Using Multivariate Statistical and Geostatistical Methods to Identify Spatial Variability of Trace Elements in Agricultural Soils in Dongguan City,Guangdong,China

Dongguan （东莞） City, located in the Pearl River Delta, South China, is famous for its rapid industrialization in the past 30 years. A total of 90 topsoil samples have been collected from agricultural fields, including vegetable and orchard soils in the city, and eight heavy metals （As, Cu, Cd, Cr, Hg, Ni, Pb, and Zn） and other items （pH values and organic matter） have been analyzed, to evaluate the influence of anthropic activities on the environmental quality of agricultural soils and to identify the spatial distribution of trace elements and possible sources of trace elements. The elements Hg, Pb, and Cd have accumulated remarkably here, incomparison with the soil background content of elements in Guangdong （广东） Province. Pollution is more serious in the western plain and the central region, which are heavily distributed with industries and rivers. Multivariate and geostatistical methods have been applied to differentiate the influences of natural processes and human activities on the pollution of heavy metals in topsoils in the study area. The results of cluster analysis （CA） and factor analysis （FA） show that Ni, Cr, Cu, Zn, and As are grouped in factor F1, Pb in F2, and Cd and Hg in F3, respectively. The spatial pattern of the three factors may be well demonstrated by geostatistical analysis. It is shown that the first factor could be considered as a natural source controlled by parent rocks. The second factor could be referred to as ＂industrial and traffic pollution sources＂. The source of the third factor is mainly controlled by long-term anthropic activities, as a consequence of agricultural activities, fossil fuel consumption, and atmospheric deposition.

Multivariate analysis and geochemical investigations of groundwater in a semi-arid region, case of superficial aquifer in Ghriss Basin, Northwest Algeria

This study aims to investigate the hydrochemical characteristics of shallow aquifer in a semi-arid region situated in northwest Algeria,and to understand the major factors governing groundwater quality.The study area is suffering from recurring droughts,groundwater resource over-exploitation and groundwater quality degradation.The approach used is a combination of traditional hydrochemical analysis methods of multivariate statistical techniques,principal component analysis(PCA),and ratios of major ions,based on the data derived from 33 groundwater samples collected in February 2014.Results show that groundwater in the study area are highly mineralized and collectively has a high concentration of chloride(as Cl^(−)).The dominant water types are Na-Cl(27%),Mg-HCO_(3)(24%)and Mg-Cl(24%).According to the(PCA)approach,salinization is the main process that controls the hydrochemical variability.The PCA analysis reveal the impact of anthropogenic factor especially the agricultural activities on the groundwater quality.The PCA highlighted two types of recharge:Superficial recharge from effective rainfall and excess irrigation water distinguished by the presence of nitrate and lateral recharge or vertical leakage from carbonate formations marked by the omnipresence of HCO_(3)^(−).Additionally,three categories of samples were identified:(1)samples characterized by good water quality and receiving notable recharge from carbonate formations;(2)samples impacted by the natural salinization process;and(3)samples contaminated by anthropogenic activities.The major natural processes influencing water chemistry are the weathering of carbonate and silicate rocks,dissolution of evaporite as halite,evaporation and cation exchange.The study results can provide the basis for local decision makers to ensure the sustainable management of groundwater and the safety of drinking water.

Characteristics and genesis of groundwater salinization in coastal areas of the Lower Reaches of Oujiang Basin

The coastal areas of the lower reaches of Oujiang River Basin are rich in groundwater resources.However,the unsustainable exploitation and utilization of groundwater have led to significant changes in the groundwater environment.Understanding the characteristics and genesis of groundwater salinization is crucial for preventing its deterioration and ensuring sustainable utilization.In this study,a comprehensive approach combining the ion ratio method,mineral saturation index method and multivariate statistical analysis was employed to investigate the hydrochemical characteristics and main controlling factors in the study area.The findings reveal that:(1)Groundwater samples in study area exhibit a neutral to slightly alkaline pH.The predominant chemical types of unconfined water are HCO_(3)-Ca·Na,HCO_(3)·Cl-Na·Ca and HCO_(3)·SO_(4)-Ca·Na,while confined water mainly exhibits Cl·HCO_(3)-Na and Cl-Na types.(2)Salinity coefficients indicate an increase in salinity from unconfined to confined water.TDS,Na^(+)and Cl^(–)concentrations show an increasing trend from mountainous to coastal areas in unconfined water,while confined water displays variability in TDS,Na^(+)and Cl^(–)concentrations.(3)Groundwater salinity is mainly influenced by water-rock interactions,including the dissolution of halite and gypsum,cation exchange,and seawater intrusion etc.Additionally,human activities and carbonate dissolution contribute to salinity in unconfined water.Seawater intrusion is identified as the primary factor leading to higher salinity in confined water compared to unconfined water,with increasing cation exchange and seawater interaction observed from unconfined to confined water.

