Multivariate Data Research Articles

Evaluation of the Pollution by the Potentially Toxic Elements in the Topsoils of a Semi-Industrial Zone, Northwestern Iran

ABSTRACT Various industries, including cement production, petrochemical processing, and power plants, exert significant environmental impacts on their surroundings. The close proximity of these industries within the study area underscores the critical need to investigate soil pollution of potentially toxic elements and elucidate possible pollution sources and associated risks. The present study involved taking 44 topsoil (0–10 cm) samples of Arsenic (As), Cadmium (Cd) Cobalt (Co), Chromium (Cr), Copper (Cu), Nickel (Ni), Lead (Pb), and Zinc (Zn) concentrations, which their mean concentration were found to be 11.29, 0.46, 15.89, 67.36, 44.36, 79.93, 8.50 and 68.27 mg kg−1, respectively. Comparison with the baseline values stated in local and international soil quality guidelines has indicated higher As, Cd, Cr, Cu, and Ni concentrations. The soil pollution indices of the geoaccumulation index, pollution index, pollution load index, and enrichment factor pollution have evaluated the pollution status of the study area to be uncontaminated to moderately contaminated, moderately polluted, polluted, deficient enrichment to moderate enrichment classes, respectively. Ecological and health risks that involved the application of the single index of ecological risk, potential ecological risk, hazard quotient, hazard index, and cancer risk have revealed that the study area was ecologically in a low-risk state with an average potential ecological risk value of 90.38, while carcinogenic (10−6-10−4) and non-carcinogenic risks (>1) were also found to be in a tolerable range for human health. Possible pollution sources were identified using multivariate statistics and further confirmed and visualized by the spatial distribution maps obtained from the deterministic interpolation method of inverse distance weighting. These analyses have suggested that the soil pollution which its existence was confirmed by the related indices and comparisons, to the potentially toxic elements of Cr and Ni can be possibly attributed to the petrochemical and power plants, to Cd could be appointed to cement plant, and to Cu could be associated with agricultural activities. It has also been found that arsenic was determined to be a common pollutant among the three main industries.

Mentally ill people with means-tested benefits at the job center : Diagnosis spectrum and care-First results from the LIPSY project

Although people receiving means-tested benefits are regularly taken care of at the job center, little is known about their mental health situation and mental health care. The aim of the study was to describe the diagnostic spectrum and the functional status as well as the mental health care utilization of individuals with mental illnesses who are receiving means-tested benefits. Mentally ill people with means-tested benefits were recruited at the job center as part of the "Leipzig Individual Placement and Support for Mentally Ill People" (LIPSY) project, where they were initially diagnosed according to ICD-10 and included in the project if they had amental disorder. Mental healthcare utilization was recorded. In the present study, data from n = 583 consecutively included persons were analyzed descriptively and by multivariate statistics. Of the study participants 60.7% (n = 583; 51.5% female; average age36years; unemployed for an average of 4.8years) suffered from affective disorders, followed by neurotic, stress and somatoform disorders (42.5%). With amean global assessment of functioning (GAF) value of 49.4 (SD 7.7), there was on average aserious impairment. Nearly half of the subjects (48.5%) had never received psychotherapeutic, psychiatric or neurological treatment in the 6 months prior to inclusion in the project. It could be shown that there is abroad spectrum of mental disorders with substantial functional impairment. The data suggest that this group is significantly undertreated and difficult to reach.

A study of online academic risk prediction based on neural network multivariate time series features

SummaryNeural networks are becoming increasingly widely used in various fields, especially for academic risk forecasts. Academic risk prediction is a hot topic in the field of big data in education that aims to identify and help students who experience great academic difficulties. In recent years, the use of machine learning algorithms and deep learning algorithms to achieve academic risk prediction has garnered increased attention and development. However, most of these studies use nontime series data as features for prediction, which are slightly insufficient in terms of timeliness. Therefore, this article focuses on time series data features that are more expressive of changes in students' learning status and uses multivariate time series data as predictive features. This article proposes a method based on multivariate time series features and a neural network to predict academic risk. The method includes three steps: first, the multivariate time series feature is extracted from the interaction records of the students' online learning platforms; second, the multivariate time series feature transformation model ROCKET is applied to convert the multivariate time series feature into a new feature; third, the new feature is converted into a final prediction result using a linear classification model. Comparative tests show that the proposed method has high effectiveness.

