed production of the culture are presented. During two years of research, visual evaluation of the best progenies by the number and weight of tubers correspondes to the results of their selection using cluster and step-by-step discriminant analyses by 35.3%. 50.0. and 62.5%. In the studied varieties, improving selection based on visual diagnostics contributes to obtaining more fruitful progenies with the average tubers weight of 73.5-147.1 g and a large fraction tubers with 67.7-103.7 g per plant. Additionally, the above mentioned methods of multivariate statistical analysis made it possible to identify the progenies of the Red Scarlett and the Impala varieties with increased weights of medium (105.9 - 155.4 g) and large (93.9 - 136.2 g) fractions, and the Latona varieties - of all the three fractions (96, 0 - 165.0 g). These progenies have a potential for more efficient management of the original potato seed production.

Study on the ancient glass composition based on the multivariate statistical analysis method

The Silk Road was a channel of cultural exchange between China and the West, and glass was the precious material evidence of early trade. The study of the relationship between the weathering of ancient glass products and glass types and chemical composition contributes to the preservation of ancient cultural relics and promotes the further study of the history of the Silk Road. Based on this, this paper analyzed the surface weathering of cultural relics, and the type, ornamentation, color, and chemical composition of cultural relics, and it was finally found that there is a significant correlation between glass type and surface weathering. Then, discriminant analysis and systematic cluster analysis were applied to establish the classification function for the glass type variables and the chemical composition variables. Finally, gray correlation analysis was used to explore the correlation between each chemical component, and found that SiO2-Al2O3 was the most associated with each other in lead-barium glass, and K2O-CaO was the highest in high potassium glass.

Research on the enhancement path of talent cultivation in the integration of dual-creation education and professional education for teacher training majors in colleges and universities based on multivariate statistical analysis

Abstract In today’s society, under the demand for high-quality talents, the professionals cultivated in colleges and universities should not only master professional skills but also need to have the spirit of innovation and entrepreneurial ability in professional aspects. In this paper, firstly, the multivariate statistical analysis method is explained, and the basic principles of the factor analysis model, principal component analysis method and systematic clustering method are given to analyze the data in the following article. Secondly, starting from the necessity of the integration of bi-initiative education and professional education of teacher training majors in colleges and universities, we constructed the 4344 specialized and innovative integrated talent cultivation model of teacher training majors and constructed an evaluation system to evaluate the effectiveness. Finally, based on the evaluation system, the data analysis of the specialized and creative integrated talent cultivation model was carried out using factor analysis, principal component analysis and systematic clustering method. The results show that in the factor analysis, the highest loading value of the first principal factor is 0.917, the contribution rate of the first principal factor is 39.67%, and the loading value of the principal component factor reaches the highest value of 0.925. The clustering analysis is based on the results of the factor analysis, and the respondents are divided into 4 clusters. The number of people in the 2nd category is more than 30 people, which accounts for about 31.41% of the total number of people. The method of multivariate statistical analysis can be used to analyze the data effectively for the specialized and integrated personnel training mode of teacher training in colleges and universities and also gives the path of specialized and integrated personnel training for teacher training.

Spatial characteristics and controlling indicators of major hydrochemical ions in rivers within coal-grain composite areas via multivariate statistical and isotope analysis methods

The spatial variability of river hydrochemistry in coal-grain composite areas is influenced by natural processes and human activities. However, there is little information thus far on the hydrochemical characteristics of river water and the spatial variability in coal–grain composite areas under the influence of multiple superimposed effects. In this study, river water samples (n = 31) were collected in a coal-grain composite area in the northern plain of Anhui Province (China). The main hydrochemical ions and stable isotopes (δ2H-H2O, δ18O-H2O, δ34S-SO42- and δ18O-SO42-) were then investigated. The objective was to analyze the spatial characteristics and controlling indicators of the main hydrochemical ions via multivariate statistical and isotope analysis methods. The values of δ34S-SO42- and δ18O-SO42- in river water ranged from 7.5 ‰ to 17.5 ‰ and from 4.4 ‰ to 10.7 ‰, respectively. According to the clustering analysis method, the river water samples were divided into two groups. Bayesian mixed model analysis indicated that the sources of SO42- in river water mainly included soil sulfate and mine drainage, with contribution proportions of 74.0 ± 3.1 % and 18.4 ± 4.1 % in River Group I, respectively, and 77.2 ± 3.0 % and 11.1 ± 5.1 % in River Group II, respectively. These results suggested that the controlling indicators of the spatial distribution characteristics of river hydrochemistry were natural processes (water–rock interactions) and human activities (coal mining, manure release and sewage discharge). The results could provide valuable information for regional water resource management, development, and similar coal–grain composite areas elsewhere.