STUDY ON THE MULTIVARIATE STATISTICAL ESTIMATION OF TROPICAL CYCLONE INTENSITY USING FY-3 MWRI BRIGHTNESS TEMPERATURE DATA

A technique for estimating tropical cyclone(TC) intensity over the Western North Pacific utilizing FY-3Microwave Imager(MWRI) data is developed. As a first step, we investigated the relationship between the FY-3 MWRI brightness temperature(TB) parameters, which are computed in concentric circles or annuli of different radius in different MWRI frequencies, and the TC maximum wind speed(Vmax) from the TC best track data. We found that the parameters of lower frequency channels' minimum TB, mean TB and ratio of pixels over the threshold TB with a radius of 1.0 or 1.5 degrees from the center give higher correlation. Then by applying principal components analysis(PCA)and multiple regression method, we established an estimation model and evaluated it using independent verification data, with the RMSE being 13 kt. The estimated Vmax is always stronger in the early stages of development, but slightly weaker toward the mature stage, and a reversal of positive and negative bias takes place with a boundary of around 70 kt. For the TC that has a larger error, we found that they are often with less organized and asymmetric cloud pattern, so the classification of TC cloud pattern will help improve the acuracy of the estimated TC intensity, and with the increase of statistical samples the accuracy of the estimated TC intensity will also be improved.

Spatial variation assessment of groundwater quality using multivariate statistical analysis(Case Study:Fasa Plain,Iran)

Groundwater is considered as one of the most important sources for water supply in Iran.The Fasa Plain in Fars Province,Southern Iran is one of the major areas of wheat production using groundwater for irrigation.A large population also uses local groundwater for drinking purposes.Therefore,in this study,this plain was selected to assess the spatial variability of groundwater quality and also to identify main parameters affecting the water quality using multivariate statistical techniques such as Cluster Analysis(CA),Discriminant Analysis(DA),and Principal Component Analysis(PCA).Water quality data was monitored at 22 different wells,for five years(2009-2014)with 10 water quality parameters.By using cluster analysis,the sampling wells were grouped into two clusters with distinct water qualities at different locations.The Lasso Discriminant Analysis(LDA)technique was used to assess the spatial variability of water quality.Based on the results,all of the variables except sodium absorption ratio(SAR)are effective in the LDA model with all variables affording 92.80%correct assignation to discriminate between the clusters from the primary 10 variables.Principal component(PC)analysis and factor analysis reduced the complex data matrix into two main components,accounting for more than 95.93%of the total variance.The first PC contained the parameters of TH,Ca2+,and Mg2+.Therefore,the first dominant factor was hardness.In the second PC,Cl-,SAR,and Na+were the dominant parameters,which may indicate salinity.The originally acquired factors illustrate natural(existence of geological formations)and anthropogenic(improper disposal of domestic and agricultural wastes)factors which affect the groundwater quality.

New Method for Multivariate Statistical Process Monitoring

A new method using discriminant analysis and control charts is proposed for monitoring multivariate process operations more reliably.Fisher discriminant analysis （FDA） is used to derive a feature discriminant direction （FDD） between each normal and fault operations,and each FDD thus decided constructs the feature space of each fault operation.Individuals control charts （XmR charts） are used to monitor multivariate processes using the process data projected onto feature spaces.Upper control limit （UCL） and lower control limit （LCL） on each feature space from normal process operation are calculated for XmR charts,and are used to distinguish fault from normal.A variation trend on an XmR chart reveals the type of relevant fault operation.Applications to Tennessee Eastman simulation processes show that this proposed method can result in better monitoring performance than principal component analysis （PCA）-based methods and can better identify step type faults on XmR charts.

Joint multivariate statistical model and its applications to the synthetic earthquake prediction

Considering the problems that should be solved in the synthetic earthquake prediction at present, a new model is proposed in the paper. It is called joint multivariate statistical model combined by principal component analysis with discriminatory analysis. Principal component analysis and discriminatory analysis are very important theories in multivariate statistical analysis that has developed quickly in the late thirty years. By means of maximization information method, we choose several earthquake prediction factors whose cumulative proportions of total sam-ple variances are beyond 90% from numerous earthquake prediction factors. The paper applies regression analysis and Mahalanobis discrimination to extrapolating synthetic prediction. Furthermore, we use this model to charac-terize and predict earthquakes in North China (30~42N, 108~125E) and better prediction results are obtained.