Using joint probability density to create most informative unidimensional indices: a new method using pain and psychiatric severity as examples

BackgroundDimension reduction methods do not always reduce their underlying indicators to a single composite score. Furthermore, such methods are usually based on optimality criteria that require discarding some information. We suggest, under some conditions, to use the joint probability density function (joint pdf or JPD) of p-dimensional random variable (the p indicators), as an index or a composite score. It is proved that this index is more informative than any alternative composite score. In two examples, we compare the JPD index with some alternatives constructed from traditional methods.MethodsWe develop a probabilistic unsupervised dimension reduction method based on the probability density of multivariate data. We show that the conditional distribution of the variables given JPD is uniform, implying that the JPD is the most informative scalar summary under the most common notions of information. B. We show under some widely plausible conditions, JPD can be used as an index. To use JPD as an index, in addition to having a plausible interpretation, all the random variables should have approximately the same direction(unidirectionality) as the density values (codirectionality). We applied these ideas to two data sets: first, on the 7 Brief Pain Inventory Interference scale (BPI-I) items obtained from 8,889 US Veterans with chronic pain and, second, on a novel measure based on administrative data for 912 US Veterans. To estimate the JPD in both examples, among the available JPD estimation methods, we used its conditional specifications, identified a well-fitted parametric model for each factored conditional (regression) specification, and, by maximizing the corresponding likelihoods, estimated their parameters. Due to the non-uniqueness of conditional specification, the average of all estimated conditional specifications was used as the final estimate. Since a prevalent common use of indices is ranking, we used measures of monotone dependence [e.g., Spearman’s rank correlation (rho)] to assess the strength of unidirectionality and co-directionality. Finally, we cross-validate the JPD score against variance–covariance-based scores (factor scores in unidimensional models), and the “person’s parameter” estimates of (Generalized) Partial Credit and Graded Response IRT models. We used Pearson Divergence as a measure of information and Shannon’s entropy to compare uncertainties (informativeness) in these alternative scores.ResultsAn unsupervised dimension reduction was developed based on the joint probability density (JPD) of the multi-dimensional data. The JPD, under regularity conditions, may be used as an index. For the well-established Brief Pain Interference Inventory (BPI-I (the short form with 7 Items) and for a new mental health severity index (MoPSI) with 6 indicators, we estimated the JPD scoring. We compared, assuming unidimensionality, factor scores, Person’s scores of the Partial Credit model, the Generalized Partial Credit model, and the Graded Response model with JPD scoring. As expected, all scores’ rankings in both examples were monotonically dependent with various strengths. Shannon entropy was the smallest for JPD scores. Pearson Divergence of the estimated densities of different indices against uniform distribution was maximum for JPD scoring.ConclusionsAn unsupervised probabilistic dimension reduction is possible. When appropriate, the joint probability density function can be used as the most informative index. Model specification and estimation and steps to implement the scoring were demonstrated. As expected, when the required assumption in factor analysis and IRT models are satisfied, JPD scoring agrees with these established scores. However, when these assumptions are violated, JPD scores preserve all the information in the indicators with minimal assumption.

Children of Nature: Thoughts on Targeted and Untargeted Analytical Approaches to Decipher Polyphenol Reactivity in Food Processing and Metabolism.