Multivariate Statistical Analysis for Assessment of Groundwater Quality around Dumpsites in Bauchi metropolis, Bauchi State, Nigeria

Groundwater is an important source of drinking water and the protection of groundwater is a major environmental issue as the importance of water quality to human health. Hence, the objective of this study was to assess the groundwater quality around dumpsites in Bauchi metropolis, Bauchi State, Nigeria using standard analytical techniques and multivariate statistical analysis methods such as correlation analysis (CA), principal component analysis (PCA), and hierarchical cluster analysis (HCA). The mean concentration of the parameters in ground water samples ranged from pH 7.55 to 9.06, temperature 32.55 to 34.25 oC, conductivity 54.80 to 65.38 μS/cm, salinity 0.20 to 0.29 mg/L, TSS 0.36 to 1.93 mg/L, TDS 57.53 to 59.95 mg/L, HCO3 26.80 to 31.00 mg/L, PO43- 1.05 to 2.06 mg/L, SO42- 2.25 to 3.26 mg/L, NO3- 1.40 to 4.50 mg/L, NO2- 0.01 to 0.03 mg/L, and Cl- 2.14 to 8.31 mg/L. The result shows that the investigated parameters were below the WHO, permissible limit, except PO43-. The concentration of the parameters were in following trend; TDS > conductivity > temperature > HCO3> pH > NO3> SO4>Cl> PO4> TSS > salinity > NO2. Multivariate analysis revealed that the main sources of water quality parameters might be related to dumpsites around the study area. According to the HCA, and PCA, the parameters were divided into three groups; the first group correlated with Salinity, TSS, SO4, NO3 and NO2; the second group correlated with TDS and HCO3, and the third with pH, temperature, and conductivity. The hydrochemistry of groundwater from the area reveals that the water could be applied to irrigation.

Detection and identification of leukemic and normal cells using Raman spectroscopy and multivariate statistical analysis

Raman spectroscopy technique was successfully employed to study the difference between leukemic cells (Jurkat cell line) and normal human bone marrow mononuclear cells using near-infrared laser (785 nm) excitation in this study. Significant differences in Raman spectra from two kinds of cell lines showed special changes in the percentage of biochemical constituents in different cells. The obtained spectral data were used to develop diagnostic algorithms by multivariate statistical analysis, including principal component analysis combined with linear discriminate analysis. The multivariate statistical analysis method for classification between cancer and normal cells has achieved good differentiation with high diagnostic sensitivity (100%), specificity (100%), and accuracy (100%), respectively. These results showed that Raman spectroscopy is a novel, nondestructive method, that provided interesting information about the changes in biochemical properties of cells. Raman spectroscopy combined with multivariate statistical analysis can serve as a potential clinical diagnosis method of leukemia.

Origin tracing and adulteration identification of bird's nest by high- and low-field NMR combined with pattern recognition

Edible bird’s nest (EBN) is a high-value health food with various nutrients and bioactive components. With increasing demand for EBN, they are often adulterated with cheaper ingredients or falsely labeled by the origin information, thus harming consumer interests. In this study, high- and low-field nuclear magnetic resonance (HF/LF-NMR) technology combined with multivariate statistical analysis was used to identify the geographical marker of EBN from different origins and authenticate the adulterated EBN with various adulterants at different adulteration rates. Authentic EBN samples from Malaysia were used to simulate adulteration using gelatin (GL), agar (AG) and starch (ST) at 10 %, 20 %, 40 %, 60 %, 80 %, and 100 % w/w, respectively. The results showed significant differences in composition among EBN from different origins, with isocaproate and citric acid serving as geographical markers for Malaysia and Vietnam, respectively. Leucine, glutamic acid, and N-acetylglycoprotein serving as geographical markers for Indonesia. In addition, PLS model further verified the accuracy of origin identification of EBN. The LF-NMR results of adulteration EBN showed a linear correlation between the transverse relaxation (T2, S2) and the adulterated ratio. The OPLS-DA based on T2 spectra could accurately identify authentic EBN from adulterated with GL, AG and ST at 40 %, 20 %, and 20 %, respectively. Fisher discrimination model was able to differentiate at 20 %, 20 %, and 40 %, respectively. These results show that the 1H NMR combined with multivariate statistical analysis method could be a potential tool for the detection of origin and adulteration of EBN.