Quality assessment of Jinhongtang Granule using UFLC-MS/MS and multivariate statistical analysis

Jinhongtang is a traditional Chinese medicine formula composed of Rheum palmatum L.stem,Sargentodoxa cuneata stem,and Taraxacum mongolicum and is used for the treatment of sepsis.However,quality assessment method for Jinhongtang is not available.In present study,we developed a UFLC-MS/MS method to determine 16 analytes in 20 batches of home-made and commercial Jinhongtang.Multivariate statistical analysis revealed the significant differences in the quality of home-made and commercial Jinhongtang and the difference in the quality of home-made samples was more significant.The integrated strategy based on UFLC-MS/MS and multivariate statistical analysis provided a new basis for the overall quality assessment of Jinhongtang.

Study on the Relationship between Soil and Environment Based on Multivariate Statistical Analysis

[Objective] The study aimed to study the relationship between soil and environment on the basis of multivariate statistical analysis. [ Method] Through field investigation, sampling and laboratory analysis, we discussed the relationship between soil properties and environmental factors in Mizhi County, North Shaanxi by using Canoco multivariate statistical analysis. [ Result]According to the effects of various environmental factors on soil properties, the influencing order of environmental factors was land use way 〉 vegetation type 〉 vegetation restoration years 〉 vegeta- tion coverage 〉 slope aspect 〉 gradient 〉 elevation. In a word, soil properties were significantly affected by land use way and vegetation type which were the most important environmental factors of soil properties in spatial variation, while vegetation restoration years were closely related to the ac- cumulation of soil nutrients. [ Condusion]The research could provide theoretical references for the construction of ecological environment in Loess Plateau of China.

Multivariate Statistical Analysis of Large Datasets: Single Particle Electron Microscopy

Biology is a challenging and complicated mess. Understanding this challenging complexity is the realm of the biological sciences: Trying to make sense of the massive, messy data in terms of discovering patterns and revealing its underlying general rules. Among the most powerful mathematical tools for organizing and helping to structure complex, heterogeneous and noisy data are the tools provided by multivariate statistical analysis (MSA) approaches. These eigenvector/eigenvalue data-compression approaches were first introduced to electron microscopy (EM) in 1980 to help sort out different views of macromolecules in a micrograph. After 35 years of continuous use and developments, new MSA applications are still being proposed regularly. The speed of computing has increased dramatically in the decades since their first use in electron microscopy. However, we have also seen a possibly even more rapid increase in the size and complexity of the EM data sets to be studied. MSA computations had thus become a very serious bottleneck limiting its general use. The parallelization of our programs—speeding up the process by orders of magnitude—has opened whole new avenues of research. The speed of the automatic classification in the compressed eigenvector space had also become a bottleneck which needed to be removed. In this paper we explain the basic principles of multivariate statistical eigenvector-eigenvalue data compression;we provide practical tips and application examples for those working in structural biology, and we provide the more experienced researcher in this and other fields with the formulas associated with these powerful MSA approaches.

Water Quality Assessment of a Tropical Mexican Lake Using Multivariate Statistical Techniques

Water quality of Mexican tropical lake Chapala was assessed through multivariate statistical techniques, cluster analysis (CA) and principal component analysis (PCA) at ten different monitoring sites for ten physicochemical variables and six metals. This study evaluated and interpreted complex water quality data sets and apportioned of pollution sources to get better information about water quality. From descriptive statistics results, the highest concentrations of metals occurred during the dry season, and this trend was explained by the fact that an unusual rainy event occurred during the month of February 2009 and brought metals into the lake by runoffs from nearby mountains. According to international criteria for water consumption by aquatic organisms [USEPA], only Zn concentration values were below these criteria whereas the values of Ni, Pb, Cd and Fe were above the corresponding values set in these criteria (Ni: 52 μg·L-1, Pb: 2.5 μg·L-1, Cd: 0.25 μg·L-1, and Fe: 1000 μg·L-1). The correlations were observed by PCA, which were used to classify the samples by CA, based on the PCA scores. Seven significant cluster groups of sampling locations—(sites 4 and 5), (sites 3 and 9), (site 7), (site 10), (sites 2 and 6), (site 8) and (site 1)— were detected on the basis of similarity of their water quality. The results revealed that the stress exerted on the lake caused by waste sources follows the order: domestic > agricultural > industrial.