Following 25 years of polyphenol research in our laboratory, the astonishing chemical and metabolic reactivity of polyphenols resulting in considerable chemical diversity has emerged as the most remarkable attribute of this class of natural products. To illustrate this concept, we will present selected data from black tea and coffee chemistry. In black tea chemistry, enzymatic fermentation converts six catechin derivatives into an estimated 30 000 different polyphenolic compounds via a process we have termed the oxidative cascade process. In coffee roasting, around 45 chlorogenic acids are converted into an estimated 250 novel derivatives following a series of diverse chemical transformations. Following ingestion by humans, these dietary polyphenols, whether genuine secondary metabolites or food processing products, encounter the microorganisms of the gut microbiota, converting them into a myriad of novel structures. In the case of coffee, only two out of 250 chlorogenic acids are absorbed intact, with most others being subject to gut microbial metabolism. Modern mass spectrometry (MS) has been key in unravelling the true complexity of polyphenols subjected to food processing and metabolism. We will accompany this assay with a short overview on analytical strategies developed, including ultrahigh-resolution MS, tandem MS, multivariate statistics, and molecular networking that allow an insight into the fascinating chemical processes surrounding dietary polyphenols. Finally, experimental results studying biological activity of polyphenols will be presented and discussed, highlighting a general promiscuity of this class of compounds associated with nonselective protein binding leading to loss of enzymatic function, another noteworthy general property of many dietary polyphenols frequently overlooked.

Fractional-order state space reconstruction: a new frontier in multivariate complex time series

This paper presents a novel approach to the phase space reconstruction technique, fractional-order phase space reconstruction (FOSS), which generalizes the traditional integer-order derivative-based method. By leveraging fractional derivatives, FOSS offers a novel perspective for understanding complex time series, revealing unique properties not captured by conventional methods. We further develop the multi-span transition entropy component method (MTECM-FOSS), an advanced complexity measurement technique that builds upon FOSS. MTECM-FOSS decomposes complexity into intra-sample and inter-sample components, providing a more comprehensive understanding of the dynamics in multivariate data. In simulated data, we observe that lower fractional orders can effectively filter out random noise. Time series with diverse long- and short-term memory patterns exhibit distinct extremities at different fractional orders. In practical applications, MTECM-FOSS exhibits competitive or superior classification performance compared to state-of-the-art algorithms when using fewer features, indicating its potential for engineering tasks.

Integrated metabolomics analysis of chill-stored rose shrimp (Parapenaeus longirostris) treated with different pressure levels of high hydrostatic pressure by 1H-NMR spectroscopy.

The antimicrobial effects of high hydrostatic pressure (HHP) treatments on chill-stored seafood are well-documented, while their impact on the metabolic profile of seafood, especially the metabolome of fish flesh, and remains underexplored. Addressing this gap, this study investigates the effects of HHP on the metabolome of chill-stored rose shrimp by conducting multivariate data analysis based on untargeted proton nuclear magnetic resonance observations. Vacuum-packed rose shrimp samples were subjected to HHP at 0, 400, 500, and 600MPa for 10 min and then stored at 2-4°C. The microorganism analysis and metabolic analysis were carried out on days 1 and 14. HHP treatment effectively deactivated Lactobacillus spp., Escherichia coli, Pseudomonas spp., total Coliforms, and sulfite-reducing anaerobic bacteria. Consequently, HHP treatment significantly reduced the formation rate of decay-related metabolites, such as hypoxanthine, trimethylamine, and biogenic amines, which exhibited significant accumulation in untreated samples. Multivariate unsupervised analyses provided insights into the overall changes in the metabolite profile induced by HHP. Metabolic pathway analysis revealed several pathways underlying spoilage, including pyruvate metabolism, valine, leucine, and isoleucine biosynthesis, purine metabolism, methane metabolism, glycine, serine, and threonine metabolism, citrate cycle (TCA cycle), glycolysis/gluconeogenesis, alanine, aspartate, and glutamate metabolism, sulfur metabolism, pantothenate and CoA biosynthesis, glutathione metabolism, and glyoxylate and dicarboxylate metabolism. Importantly, these pathways underwent alterations due to the application of HHP, particularly at high-pressure levels. In summary, the results unveil the potential mechanisms of HHP effects on chill-stored rose shrimps.

DDANF: Deep denoising autoencoder normalizing flow for unsupervised multivariate time series anomaly detection

In recent years, the proliferation of IoT technologies and the widespread adoption of wireless sensors across various critical infrastructures such as power plants, service monitoring systems, space and earth exploration missions, and water treatment facilities have resulted in the generation of vast quantities of multivariate time series data. Within this context, unsupervised anomaly detection has emerged as a pivotal yet challenging problem in time series research, necessitating machine learning models capable of identifying rare anomalies amidst massive datasets. Traditionally, unsupervised methods have approached this issue by learning representations of primary patterns within sequences and detecting deviations through reconstruction errors. However, the effectiveness of this approach is often limited due to the intricate dynamics and diverse patterns inherent in these dynamic systems. Moreover, many existing unsupervised anomaly detection techniques fail to fully exploit inter-feature relationships within multivariate time series data, thereby overlooking a crucial criterion for accurate detection. To address these shortcomings, this paper introduces a novel unsupervised method for multivariate time series anomaly detection based on normalized flows and autoencoders. Central to our approach is the incorporation of a channel shuffling mechanism during training, enhancing the model’s capacity to discern inter-channel patterns and anomalies. Concurrently, the application of normalized flows within the autoencoder framework serves to constrain the latent space, effectively isolating anomalies and improving detection accuracy. Experimental validation conducted on two large-scale public datasets demonstrates the efficacy of the proposed method compared to established benchmarks, highlighting its superior performance.

Implications of Water Quality Index and Multivariate Statistics for Improved Environmental Regulation in the Irtysh River Basin (Kazakhstan)

The selection of sites for permanent environmental monitoring of natural water bodies should rely on corresponding source apportionment studies. Tools like the water quality index (WQI) assessment may support this objective. This study aims to analyze a decade-long dataset of measurements of 26 chemical components at 26 observation points within the Irtysh River Basin, aiming to identify priority zones for stricter environmental regulations. It was achieved through the WQI tool integrated with geoinformation systems (GISs) and multivariate statistical techniques. The findings highlighted that both upstream sections of tributaries (Oba and Bukhtarma rivers) and the mainstream of the basin are generally in good condition, with slight fluctuations observed during flooding periods. Areas in the basin experiencing significant impacts from mining and domestic wastewater treatment activities were identified. The rivers Glubochanka (GL) and Krasnoyarka (KR) consistently experienced marginal water quality throughout the observation period. Various contaminant sources were found to influence water quality. The impact of domestic wastewater treatment facilities was represented by twofold elevated concentrations of chemical oxygen demand, reaching 22.6 and 27.1 mg/L for the KR and GL rivers, respectively. Natural factors were indicated by consistent slight exceedings of recommended calcium levels at the KR and GL rivers. These exceedances were most pronounced during the cold seasons, with an average value equal to 96 mg/L. Mining operations introduced extremal concentrations of trace elements like copper, reaching 0.046–0.051 mg/L, which is higher than the threshold by 12–13 times; zinc, which peaked at 1.57–2.96 mg/L, exceeding the set limit by almost 50–100 times; and cadmium, peaking at levels surpassing 1000 times the safe limit, reaching 0.8 mg/L. The adverse impact of mining activities was evident in the Tikhaya, Ulba, and Breksa rivers, showing similar trends in trace element concentrations. Seasonal effects were also investigated. Ice cover formation during cold seasons led to oxygen depletion and the exclusion of pollutants into the stream when ice melted, worsening water quality. Conversely, flooding events led to contaminant dilution, partially improving the WQI during flood seasons. Principal component analysis and hierarchical cluster analysis indicated that local natural processes, mining activities, and domestic wastewater discharge were the predominant influences on water quality within the study area. These findings can serve as a basis for enhanced environmental regulation in light of updated ecological legislation in Kazakhstan, advocating for the establishment of a comprehensive monitoring network and the reinforcement of requirements governing contaminating activities.