Analysis of morphological characteristics of gravels based on digital image processing technology and self-organizing map

A comprehensive understanding of spatial distribution and clustering patterns of gravels is of great significance for ecological restoration and monitoring.However,traditional methods for studying gravels are low-efficiency and have many errors.This study researched the spatial distribution and cluster characteristics of gravels based on digital image processing technology combined with a self-organizing map(SOM)and multivariate statistical methods in the grassland of northern Tibetan Plateau.Moreover,the correlation of morphological parameters of gravels between different cluster groups and the environmental factors affecting gravel distribution were analyzed.The results showed that the morphological characteristics of gravels in northern region(cluster C)and southern region(cluster B)of the Tibetan Plateau were similar,with a low gravel coverage,small gravel diameter,and elongated shape.These regions were mainly distributed in high mountainous areas with large topographic relief.The central region(cluster A)has high coverage of gravels with a larger diameter,mainly distributed in high-altitude plains with smaller undulation.Principal component analysis(PCA)results showed that the gravel distribution of cluster A may be mainly affected by vegetation,while those in clusters B and C could be mainly affected by topography,climate,and soil.The study confirmed that the combination of digital image processing technology and SOM could effectively analyzed the spatial distribution characteristics of gravels,providing a new mode for gravel research.

Evaluation of hydro-chemistry in a phreatic aquifer in the Vindhyan Region, India, using entropy weighted approach and geochemical modelling

Groundwater quality monitoring and geochemical characterization in the phreatic aquifer are critical for ensuring universal and equitable access to clean,reliable,and inexpensive drinking water for all.This research was intended to investigate the hydrogeochemical attributes and mechanisms regulating the chemistry of groundwater as well as to assess spatial variation in groundwater quality in Satna district,India.To accomplish this,the groundwater data comprising 13 physio-chemical parameters from thirty-eight phreatic aquifer locations were analysed for May 2020 by combining entropy-weighted water quality index(EWQI),multivariate statistics,geochemical modelling,and geographical information system.The findings revealed that the groundwater is fresh and slightly alkaline.Hardness was a significant concern as 57.89% of samples were beyond the permissible limit of the World Health Organisation.The dominance of ions were in the order of Ca^(2+)> Na^(+)> Mg^(2+)> K^(+) and HCO_(3)^(-)> SO_(4)^(2-)> Cl^-> NO_(3)^(-)> F^(-).Higher concentration of these ions is mainly concentrated in the northeast and eastern regions.Pearson correlation analysis and principal component analysis(PCA) demonstrated that both natural and human factors regulate groundwater chemistry in the region.The analysis of Q-mode agglomerative hierarchical clustering highlighted three significant water clusters.Ca-HCO_3 was the most prevalent hydro-chemical facies in all three clusters.Geochemical modelling through various conventional plots indicated that groundwater chemistry in the research region is influenced by the dissolution of calcite/dolomite,reverse ion exchange,and by silicate and halite weathering.EWQI data of the study area disclosed that 73.69% of the samples were appropriate for drinking.Due to high salinity,Magnesium(Mg^(2+)),Nitrate(NO_(3)^(-)),and Bicarbonate(HCO_(3)^(-)) concentrations,the north-central and north-eastern regions are particularly susceptible.The findings of the study may be accomplished by policymakers and groundwater managers to achieve sustainable groundwater development at the regional scale.

Genetic differentiation in functional traits among wild cherry (Prunus avium L.) half-sib lines