Experimental development of a method of short and medium-term photovoltaic generation forecasting using multivariate statistics and mathematical modeling

Uncertainties in photovoltaic solar energy production can make it challenging to dispatch energy into the electricity grid. Although photovoltaic generation storage solves this problem, a forecasting of the photovoltaic solar energy produced is necessary to control the energy injected into the grid. This article aims to develop the probabilistic methodology Reduced-Rank Regression (RRR) for forecasting photovoltaic generation in the short and medium terms. The RRR methodology forecasting uses the generation data of a grid-connected photovoltaic system. The proposed RRR model is simple, easy to access and apply, and does not use irradiance data. The model developed uses the multivariate statistical analysis technique. A advantage is that with a correlation with the performance indices of photovoltaic solar energy systems, the proposed method can be applied in any geographical location on the planet and with different photovoltaic solar energy systems. The application of the RRR methodology requires two searches/inputs. The first input is weather forecast data obtained from a weather forecasting platform, and the second is actual historical data on photovoltaic generation at the site where the method was developed. The proposed method was compared with the persistence method. Using a horizon of 1–10 h, the average monthly root mean square error for the RRR ranged from 7.3 % to 50.1 %. For the persistence method, the average monthly root mean square error ranged from 15.1 % to 65.0 %. Therefore, with the horizon of 24 h, the average monthly root mean square error for the RRR ranged from 4.5 % to 43.2 %. For the persistence method, the average monthly root mean square error ranged from 11.5 % to 75.0 %. We show experimentally that our method is competitive with the state-of-the-art in terms of obtaining photovoltaic generation forecasting without using solar radiation data.

Learning Network-Structured Dependence From Non-Stationary Multivariate Point Process Data

Learning Network-Structured Dependence From Non-Stationary Multivariate Point Process Data

Fusion of spectra and texture features of hyperspectral imaging for quantification and visualization of characteristic amino acid contents in beef

Characteristic amino acids is an important indicator for evaluating the nutritional value and flavor parameter of beef. To accurately quantify and visualize of arginine and alanine content in beef, the feasibility of visible near-infrared hyperspectral multivariate calibration analysis and data fusion was explored. Three methods were used to select the optimal feature wavelength and fusion multi-level texture information, and to develop the prediction performance of linear, non-linear and neural network models in different signals. Compared to all models, the linear model demonstrated superior performance in terms of characteristic spectral and fused spectral. Among, the more effective prediction performances emerged from CARS-ASM-ENT-PLSR model with RP2 = 0.9211, RMSEP = 0.1252 mg/100 g and RPDp = 3.49 for alanine. The UVE-ASM-ENT-HOM-COR-PLSR model for arginine prediction achieved a good performance of RP2 = 0.8596, RMSEP = 0.8596 mg/100 g and RPDp = 2.62. Finally, visualization plots of arginine and alanine content distribution were generated. This study shows that the data fusion method provides a new approach for rapid evaluation of CAA content.

Mean and covariance estimation for discretely observed high-dimensional functional data: Rates of convergence and division of observational regimes

Estimation of the mean and covariance parameters for functional data is a critical task, with local linear smoothing being a popular choice. In recent years, many scientific domains are producing multivariate functional data for which p, the number of curves per subject, is often much larger than the sample size n. In this setting of high-dimensional functional data, much of developed methodology relies on preliminary estimates of the unknown mean functions and the auto- and cross-covariance functions. This paper investigates the convergence rates of local linear estimators in terms of the maximal error across components and pairs of components for mean and covariance functions, respectively, in both L2 and uniform metrics. The local linear estimators utilize a generic weighting scheme that can adjust for differing numbers of discrete observations Nij across curves j and subjects i, where the Nij vary with n. Particular attention is given to the equal weight per observation (OBS) and equal weight per subject (SUBJ) weighting schemes. The theoretical results utilize novel applications of concentration inequalities for functional data and demonstrate that, similar to univariate functional data, the order of the Nij relative to p and n divides high-dimensional functional data into three regimes (sparse, dense, and ultra-dense), with the high-dimensional parametric convergence rate of log(p)/n1/2 being attainable in the latter two.