Understanding intra-specific variation in leaf functional traits is one of the key requirements for the evaluation of species adaptive capacity to ongoing climate change, as well as for designing long-term breeding and conservation strategies. Hence, data of 19 functional traits describing plant physiology, antioxidant properties, anatomy and morphology were determined on 1-year-old seedlings of wild cherry (Prunus avium L.) half-sib lines. The variability within and among half-sib lines, as well as the estimation of multi-trait association, were examined using analysis of variance (ANOVA) followed by Tukey's honestly significant difference test and multivariate analyses: principal component analysis (PCA), canonical discriminant analysis (CDA) and stepwise discriminant analysis (SDA). Pearson’s correlation coefficient was used to evaluate linear correlation between the study parameters. The results of the ANOVA showed the presence of statistically significant differences (P < 0.01) among half-sib lines for all study traits. The differences within half-sib lines, observed through the contribution of the examined sources of variation to the total variance (%), had higher impact on total variation in the majority of the examined traits. Pearson’s correlation analysis and PCA showed strong relationships between gas exchange in plants and leaf size and stomatal density, as well as between leaf biomass accumulation, intercellular CO_(2) concentration and parameters related to antioxidant capacity of plants. Likewise, the results of SDA indicate that transpiration and stomatal conductance contributed to the largest extent, to the discrimination of the wild cherry half-sib lines. In addition, PCA and CDA showed separation of the wild cherry half-sib lines along the first principal component and first canonical variable with regards to humidity of their original sites. Multiple adaptive differences between the wild cherry half-sib lines indicate high potential of the species to adapt rapidly to climate change. The existence of substantial genetic variability among the wild cherry half-sib lines highlights their potential as genetic resources for reforestation purposes and breeding programmes.

The Key Factors Affecting Tuber Development of Potato in vitro and the Relation with Protein Fractions

According to previous analysis, some properties bounding up with tuber yield were investigated. The results showed that tuber average weight, plastid Mg2 + -ATPase activity, plastid Ca2 + -ATPase activity, mitochondria Mg2 + -ATPase activity, total soluble protein content, tuber average diameter, and Q-enzyme activity were important factors determining the tuber yield. The linear regression equation was:Y = 0.5211 + 0.0595X(1)+0.8389X(2) +0.0882X(3) -0.0073X(4) +0.1449X(5) +0.3510X(6) +0.0031X(7) -0.00003X(8) + 0.3412X(9) + 0.0127X(10) + 0.2904X(ll) + 0.0570X(12) + 0.0159X(13) + 0.3585X(14) + 0.0134X(15) - 0.1012X(16). At the same time, the relation between several important properties and soluble protein fractions were analyzed.

Quantitative hazard assessment system(Has-Q) for open pit mine slopes

Rock slope hazard assessment is an important part of risk analysis for open pit mines.The main parameters that can lead to rock slope failures are the parameters traditionally used in geomechanical classifications,the slope geometrical parameters and external factors like rainfall and blasting.This paper presents a methodology for a hazard assessment system for open pit mine slopes based on 88 cases collated around the world using principal components analysis,discriminant analysis and confidence ellipses.The historical cases used in this study included copper,gold,iron,diamond,lead and zinc,platinum and claystone mines.The variables used in the assessment methodology are uniaxial compressive strength of intact rock;spacing,persistence,opening,roughness,infilling and orientation of the main discontinuity set;weathering of the rock mass;groundwater;blasting method;and height and inclination of the pit.While principal component analysis was used to quantify the data,the discriminant analysis was used to establish a rule to classify new slopes about its stability condition.To provide a practical hazard assessment system,confidence ellipses were used to propose a hazard graph and generate the HAS-Q.The discriminant rule developed in this research has a high discrimination capacity with an error rate of 11.36%.

Delineation of spring capture zones in southern Great Basin,USA based on modeling results and geochemical data

The protection zones or capture zones of springs in desert environments can be hard to identify,but they are critical to spring protection.Most springs fed by regional aquifers are susceptible to contamination and groundwater development.The U.S.Environmental Protection Agency has established hydrogeologic mapping methods to delineate protection zones for springs.However,it is often difficult to determine a regional aquifer system's flow pattern with this technique alone,and the use of these methods is not conducive to efficient groundwater management.Particle tracking analysis using a well-conceptualized and calibrated numerical model for the three-dimensional groundwater flow domain feeding a given group of springs can help facilitate the identification of spring capture zone boundaries.Building upon this basis,a multifaceted approach was developed to define clear boundaries of the capture zones for the springs in the Furnace Creek,Ash Meadows,and the Muddy River areas in the southern Great Basin,USA.Capture zones were first delineated from inverse particle tracking and Hydrologic Unit 12 watersheds.Afterwards,they were adjusted based on water budgets,geology,and hydrologically significant faults.Finally,a geochemical analysis of the groundwater chemistry and isotopic data was conducted to verify the extent of each spring capture zone.This multifaceted approach adds confidence to the new delineations.

Spatial and statistical assessment of nitrate contamination in groundwater:Case of Sais Basin, Morocco

The objective of this study is to evaluate the nitrate contamination in the plioquaternary aquifer of Sais Basin based on a statistical approach. A total of 98 samples were collected in the cultivated area during the spring and autumn period of 2018. The results show that 55% and 57% of the samples in spring and autumn respectively exceed the threshold fixed by WHO(50 mg/L). However, nitrate concentrations do not show seasonal and spatial variation(p>0.05). The results of the correlation matrix, principal component analysis(PCA), and hierarchical cluster analysis(HCA) suggest that nitrate pollution is related to anthropogenic source. Moreover, multiple linear regression results show that NO3 is more positively explained in the spring period by Ca and SO4 and negatively explained by pH and HCO3. Regarding the autumn period, nitrate pollution is positively explained by Ca and negatively by pH. This study proposes a useful statistical platform for assessing nitrate pollution in groundwater.

Effects of Vegetation Succession on Soil Fertility Within Farming-Plantation Ecotone in Ziwuling Mountains of the Loess Plateau in China

To further understand the relationship between vegetation succession and soil fertility within farming-plantation ecotone in Ziwuling Mountains of the Loess Plateau, nine kinds of widely distributed communities at different succession stages were selected, and the effects of vegetation succession on soil fertility were studied through the methods of comparing two hierarchical clustering （similarity index： B） and other multivariate analysis. The results showed that： （i） the similarity in clustering pattern of nine communities which classified by plant species and soil nutrients respectively showed a trend of B ^-overall plant-soil0-10cn〉B^-overall plant-soil 10-20 cm 〉B^- overall plant-soil 20-40 cm, and for the top soil, it showed a trend of B^- grass-soil 0-10 cm 〉 B^-shrub-soil 0-10 cm 〉 Btree-soil0-10 cm; （ii） soil fertility increased during the succession process from abandoned land to forest community, and the soil fertility of forest community showed an increased order of coniferous forest →mixed forest →broadleaf forest; （iii） during the process of vegetation succession, the variation of topsoil fertility was higher than that of the subsurface soil （coefficient of variation： CV0-10 cm 〉CV 10-20 cm 〉 CV20-40 cm）, and when the succession developed into the stages of shrub and forest communities, the top soil fertility had been improved significantly; and （iv） for the subsurface soil of the communities at the advanced succession stages, the soil fertility also increased to some extent. Our results suggested that the method of comparing two hierarchical clustering reflected the similarity level of different cluster patterns, therefore, it was helpful to study the relationship between vegetation succession and soil fertility. There was a corresponding relationship between the change process of soil fertility from the top soil to subsurface soil and the process of vegetation succession from the early stages to the advanced stage. The differentiations of soil fertility in vertical space and horizontal space were both caused by vegetation succession, which was significant for both the shrub and forest communities. The improved level of forest soil fertility was related to forest vegetation types and the improved fertility level of broad-leaved forest-soil community was higher than that of the coniferous forest soil. In the practice on soil fertility ecological restoration of the loess plateau, it is important to carry out reasonably artificial forestation so as to enhance the restoration and improvement of soil fertility.

Metabolomics-based comparative analysis of the effects of host and environment on Viscum coloratum metabolites and antioxidative activities

Viscum coloratum(Kom.)Nakai is a well-known medicinal hemiparasite widely distributed in Asia.The synthesis and accumulation of its metabolites are affected by both environmental factors and the host plants,while the latter of which is usually overlooked.The purpose of this study was to comprehensively evaluate the effects of host and habitat on the metabolites in V.coloratum through multiple chemical and biological approaches.The metabolite profile of V.coloratum harvested from three different host plants in two habitats were determined by multiple chemical methods including high-performance liquid chromatography-ultraviolet(HPLC-UV),gas chromatography-flame ionization detector(GC-FID)and ultra-performance liquid chromatography quadrupole time of flight mass spectrometry(UPLC-QTOF/MS).The differences in antioxidant efficacy of V.coloratum were determined based on multiple in vitro models.The multivariate statistical analysis and data fusion strategy were applied to analyze the differences in metabolite profile and antioxidant activity of V.coloratum.Results indicated that the metabolite profile obtained by various chemical approaches was simultaneously affected by host and environment factors,and the environment plays a key role.Meanwhile,three main differential metabolites between two environment groups were identified.The results of antioxidant assay indicated that the environment has greater effects on the biological activity of V.coloratum than the host.Therefore,we conclude that the integration of various chemical and biological approaches combined with multivariate statistical and data fusion analysis,which can determine the influences of host plant and habitat on the metabolites,is a powerful strategy to control the quality of semi-parasitic herbal medicine.