Application of soil quality evaluation indices and multivariate statistics to assess soil health: a case study

Application of soil quality evaluation indices and multivariate statistics to assess soil health: a case study

A novel test of missing completely at random: U-statistics-based approach

In this paper, a novel test for testing whether data are missing completely at random is proposed. Asymptotic properties of the test are derived utilizing the theory of non-degenerate U-statistics. It is shown that the novel test statistic coincides with the well-known Little's d 2 statistic in the case of a multivariate data that has only one variable susceptible to missingness. Then, the extensive simulation study is conducted to examine the performance of the test in terms of the preservation of type I error and in terms of power. Various underlying distributions, dimensions of the data, sample sizes and alternatives are used. Performance of the Little's MCAR test is used as a benchmark for the comparison. The novel test shows better performance in all of the studied scenarios, better preserving the type I error and having higher empirical powers for every studied alternative.

Lipidome and metabolome profiling of longissimus lumborum beef with different ultimate pH and postmortem aging

The objective of this exploratory study was to assess the changes on lipidome and metabolome profiling of Longissimus lumborum bull muscle with different ultimate pH (pHu) and aging periods. The bull muscles classified as normal, intermediate, or high pHu were collected from a Brazilian commercial slaughterhouse, cut into steaks, individually vacuum-packaged, and aged for 3 days (3-d) or 21 days (21-d) at 2 °C. Muscle extracts were analyzed for the profiles of both lipids, by mass spectrometry (via direct flow-injection), and metabolites, by nuclear magnetic resonance, with downstream multivariate data analysis. As major results, pairwise comparisons identified C12:0 and C14:0 acylcarnitines as potential biomarkers of the intermediate pHu-muscle, which are related to lipid catabolism for alternative energy metabolism and indicate less protein breakage postmortem. Interestingly, the concentration of arginine at early postmortem aging (3-d) may influence the previously reported improved tenderness in normal and high pHu-muscles. Moreover, upregulation of fumarate, formate, and acetate with increased pHu muscle at 21-d aging indicate more intense tricarboxylic acid cycle, amino acid degradation, and pyruvate oxidation by reactive oxygen species, respectively. These three compounds (fumarate, formate, and acetate) discriminated statistically the muscle with high pHu at 21-d aging. The normal pHu-muscle showed higher concentrations of glycogenolysis and glycolysis metabolites, including glucose, mannose, and pyruvate. Hence, our results enhance knowledge of postmortem biochemical changes of beef within different pHu groups aged up to 21 days, which is essential to understand the mechanisms underpinning bull meat quality changes.

An anomaly detection model for multivariate time series with anomaly perception

Multivariate time series anomaly detection is a crucial data mining technique with a wide range of applications in areas such as IT applications. Currently, the majority of anomaly detection methods for time series data rely on unsupervised approaches due to the rarity of anomaly labels. However, in real-world scenarios, obtaining a limited number of anomaly labels is feasible and affordable. Effective usage of these labels can offer valuable insights into the temporal characteristics of anomalies and play a pivotal role in guiding anomaly detection efforts. To improve the performance of multivariate time series anomaly detection, we proposed a novel deep learning model named EDD (Encoder-Decoder-Discriminator) that leverages limited anomaly samples. The EDD model innovatively integrates a graph attention network with long short term memory (LSTM) to extract spatial and temporal features from multivariate time series data. This integrated approach enables the model to capture complex patterns and dependencies within the data. Additionally, the model skillfully maps series data into a latent space, utilizing a carefully crafted loss function to cluster normal data tightly in the latent space while dispersing abnormal data randomly. This innovative design results in distinct probability distributions for normal and abnormal data in the latent space, enabling precise identification of anomalous data. To evaluate the performance of our EDD model, we conducted extensive experimental validation across three diverse datasets. The results demonstrate the significant superiority of our model in multivariate time series anomaly detection. Specifically, the average F1-Score of our model outperformed the second-best method by 2.7% and 73.4% in both evaluation approaches, respectively, highlighting its superior detection capabilities. These findings validate the effectiveness of our proposed EDD model in leveraging limited anomaly samples for accurate and robust anomaly detection in multivariate time series data.

Data-driven evaluation for quantifying energy resilience in distribution systems with microgrids and P2P energy trading

Electrical distribution systems (EDS) face the major issue of complete interruption of power when the supply end of the electrical grid encounters extreme events. The integration of distributed energy resources (DERs) at the residential level has opened the path to building energy resilience within these systems, enabling them to withstand such incidents at the load end. To further enhance energy resilience, peer-to-peer (P2P) energy trading networks could be established at the community level. While several researchers have developed models to implement energy trading in the P2P network with DERs, enabling prosumers to benefit from cost savings, the impact of both DERs and P2P energy trading on the system’s resilience has not been evaluated with a quantifiable measure. In this work, we propose a methodology introducing a quantifiable measure for evaluating energy resilience in electrical distribution systems with DERs and P2P energy trading by calculating the percolation threshold (PT) using complex networks for both univariate and multivariate data. We employ cooperative game theory to model P2P energy trading, ensuring that all prosumers participate rationally. We consider the formation of microgrids in a standard IEEE-123 node test feeder system integrated with renewable energy sources. The improvement in energy resilience with trading of energy in the P2P network is analyzed for the most resilient microgrid in the distribution system. The results demonstrate a 67.91% total cost benefit and a 25.07% improvement in the resilience of the microgrid due to the integration of DERs and P2P energy trading for a period of one year.

Novel authentication of African geographical coffee types (bean, roasted, powdered) by handheld NIR spectroscopic method

African coffee is among the best traded coffee types worldwide, and rapid identification of its geographical origin is very important when trading the commodity. The study was important because it used NIR techniques to geographically differentiate between various types of coffee and provide a supply chain traceability method to avoid fraud. In this study, geographic differentiation of African coffee types (bean, roasted, and powder) was achieved using handheld near-infrared spectroscopy and multivariant data processing. Five African countries were used as the origins for the collection of Robusta coffee. The samples were individually scanned at a wavelength of 740–1070 nm, and their spectra profiles were preprocessed with mean centering (MC), multiplicative scatter correction (MSC), and standard normal variate (SNV). Support vector machines (SVM), linear discriminant analysis (LDA), neural networks (NN), random forests (RF), and partial least square discriminate analysis (PLS-DA) were then used to develop a prediction model for African coffee types. The performance of the model was assessed using accuracy and F1-score. Proximate chemical composition was also conducted on the raw and roasted coffee types. The best classification algorithms were developed for the following coffee types: raw bean coffee, SD-PLSDA, and MC + SD-PLSDA. These models had an accuracy of 0.87 and an F1-score of 0.88. SNV + SD-SVM and MSC + SD-NN both had accuracy and F1 scores of 0.97 for roasted coffee beans and 0.96 for roasted coffee powder, respectively. The results revealed that efficient quality assurance may be achieved by using handheld NIR spectroscopy combined with chemometrics to differentiate between different African coffee types according to their geographical origins.

Study on O3-NOx-VOCscombined air pollution and ozone health effects in the Hexi Corridor.

In order to study the ecological and atmospheric recovery of the Hexi Corridor region, this paper analyzes the migration changes of land use characteristics and utilizes multivariate data and BenMAP-CE software to study the pollution characteristics of ozone and its precursors and the impact on human health in the Hexi Corridor region. The results showed that the increase of cultivated land area in the Hexi Corridor mainly originated from grassland. The MDA8-O3 concentrations met the primary and secondary standards of the Ambient Air Quality Standards on 43% and 99% of the days, respectively. NO2 showed a negative weekend effect with O3, and HCHO was opposite to it. Temperature, barometric pressure, and vegetation were highly correlated with O3-NO2-HCHO. Ozone pollution in the study area caused about 60% of all-cause premature deaths due to cardiovascular diseases. The study suggests that controlling exogenous transport in Wuwei City during the high ozone period (except August) is mainly dominated by the west and northwest, and that synergistic management of VOCs and NOx emissions can reduce O3 pollution and, consequently, reduce the risk to human health.